A Review in Fault Diagnosis and Health Assessment for Railway

applied sciences Review

A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives Fernando Garramiola * , Javier Poza, Patxi Madina, Jon del Olmo and Gaizka Almandoz Faculty of Engineering, Mondragon Unibertsitatea, 20500 Arrasate-Mondragón, Spain; [email protected] (J.P.); [email protected] (P.M.); [email protected] (J.d.O.); [email protected] (G.A.) * Correspondence: [email protected]; Tel.: +34-943-794-700 Received: 16 November 2018; Accepted: 29 November 2018; Published: 3 December 2018







Abstract: During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade. Keywords: Fault Diagnosis; Health-Assessment; Condition-based maintenance; railway traction drives

1. Introduction In the last decade, due to the increasing importance of availability in Railway systems, early fault diagnosis and Condition-based maintenance (CBM) have become key points for Railway industry. CBM is based on system health monitoring for decision-making [1]. An easy way to formalize a CBM framework is using the standard ISO 13374 [2], in which the CBM tasks are divided into six different levels. The ideal maintenance strategy should be able to act once a fault is detected and before the failure occurs. Thus, cost-effectiveness maintenance decision is made without worsening the availability of the system. Maintenance decision-making can be done based on diagnosis or prognosis. Diagnosis aims to detect and isolate faults, whereas prognosis estimates the future state of a component. Furthermore, other solutions have arisen, as Prognosis and Health Management (PHM), in order to predict and protect the systems based on estimation of time to failure [3]. A review of prognosis methods is presented in [4], classifying the methods depending on the computational resources and historical data quantity needed. CBM requires fault diagnosis for Health Assessment (HA). Fault Detection and Isolation (FDI) approaches detect and located the fault, whereas Fault Detection and Diagnosis (FDD) approaches provide the fault severity too. In case of complex systems and multivariate analysis, FDI approaches can be mainly classified as Model-based and Data-driven. A comparison of different Data-driven approaches, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), is given in [5]. Model–based approaches require a physical knowledge of system, whereas data-driven approaches need a large quantity of data. Model-based approaches require the development of an analytical model, that simulates the system behaviour, in order to calculate the difference between the real system and the analytical model output, this difference is called residual. This is the signal used Appl. Sci. 2018, 8, 2475; doi:10.3390/app8122475

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evaluation is done using norms or statistical methods [8]. Some statistical methods are Hypoth tests or Likelihood ratio (LR). Likelihood ratio is given by Equation (1), being 𝑝 the probab density for a normal distribution Ν(𝜇, 𝜎 ), where 𝜇 is the mean value and 𝜎 is the varia Appl. Sci. 2018, is 8, 2475 2 of 19 𝑝(𝑟) gives Probability density given by Equation (2) to evaluate the residual 𝑟 . Thus, probability of a given sample of the residual to be within the normal distribution Ν(𝜇, 𝜎 ). On for fault detection, as it is shown in Figure 1. Analytical models are mainly developed by structural other hand, LR shows which of the compared probability densities is more probable. For examp approaches [6], based on consistency relation and observer-based approaches [7]. Residual evaluation a faulty case isgiven by 𝑝norms is compared to a fault-free case given byare 𝑝 Hypothesis , as it is shown done using or statistical methods [8]. Some statistical methods tests or in Equation Likelihood Likelihood ratio is given by Equation (1), being p the probability density for a ) >(LR). in case that the 𝐿𝑅(𝑟ratio 0 faulty
case is accepted, as 𝑝 is more probable than 𝑝 , otherwise it normal distribution N µ, σ2 , where µ is the mean value and σ2 is the variance. Probability density is fault-free case. given by Equation (2) to evaluate the residual r. Thus, p(r ) gives the probability of a given sample of
the residual to be within the normal distribution N µ, σ2 . On the other hand, LR shows which of the 𝑝 (𝑟) compared probability densities is more probable. For example, 𝐿𝑅(𝑟) = ln if a faulty , case given by p1 is compared to a fault-free case given by p0 , as it is shown in Equation (1), ) that the LR(r) > 0 faulty case is 𝑝 in(𝑟case accepted, as p1 is more probable than p0 , otherwise it is a fault-free case.

being 𝑝 and 𝑝 the probability density of the residual for two normal distributions, for exam p (r ) , (1) LR(r ) = ln 1 faulty and a fault-free cases. p0 (r ) ( )distributions, for example, faulty being p1 and p0 the probability density of the residual for 1 two normal 𝑝(𝑟 ) = 𝑒2 , and a fault-free cases. (r − µ ) 1 𝜎 √−2𝜋 p(r ) = √ e 2σ2 , (2) σ 2π

being 𝜇 and 𝜎 the mean value and variance of the normal distribution to which the residual being µ and σ2 the mean value and variance of the normal distribution to which the residual will be compared.be compared. Outputs

System

Threshold

Analytical model

Residual generation

+

Fault decision

Residual

-

Residual evaluation

Figure 1. Model-based fault diagnosis. Figure 1. Model-based fault diagnosis.

On the other hand, in data-driven approaches, it is assumed that a fault alters some of the measured signals, so for fault detection are obtained those signals, that using mainly signal On the other hand, infeatures data-driven approaches, itfrom is assumed a fault alters some o processing or statistics. In case of data-driven approaches, a fault-free training is required in order to get measured signals, for faultcan detection obtained those signals, using mainly si a pattern.so In features case that fault detection be done by are analysis of just one from signal, signal-based approaches be used. In contrast to of other Data-driven approaches, Signal-based approaches training are based onis signal processing orcanstatistics. In case data-driven approaches, a fault-free required in ord processing and they do not need a previous training. Fusion of different FDI approaches are presented get a pattern. In case that fault detection can be done by analysis of just one signal, signal-b in [9]. Fusion can benefit from advantages of different FDI approaches. A review of different FDI approaches is presented in [10]. Based on [11],Data-driven a classification ofapproaches, main FDI approaches is shown in approache approaches can be used. In contrast to other Signal-based Figure 2, although there are other techniques, such as artificial intelligence based [12], bond graph [13] based on signal processing and they do not need a previous training. Fusion of different or hybrid approaches [4], which are not presented. Diagnostic observers for model-based approaches, approaches are [9]. Fusion benefit from advantages of different FDI approache PCApresented for data-drivenin and Motor currentcan signature analysis (MCSA) for signal-based are some of the most usualFDI FDI approaches in electric drives. review of different approaches is presented in [10]. Based on [11], a classification of main

approaches is shown in Figure 2, although there are other techniques, such as artificial intellig based [12], bond graph [13] or hybrid approaches [4], which are not presented. Diagnostic obser for model-based approaches, PCA for data-driven and Motor current signature analysis (MCSA signal-based are some of the most usual FDI approaches in electric drives.

in case of hard faults, mainly in sensors that take part in the control strategy, the system leaves the safety zone and protections act. Thus, it could be hard to avoid the failure and there could be a loss of availability, unless a fault tolerant control (FTC) is implemented. A review of FTC is presented in [22]. Several publications related to the application of FDI to electric traction drives are presented in the literature [23]. The research works are focused on sensors [24–26], power electronics devices Appl. Sci. 2018, 8, 2475 3 of 19 [15,27] and electric motors [28–30]. Observer-based

ConsistencyARR Model- based Parity space

Kalman filter Fuzzy

Knowledgebased

Neural networks

Time series classifiers: Shapelet..

FDI classification

PCA

Statistics-based Data-driven

ICA

PLS

Wavelet

Signal-based

MCSA

MED

Figure 2. 2. Fault and isolation isolation (FDI) (FDI) approaches approaches classification. classification. Figure Fault detection detection and

Furthermore, there are review articles related to fault diagnosis in renewable energies applications [14,15] and aerospace applications [16], as well as a review in Railway applications but focused on the wheel-rail and suspension health monitoring [17]. In Railway systems, historically corrective maintenance and planned preventive maintenance have been applied. Recently, due to the improvement of technology and computational capabilities, frameworks for HA and CBM have been applied to Railway systems [17–20]. Among the different parts in railway systems, traction drives play an important role, as they are responsible for train motion. The main faults in traction drives occur in sensors, power electronics devices and electric motors. In [21] the sensor technology in railway traction drives is presented. In case of faults, incipient or abrupt, the system performance is degraded and early fault diagnosis should be implemented in order to prevent a failure and improve the availability. On the other hand,

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in case of hard faults, mainly in sensors that take part in the control strategy, the system leaves the safety zone and protections act. Thus, it could be hard to avoid the failure and there could be a loss of availability, unless a fault tolerant control (FTC) is implemented. A review of FTC is presented in [22]. Several publications related to the application of FDI to electric traction drives are presented in the literature [23]. The research works are focused on sensors [24–26], power electronics devices [15,27] and electric motors [28–30]. Furthermore, although during the last decade several publications related to fault diagnosis and health assessment in Railway traction drives have been published [25,31–33], there is not a review, which summarizes all the research work done in Railway traction drives. Previous review works are focused on sensors more than in fault diagnosis [21] or on fault diagnosis in railway traction power supply [18]. The objective of this article is to present the latest research works in fault diagnosis and health assessment in Railway traction drives, as an essential asset for its integration in a CBM system. Scopus and Web of Science databases have been used, following PRISMA guidelines, in order to gather the relevant research works. First, documents are selected in case of finding one of the following keywords: “fault,” “monitoring” or “maintenance” on the author keywords, abstract or title. In addition to these keywords, search was limited to documents where “railway” and “traction” keywords appear on the title. Then, the search was refined to articles and conference proceedings since 2005, in the field of engineering, computing and decision sciences. 80 documents in Scopus and 62 in Web of Science were listed and both lists were compared to avoid duplicities. Finally, the documents were analysed in order to select the ones related to health assessment and fault diagnosis in Railway Traction drives. The article has the following structure: Section 2 summarizes the research works in Health assessment and CBM in Railway traction drives. Section 3 presents different FDI approaches applied to Railway systems. Section 4 presents the state of the art of fault diagnosis in Railway Traction Drives. Finally the discussions and conclusions are given. 2. Condition-Based Maintenance in Railway Systems In opposition to run-to failure or corrective maintenance, in which the goal is to replace the faulty component, preventive maintenance strategies, aims to prevent the failure, by means of condition monitoring and early fault detection. A classification of maintenance strategies is presented in Figure 3. Several reviews in CBM are available in the literature [1,34–37] A comparison between CBM and Time-based maintenance (TBM) is presented in [38]. The cost-effectiveness of CBM is dependent on some factors, such as condition monitoring quality and required planning time. In [39] a framework for CBM implementation is presented. The framework is based on five different blocks in order to improve the interaction among the different disciplines during CBM design and implementation periods. The framework integrates tools as Failure mode and Effects Analysis (FMEA) and standards as ISO 13374. Standard ISO13374, developed for condition monitoring and diagnostic of machines, presents the following functional modules for CBM implementation: Data Acquisition (DA), Data Manipulation (DM), State Detection (SD), Health Assessment (HA), Prognostics Assessment (PA) and Advisory Generation (AG). An example of each module functionality is shown in Figure 4. In the example presented, in the HA module, not only the diagnosed faults are given, the effects on system performance are presented too, in order to help in the maintenance decision-making [40]. In [41], the application of the standard ISO 13374 to a railway system is proposed. An on-board diagnosis is proposed, being the DA, DM, SD and HA on-board implemented, whereas in a remote diagnosis only DA, DM and SD are on-board implemented and data is sent to maintenance centre for diagnosis, as it is shown in Figure 5. Furthermore, in Figure 6, a distributed supervision is presented, in opposition to centralized supervision where a global diagnosis is done. Thus, in the distributed supervision there are local diagnosis units apart from a global one.

2. Condition-Based Maintenance in Railway Systems In opposition to run-to failure or corrective maintenance, in which the goal is to replace the faulty component, preventive maintenance strategies, aims to prevent the failure, by means of condition monitoring early fault detection. A classification of maintenance strategies is presented in Figure Appl. Sci. 2018,and 8, 2475 5 of 19 3.

Maintenance

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In [39] a framework for CBM implementation is presented. The framework is based on five different blocks in order to improve the interaction among the different disciplines during CBM NO The YES integrates tools as Failure mode and Effects Before design and implementation periods. framework failure ? Analysis (FMEA) and standards as ISO 13374. Standard ISO13374, developed for condition monitoring and diagnostic of machines, presents the following functional modules for CBM implementation: Data Acquisition (DA), Data Manipulation (DM), State Detection (SD), Health Corrective Preventive Appl. Sci. 2018, 8, x FOR PEER REVIEW 5 of 19 Assessment (HA), maintenance Prognostics Assessment (PA) and Advisory Generation (AG). An example of each maintenance moduleInfunctionality is shown Figure 4. In the example presented, in theframework HA module, only [39] a framework forinCBM implementation is presented. The is not based onthe five diagnosed faults are given, the effects on system performance are presented too, in order to help in different blocks in order to improve the interaction among the different disciplines during CBM thedesign maintenance decision-making [40]. The framework integrates tools as Failure mode and Effects and implementation periods. Condition

NO Standard ISO13374, YES Analysis (FMEA) and standards as ISO 13374. developed for condition monitoring HEALTH INDEX: applied ? 5/10 monitoring and diagnostic of machines, presents the following functional modules for CBM REMAINING USEFUL r REPLACE THE SENSOR A LIFE BEFORE DIAGNOSIS: IN THE NEXT PLANNEDHealth implementation: Data Acquisition (DA), Data Manipulation (DM), State Detection (SD), ALARM SYSTEM FAILURE: MOTOR CURRENT SENSORS MAINTENANCE 420 HOURS SENSOR WARNING D ConditionAssessment (HA), Prognostics Assessment (PA) and Advisory Generation (AG). An example of each OFFSET FAULT +40 A Time-based TRACTION TORQUE t based DECREASE 10% maintenance module functionality is shown in Figure 4. In the example presented, in maintenance the HA module, not only the diagnosed faults are given, the too, in order to help in DATAeffects on system performance HEALTH are presented ADVISORY PROGNOSTICS DATA ACQUISITION STATE DETECTION MANIPULATION ASSESSMENT GENERATION ASSESSMENT the maintenance (DA) decision-making (SD) of maintenance strategies. Figure 3.[40]. Classification Figure (DM) 3. Classification of maintenance (HA) strategies. (PA)

(AG)

4. Functional of ISO 133374 basedAon [2]. HEALTH INDEX: Several reviews in Figure CBM are available modules in the literature [1,34–37] comparison between CBM and 5/10 REMAINING USEFUL r REPLACE THE SENSOR A Time-based maintenance (TBM) is presented in [38]. The cost-effectiveness of CBM is dependent on LIFE BEFORE DIAGNOSIS: IN THE NEXT PLANNED ALARM SYSTEM FAILURE: MOTOR CURRENT SENSORS MAINTENANCE In [41], the application of the standard ISO 13374 to a railway system is proposed. An on-board 420 HOURS SENSOR planning time. some factors, such as and required WARNING D condition monitoring quality OFFSET FAULT +40 A diagnosis is proposed, being the DA, DM, SD and HA on-board implemented, whereas in a remote TRACTION TORQUE t DECREASE 10% diagnosis only DA, DM and SD are on-board implemented and data is sent to maintenance centre for DATA ADVISORY DATA ACQUISITION STATE DETECTION diagnosis, as it is shown in Figure 5. Furthermore, in Figure ASSESSMENT 6,HEALTH a distributedPROGNOSTICS supervision is presented, MANIPULATION GENERATION ASSESSMENT (DA) (SD) (DM) (HA) is done. Thus, in opposition to centralized supervision where a global diagnosis in the distributed (AG) (PA) supervision there are local diagnosis units apart from a global one. Figure4.4.Functional Functionalmodules modulesof ofISO ISO133374 133374based basedon on[2]. [2]. Figure

Remote On-board In [41], the application of the standard ISO 13374 to a railway system is proposed. An on-board diagnosis diagnosis diagnosis is proposed, being the DA, DM, SD and HA on-board implemented, whereas in a remote diagnosis only DA, DM and SD are on-board implemented and data is sent to maintenance centre for diagnosis, as it is shown in Figure 5. Furthermore, in Figure 6, a distributed supervision is presented, in opposition to centralized supervision where a global diagnosis is done. Thus, in the distributed Diagnosis supervision there are local diagnosis units apart from a global one. Remote On-board

Data from

Remote subsystems diagnosis

Health

On-board assessment diagnosis Diagnosis

Diagnosis

Remote On-board

Data from subsystems

Health assessment

Figure 5. Remote and on-board diagnosis. Figure 5. Remote and on-board diagnosis. Diagnosis

On-board equipment is normally limited in data storage capacity. On-board diagnosis allow On-board is no normally inmany data storage capacity. diagnosis allow providing HAequipment and there is need tolimited store so variables, just theOn-board fault indicators. Moreover, providing HA and there is no need to store so many variables, just the fault indicators. Moreover, communication from train to remote supervision is bounded by transceivers availability and communication from train to remote supervision is bounded by transceivers availability and communication latency. In the near future, this latency could be lower than 100 ms [42]. Thus, onboard diagnosis is more suitable when real-time FDI methods should be implemented, whereas remote diagnosis can be used for delayed HA, as the data are offline analysed. Data can be sent to Figure 5. Remote and on-board diagnosis.

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communication latency. In the near future, this latency could be lower than 100 ms [42]. Thus, on-board diagnosis is more suitable when real-time FDI methods should be implemented, whereas remote diagnosis Appl. Sci. 2018, 8, x FOR PEER REVIEW 6 of 19 can be used for delayed HA, as the data are offline analysed. Data can be sent to remote diagnosis, with a time interval over with the aforementioned or at the end of the day, depending on end the celerity the remote diagnosis, a time intervallatency, over the aforementioned latency, or at the of the of day, maintenance decision-making to offer. depending on the celerity of the maintenance decision-making to offer. On-board centralised diagnosis

On-board decentralised diagnosis

Remote On-board

Diagnosis

Diagnosis

System health assesment

Subsystems health assesment

Diagnosis

Diagnosis

Diagnosis

Figure 6. Decentralised and centralised diagnosis. Figure 6. Decentralised and centralised diagnosis.

In [43] a proposal of CBM for on-board equipment application in a railway application is presented. In [43] a proposal of CBM on-board equipment application a railway application is A rating criterion for health statusfor is presented, as well as four different in levels for health assessment. presented. A rating criterion for health status is presented, as well as four different levels for health A transition from preventive to predictive maintenance for railway systems is presented in [44], assessment. presenting a solution based on remote diagnosis and prognosis. This transition is one of the key A transition frommaintenance preventive toproposal predictive forpoints railway is presentedare in [44], points for the smart in maintenance [45]. Other key insystems smart maintenance asset presenting a solution based on remote diagnosis and prognosis. This transition is one of the key management, support system by artificial intelligence and integrated database. The asset management points for the smart maintenance proposal in [45]. Other key points in smart maintenance are asset aims to plan an optimal maintenance in systems, which deterioration is slow but maintenance work management, supportsystem system by artificial intelligence and integrated database. The asset takes long. Support by artificial intelligence expects to help maintenance engineers in the management aims to plan an optimal maintenance in systems, which deterioration is but a decision-making. Nowadays, engineers need a long-term expertise in order to be ableslow to take maintenance takesFinally, long. Support systemdatabase by artificial intelligence expects to all help maintenancework decision. an integrated is needed in order to have themaintenance data coming engineers in the decision-making. Nowadays, engineers need a long-term expertise in order to be from maintenance and diagnosis tools available, as well as the historic data. able toThere take aare maintenance decision. Finally, an integrated database is needed in order to have all the different commercial supervision systems in order to improve the maintenance service, data from maintenance diagnosis available, ascommunication well as the historic data. supervision. as itcoming is shown in Table 1, mainlyand based on datatools monitoring and to remote There are different commercial supervision systems in order to improve the maintenance Moreover, Shift2rail [46] is a public-private initiative, where European Union and main rail equipment service, as it is shown in Table 1, mainly based on data monitoring and communication manufactures promote innovations in railway, being the smart maintenance a key point. to remote supervision. Moreover, Shift2rail [46] is a public-private initiative, where European Union and main rail equipment manufactures promote in railway,systems. being the smart maintenance a key Table 1. innovations Commercial supervision point. Company

Supervision System

CAF sDiag, COSMOS Table 1. Commercial supervision systems. Bombardier ORBIFLO, AIMS Company Supervision System Siemens Railigent Alstom TrainTracer, HealthHub CAF sDiag, COSMOS

Bombardier ORBIFLO, AIMS Siemens Railigent On the other hand, there is a need to improve the synergy between maintenance and safety Alstom TrainTracer, HealthHub engineering teams. In Railway applications, safety engineers should develop a FMEA in order to elaborate a safety analysis. A safety analysis is needed in order to identify the most critical components Onsystem. the other hand, there the is ainformation need to improve theinsynergy between maintenance and safety in the Unfortunately, available the FMEA is mainly qualitative, providing engineering teams. In Railway applications, safety engineers should develop a FMEA in order to elaborate a safety analysis. A safety analysis is needed in order to identify the most critical components in the system. Unfortunately, the information available in the FMEA is mainly qualitative, providing just risk priority numbers (RPN) to quantify the effects on the system, being limited for maintenance use. Thus, some research works use fault injection techniques and Hardware-

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just risk (HIL) priorityvalidation numbers (RPN) to quantify effects on being limited for maintenance use. for in-the-loop to quantify thethe effects. In the [40]system, the fault injection technique is used Thus, some research works use fault injection techniques and Hardware-in-the-loop (HIL) validation system analysis under sensor faults. The analysis results have been used to enhance the initial FMEA, to quantify the effects. In [40] the fault injection technique used for system sensor and already available from safety engineers, as well as itsis integration in analysis design, under validation faults. The analysis results have been used to enhance the initial FMEA, already available from maintenance tasks, as it is shown in Figure 7. FMEA is enhanced with field informationsafety obtained engineers, as well as its integration in design, validation and maintenance tasks, as it is shown in from maintenance engineers and with system analysis under faults in the HIL platform. A fault Figure 7. FMEA is enhanced with field information obtained from maintenance engineers and with injection technique has been used in several other railway applications [47–49], validating the system analysis under faults in the HIL platform. A fault injection technique has been used in several analysis in simulation or in HIL platforms, composed of a Real-time simulator and a commercial other railway applications [47–49], validating the analysis in simulation or in HIL platforms, composed TCU.of a Real-time simulator and a commercial TCU. Fault diagnosis approaches Safety engineers Functional requirements

Safety requirements + availability + degraded mode

System mode (SIMULINK)

FMEA

System extended model (fault injection)

Design engineers

Fault and effects analysis

Design Validation

Validation plan

HIL platform

Operation and maintenance

Operation and maintenance Maintenance engineers

Field information

Figure 7. Integration of failure mode and effects analysis (FMEA) and fault injection for maintenance [48].

Figure 7. Integration of failure mode and effects analysis (FMEA) and fault injection for maintenance [48]. The influence on maintenance of Internet of Things (IoT) and new communications technologies

is reviewed in [50]. IoT can help in the implementation of a preventive maintenance and a dynamic The influence oninmaintenance of Internet Things (IoT) and new communications decision-making railway systems due to theofinformation obtained from collected data. technologies is reviewed in [50]. IoT can help in the implementation of a preventive maintenance andauthors a dynamic In [51] a review in PHM is presented. A case study on a bogie is presented too. The decision-making in railway systems due to the information obtained collected propose four steps for PHM implementation: component critical analysis,from sensor selection data. for condition monitoring, data processing prognosticsAapproach. Theon component critical analysistoo. canThe be done In [51] a review in PHM and is presented. case study a bogie is presented authors by means of the aforementioned FMEA. Sensor selection is essential to represent system degradation. propose four steps for PHM implementation: component critical analysis, sensor selection for Furthermore, the costdata of sensors should be considered for the cost-effectiveness of conditioncritical monitoring condition monitoring, processing and prognostics approach. The component analysis system. The prognostics approach proposed is hybrid, based on the combination of model-based can be done by means of the aforementioned FMEA. Sensor selection is essential to representand system data-driven approaches. A comparison among different approaches for prognosis is presented.

degradation. Furthermore, the cost of sensors should be considered for the cost-effectiveness of condition The prognostics approach proposed is hybrid, based on the 3. Faultmonitoring Diagnosis insystem. Railway Systems combination of model-based and data-driven approaches. A comparison among different approaches A variety of fault diagnosis approaches has been used for electric and mechanical parts in for prognosis is presented.

Railway Systems. Apart from Railway Traction drives, which will be reviewed in the following section, previously mentioned FDI approaches have been applied to other parts of Railway Systems. 3. Fault Diagnosis in Railway Systems In [52,53], a vision-based condition monitoring is applied in order to detect faults in railway lines. An online-monitoring is proposed in [54] forhas railbeen fasteners, acceleration signals with A variety of fault diagnosis approaches used analysing for electric and mechanical parts in wavelet transform. Wavelet transform [55] allows analysing a signal by mean of a variable time Railway Systems. Apart from Railway Traction drives, which will be reviewed in the following

section, previously mentioned FDI approaches have been applied to other parts of Railway Systems. In [52,53], a vision-based condition monitoring is applied in order to detect faults in railway lines. An online-monitoring is proposed in [54] for rail fasteners, analysing acceleration signals with wavelet transform. Wavelet transform [55] allows analysing a signal by mean of a variable time window and different scaling, inversely proportional to frequency, as it is shown in (3) for continuous wavelet

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window and different scaling, inversely proportional to frequency, as it is shown in (3) for continuous wavelet transform. Appl. Sci. 2018, 8, x FOR PEER REVIEW 8 of 19   Z ∞ 1 t−τ wt(s, τ ) = √ x (t)Ψ∗ dt, (3) s− 𝜏𝜏 s 1−∞ ∞ 𝑡𝑡
𝑤𝑤𝑤𝑤(𝑠𝑠, 𝜏𝜏) = � 𝑥𝑥(𝑡𝑡)Ψ ∗ � � 𝑑𝑑𝑑𝑑, (3) 𝑠𝑠 translation τ and scaling s and x (t) being Ψ∗ t−s τ the complex conjugate of wavelet √𝑠𝑠basis −∞ for the time the signal ∗ 𝑡𝑡−𝜏𝜏 beingtoΨtransform. � � the complex conjugate of wavelet basis for the time translation 𝜏𝜏 and scaling 𝑠𝑠 and 𝑠𝑠 One of the applications of wavelet transform is the suppression of the noise in the original 𝑥𝑥(𝑡𝑡) the signal to transform. signal. Based on wavelet transform, Wavelet Packet Transform (WPT) resolution One of the applications of wavelet transform is the suppression of the improves noise in thethe original signal. for high-frequency region. The signal is decomposed into approximation and detail signals, then detail Based on wavelet transform, Wavelet Packet Transform (WPT) improves the resolution for the highsignalsfrequency are limited to a The threshold and finally theinto signal is reconstructed based on approximation region. signal is decomposed approximation and detail signals, then the detailand signals are a threshold andapplications, finally the signal is reconstructed on approximation and detail signals. Inlimited case oftofault detection a suitable wavelet based basis should be chose in order detailbetween signals. In case of fault applications, a suitable wavelet should be chose in order in to classify fault-free anddetection faulty cases. Wavelet basis could be basis selected to identify changes to classify fault-free and WPT faultyenergy cases. Wavelet basistocould be selected to identify changes inwith entropy, energy between and so forth. In [56] is applied vibrations signals in combination entropy, energy and so forth. In [56] WPT energy is applied to vibrations signals in combination with neural networks, in order to detect faults in the axle of bogies. neural networks, in order to detect faults in the axle of bogies. Vibration signals are analysed by mean of another signal-based approach, an improved Minimum Vibration signals are analysed by mean of another signal-based approach, an improved Entropy Deconvolution (MED) [57], in order to detect faults in bearing. The method presented Minimum Entropy Deconvolution (MED) [57], in order to detect faults in bearing. The method improves the fault diagnosis in case of low ratio compared to classical MED.MED. presented improves the fault diagnosis in signal-to-noise case of low signal-to-noise ratio compared to classical On theOn other hand, among thethe statistics-based in [58] [58]Principal Principal component analysis the other hand, among statistics-basedapproaches, approaches, in component analysis (PCA)(PCA) is used lineline fault detection. statistical method, where is for usedrailway for railway fault detection.PCA PCA[59] [59]isis multivariate multivariate statistical method, where the the data, normally normalized and on based on quantity a large quantity of observations and variables, are data, normally normalized and based a large of observations and variables, are representing representing in a new space, with a lower dimension than the original one and given by principal in a new space, with a lower dimension than the original one and given by principal vectors. PCA vectors.the PCA decomposes the data into two matrices,the onescore including the score vectors, which represent decomposes data into two matrices, one including vectors, which represent the data in the the data in the new space and another one for the loading or principal vectors. A variety of new space and another one for the loading or principal vectors. A variety of decomposition algorithms decomposition algorithms are used to obtain the principal components, in order to improve the are used to obtain the principal components, in order to improve the detectability of variations in the detectability of variations in the data, setting the component with major changes in horizontal axis data, setting component with changes major changes horizontal axis in and second oneon with major and the the second one with major in verticalinaxis, as it is shown thethe example, based motor changes in vertical axis, as it is8.shown in the example, based fault-free on motordata phase in Figure phase currents, in Figure PCA approach needs historical forcurrents, fault detection. First,8.anPCA approach needs historical fault-free data for fault detection. First, an offline step based on fault-free offline step based on fault-free data, in order to obtain the fault detection threshold, then an online data, in order to obtain the fault detection threshold, then an online step to compare the actual step to compare the actual data to the threshold for fault detection. Among others, Hotelling’s T2 anddata prediction (SPE) statistics areHotelling’s used for threshold and fault detection. to the squared threshold for faulterror detection. Among tests others, T2 andsetting squared prediction error A (SPE) variation of PCA applied in [60] in order detect faults in A transformers, adequate statistics tests are used isfor threshold setting andtofault detection. variation ofbeing PCAmore is applied in [60] when data have complex non-linear relationship. In adequate [20] the data for railway point machinenon-linear (RPM) in order to detect faults in transformers, being more when data have complex replacement is obtained based on electric current monitoring and Shapelet method. Training data is relationship. In [20] the data for railway point machine (RPM) replacement is obtained based on required for its application too. electric current monitoring and Shapelet method. Training data is required for its application too. 15 fault-free faulty

10

PC2

5

0

-5

-10

-15 -15

-10

-5

0

5

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PC1

FigureFigure 8. Scores of principal component analysis foran anunbalance unbalance induction 8. Scores of principal component analysis(PCA) (PCA)application application for in in an an induction motor motor phasesphases for anfor operation point. an operation point.

AC supply, including a transformer andsensor a rectifier is shown in Figure 10. In cases, based approach is presented in [62] for faultonboard, detectionasinitthe suspension system. A both hypothesis the parts for of aresidual Railwayevaluation. Traction drive are the electric machine, the power electronics, the traction testmain is utilized control unit (TCU) and the sensors. Although there are railway traction drives based on other 4. Fault Diagnosis in DC Railway Traction Drives nowadays, the most usual configuration is based on technologies, such as motors and thyristors, induction motors Appl. Sci. 2018, 8, 2475(IM) and Insulated Gate Bipolar Transistors (IGBT). Thus, this configuration 9will of 19 The railway traction drive is a key point for the Railway system availability. Railway traction be proposed as example in this article. drives are fed with DC supply provided by an external substation, as it is shown in Figure 9, or with In power electronics, power converters, power switches and passive components, as the DC-link AC supply, a transformer and a rectifier onboard, as it is showngiven in Figure 10. In redundancy both cases, Amongincluding the model-based approaches, a consistency-based approach, analytical capacitor, are included. The power converters in a DC catenary supplied Railway traction drive are the main parts of aisRailway Traction drive are the electric machine, the power electronics, the traction relations (ARR), proposed for traction power supply fault detection [61]. An Observer-based an inverter to supply the motor and a braking chopper, whereas a rectifier is needed to be added, in control unit (TCU) and the sensors. Although there are railway traction drives based on other approach is presented in [62] for sensor fault detection in the suspension system. A hypothesis test is case of AC catenary supply. The inverter transfers power in DC-link to the electric machine, normally technologies, such as DC motors and thyristors, nowadays, the most usual configuration is based on utilized for residual evaluation. an induction motor, during traction or returns to DC-link from motor during braking operation. induction motors (IM) and Insulated Gate Bipolar Transistors (IGBT). Thus, this configuration will Furthermore, by means of a chopper, 4. proposed Fault Diagnosis in Railway Tractionenergy Drivesis dissipated in the braking resistor in case that the be as example in this article. energy cannot be returned to catenary. In case of AC catenary, a controlled rectifier for AC/DC In power electronics, power converters, power switches and passive components, as the DC-link The railway conversion is used.traction drive is a key point for the Railway system availability. Railway traction capacitor, are included. power converters in aexternal DC catenary supplied Railway traction drive9,are drives are fed with DCThe supply provided by an substation, as it is shown in Figure or an inverter to supply the motor and a braking chopper, whereas a rectifier is needed to be added, in with AC supply, including a transformer and a rectifier onboard, as it is shown in Figure 10. In both Sensors case ofvcat ACSensors catenary supply. The inverter transfers power DC-link to the electric machine, normally cases, the main parts of a Railway drive are thein electric machine, the power electronics, the Power electronics Traction Power electronics Power electronics an induction motor, during traction or returns to DC-link from motor during braking operation. traction control unit (TCU) and the sensors. Although there are railway traction drives based on other Furthermore, means ofInput a chopper, energy Braking is nowadays, dissipated the in the braking resistor in case that the technologies, by such as DC motors most usual on IM M Inverterconfiguration is based contactors and and thyristors, Chopper energy cannot be (IM) returned to catenary. case ofTransistors AC catenary, a controlled for AC/DC filters induction motors and Insulated GateInBipolar (IGBT). Thus, this rectifier configuration will be conversion is example used. in this article. proposed as Encoder

vcat

Traction Control Unit

Sensors Power electronics

Power electronics

Power electronics

Sensors

Figure 9. Structure of a Railway traction drive connected to DC catenary. Input

Braking

IM M

Inverter contactors and The electric machine is connected to a gearbox, Chopperwhich is connected to the bogie axle to move the filters train [19]. One inverter can supply one or more electric motors in parallel. Encoder The TCU is the responsible for the execution of the switching modulation for power converters, Traction Control Unit management. Sensors measurements are as well as main and pre-charge contactors and protections monitored by the TCU for control strategy and fault diagnosis algorithms.

Figure Figure9.9.Structure StructureofofaaRailway Railwaytraction tractiondrive driveconnected connectedtotoDC DCcatenary. catenary.

Sensors The electric machine is connected to a gearbox, which is connected to the bogie axle to move the train [19]. One inverter can supply one or more electric motors in parallel. Sensors vcat TCU is the responsible Power for the execution Power electronics Power electronicsfor power converters, The of the switching modulation electronics as well as main and pre-charge contactors and protections management. Sensors measurements are Braking IM M Inverter monitored by the TCU forRectifier control strategy andChopper fault diagnosis algorithms. Encoder

Sensors Traction Control Unit

vcat

Power

Power electronics

Power electronics

Sensors

Figure Structure Figure10. 10. Structureofofa aRailway Railwaytraction tractiondrive driveconnected connectedtotoAC ACcatenary. catenary. electronics Braking

IM M Inverter Rectifier From 2005,electronics, several research relatedpower to fault diagnosis Railway traction drives have been In power powerworks converters, switches andinpassive components, as the DC-link Chopper published. In this review,The fault diagnosis research have been classified in three groups, asare a capacitor, are included. power converters in aworks DC catenary supplied Railway traction drive Encoder an inverter to supply the motor and a braking chopper, whereas a rectifier is needed to be added, in Traction Control Unit case of AC catenary supply. The inverter transfers power in DC-link to the electric machine, normally an induction motor, during traction or returns to DC-link from motor during braking operation. Figure 10. Structure of a Railway traction drive connected to AC catenary. Furthermore, by means of a chopper, energy is dissipated in the braking resistor in case that the energy cannot be returned to catenary. In case of AC catenary, a controlled rectifier for AC/DC conversion From 2005, several research works related to fault diagnosis in Railway traction drives have been is used. published. In this review, fault diagnosis research works have been classified in three groups, as a The electric machine is connected to a gearbox, which is connected to the bogie axle to move the train [19]. One inverter can supply one or more electric motors in parallel. The TCU is the responsible for the execution of the switching modulation for power converters, as well as main and pre-charge contactors and protections management. Sensors measurements are monitored by the TCU for control strategy and fault diagnosis algorithms. From 2005, several research works related to fault diagnosis in Railway traction drives have been published. In this review, fault diagnosis research works have been classified in three groups, as a

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function of the item where the approach is applied. Thus, these three groups are electric machines, sensors and power electronics separately. Although there are interesting articles and reviews in fault diagnosis for traction drives [28,30], the applications works for Railway traction drives are more limited. Thus, in the following subsections, the fault diagnosis research in Railway traction drives is presented, analysing the fault diagnosis in electric machines, sensors and power electronics. Furthermore, the fault diagnosis approaches implemented in Railway traction drives are summarized in Table 2. Table 2. FDI approaches in Railway Traction drives. FDI Approach

Reference

Electric Machine

MCSA VCT Flux FFT Current transient FFT Observer-based ToMFIR PCA, ICA Model-based estimator Wavelet entropy Wavelet energy and SVM Current FFT Fault signatures analysis Least squares

[63] [64] [65] [66] [31,33,67–71] [72,73] [74,75] [32,76] [77] [78] [79] [80] [81]

Rotor bars Rotor bars Dirt Insulation degradation

Sensors

Power Electronics

Voltage and current Speed and current Voltage, current and speed Power switches Power switches Power switches Power switches Power switches Capacitor

4.1. Fault Diagnosis in Electric Machines Induction motors are the most usual electric machines in railway applications, although recently permanent magnet motors have been utilized too for railway applications [82]. The main faults in induction motors are rotor faults, stator windings faults and mechanical faults [30]. The rotor faults are mainly due to broken rotor bars or core damage. Stator winding faults are given by inter-turn short-circuits. On the other hand, there is a variety of mechanical faults, such as eccentricity and bearing faults. Bearing faults are the most usual faults in electric machines according to [83]. Despite the variety of fault diagnosis approaches for induction machines presented in the literature [84–87], the application to Railway traction drives is more limited. Thus, in [63] the rotor bars faults are detected by Motor current signature analysis (MCSA) for an induction motor used for a high speed railway application. MCSA is one of the most usual signal-based FDI approach for fault detection in electric machines. MCSA is based on stator current frequency spectrum analysis for faulty induction machine in comparison to a fault-free induction machine and its main drawback is that it can be only applied in steady state. An example for an induction motor with unbalanced phases is shown in Figure 11. Faults such as rotor bars breakage, eccentricity or inter-turn short-circuits can be detected [30] with this strategy. On the other hand, in [64] the oscillation that arises in the flux when a rotor fault occurs is analysed. The oscillation frequency is twice the slip frequency. This approach, called Virtual Current Technique (VCT), can be used in transient and steady states. Furthermore, compared to other techniques as Vienna Monitoring Method (VMM), VCT allows linking the oscillation and the number of broken rotor bars. FFT is used too in [65] to detect dirt in motors, as the accumulation of electromagnetic material generates changes in the flux harmonics amplitudes. Finally, in [66] the motor winding insulation degradation is detected. Inverter applies a step voltage to each motor phase and transient current are analysed. An insulation degradation is revealed in a parasitic capacitor change, being the capacitor the main factor for the current transient behaviour.

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3 Peak in current spectrum due to

|iu(A)|

an unbalance in motor phases

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Figure 11. 11. Comparison of stator current frequency spectrums for balanced and unbalanced motor motor phases. Figure Comparison of stator current frequency spectrums for balanced and unbalanced phases.

4.2. Fault Diagnosis in Sensors

4.2. Fault Diagnosis Sensors Several sensorsinare located in a Railway traction drive. A review is presented in [21]. Current and voltage sensors, such as catenary current, motor phase current and DC-link voltage, are based on Several sensors are located in a Railway traction drive. A review is presented in [21]. Current Hall-Effect. In Hall-effect sensors, offset and gain faults can arise due to magnetic core degradation, and voltage sensors, such as catenary current, motor phase current and DC-link voltage, are based magnetic properties variation with temperature or change of the magnetic field orientation [88]. on Hall-Effect. In Hall-effect sensors, offset and gain faults can arise due to magnetic core Speed sensors in Railway applications are normally variable reluctance sensors, or Hall-Effect sensors. degradation, magnetic properties variation with temperature or change of the magnetic field Commonly, sensors take part in the control strategy, so hard faults, such as sensor disconnection; have orientation [88]. Speed sensors in Railway applications are normally variable reluctance sensors, or as consequence the protections activation. Thus, early diagnosis is not possible in these cases, although Hall-Effect sensors. Commonly, sensors take part in the control strategy, so hard faults, such as sensor it can help for a faster corrective maintenance. disconnection; have as consequence the protections activation. Thus, early diagnosis is not possible Among the sensor fault diagnosis articles in Railway traction drives, in [67] a Luenberger in these cases, although it can help for a faster corrective maintenance. observer based approach for DC-link voltage and catenary current sensor fault diagnosis is presented Among the sensor fault diagnosis articles in Railway traction drives, in [67] a Luenberger The observer design is based on the rectifier model. A bank of residuals, taken from Luenberger observer based approach for DC-link voltage and catenary current sensor fault diagnosis is presented observer errors and open loop estimations, are used for fault isolation. The FDI approach is validated The observer design is based on the rectifier model. A bank of residuals, taken from Luenberger in an experimental test bench. Similar to this, in [31] another approach is presented, using the observer errors and open loop estimations, are used for fault isolation. The FDI approach is validated input filter model for a DC supplied traction drive. In this case, the approach is validated in a in an experimental test bench. Similar to this, in [31] another approach is presented, using the input Hardware-in-the-loop platform (HIL) with a commercial TCU for a tram. Furthermore, a fusion of filter model for a DC supplied traction drive. In this case, the approach is validated in a Hardwaredifferent FDI approaches is proposed to improve the reliability of the fault isolation. A unknown in-the-loop platform (HIL) with a commercial TCU for a tram. Furthermore, a fusion of different FDI input observer (UIO) is proposed in [68], using the inverter model instead of the rectifier model and in approaches is proposed to improve the reliability of the fault isolation. A unknown input observer order to get a FTC. UIO is implemented due to the unknown inputs in the inverter model. A sliding (UIO) is proposed in [68], using the inverter model instead of the rectifier model and in order to get mode observer (SMO) is proposed in [69], again with the rectifier model. Compared to Luenberger a FTC. UIO is implemented due to the unknown inputs in the inverter model. A sliding mode observer, SMO can be applied to non-linear systems and it has a better robustness under modelling observer (SMO) is proposed in [69], again with the rectifier model. Compared to Luenberger uncertainties and measurement noises. In [70] a bank of SMO observers is proposed for DC-link observer, SMO can be applied to non-linear systems and it has a better robustness under modelling voltage and catenary current sensors. Later, in [33] a SMO in combination with adaptive techniques in uncertainties and measurement noises. In [70] a bank of SMO observers is proposed for DC-link order to detect the different fault modes for a DC-link voltage sensor is presented. In [69] a SMO-based voltage and catenary current sensors. Later, in [33] a SMO in combination with adaptive techniques sensor FDI for incipient faults is presented. Adaptive thresholds are proposed in order to improve in order to detect the different fault modes for a DC-link voltage sensor is presented. In [69] a SMOdetectability. Finally, [71] presents the DC-link and catenary current sensors fault reconstruction based based sensor FDI for incipient faults is presented. Adaptive thresholds are proposed in order to on a SMO and the equivalent injection signal. Equivalent injection signal represents the average value improve detectability. Finally, [71] presents the DC-link and catenary current sensors fault to maintain sliding motion. The application of equivalent injection signal requires the treatment of reconstruction based on a SMO and the equivalent injection signal. Equivalent injection signal sensors as actuators, so a new augmented system should be designed from original system and sensors represents the average value to maintain sliding motion. The application of equivalent injection to be detected. The SMO for the augmented system is shown in Figure 12, being Gl and Gn feedback signal requires the treatment of sensors as actuators, so a new augmented system should be designed matrix gains, whereas ν is the nonlinear term responsible for sliding motion. from original system and sensors to be detected. The SMO for the augmented system is shown in Figure 12, being Gl and Gn feedback matrix gains, whereas ν is the nonlinear term responsible for sliding motion.

Figure 12. Sliding mode observer (SMO) proposed in [71] for sensor fault diagnosis.

f

vbus

(V)

v

bus

(V)

Moreover, this approach is validated in a HIL platform too, composed of a Real Time simulator and a commercial TCU. The fault reconstruction for a gain fault injected in DC-link voltage sensor is Appl. Sci. 8, 12 of of 19 Appl. Sci. 2018, 2018, 8, 2475 x FOR13. PEER REVIEW 12 19 shown in Figure On the other hand, in [72] another approach is presented for a nonlinear system, presented as a Takagi-Sukeno Fuzzy model. This approach is based on the total measurable fault information G v residual (ToMFIR) for fault isolation, whereasn a bank of observers is used for residuals generation. ToMFIR technique is presented in+ [73] too. This article affirms that the solution proposed can achieve Gl a better solution for sensor fault detection than observer-based approaches, as well as a more general z1 + e FDD framework. A HIL platform is used for validation, composed of a Real Time Simulator, an z1 z1 + + u C B 0 0 induction motor, a rectifier and a+ current∫sensor.x Another data-driven approach is presented in [74], z2 ez2 + where Independent Component Analysis (ICA)z in combination with Kullback-Leibler divergence z2 (KLD) is presented to detect incipient faults in motor current sensors. ICA, in contrast to PCA, A0 provides satisfactory results for non-Gaussian measurement signals. Furthermore, this approach does not require the probability density function (PDF) calculation, reducing the computational cost Figure 12. 12. Sliding mode observer (SMO) (SMO) proposed proposed in in [71] [71] for for sensor sensor fault fault diagnosis. diagnosis. Figure and allowing an online implementation. Recently, a new approach based on PCA and called probability relevant PCA [75],validated presents a acomparison amongcomposed different PCA-based approaches. It Moreover, Moreover, this this approach approach is is validated in in a HIL HIL platform platform too, too, composed of of aa Real Real Time Time simulator simulator reduces the missing alarm ratio and false alarm rate in case of incipient faults in current and voltage and a commercial TCU. The fault reconstruction for a gain fault injected in DC-link voltage and a commercial TCU. The fault reconstruction for a gain fault injected in DC-link voltage sensor sensor is is sensors of an electrical drive for a high-speed train. shown shown in in Figure Figure 13. 13. On the other hand, in [72] another approach is presented for a nonlinear system, presented as a Takagi-Sukeno Fuzzy model. 800 This approach is based on the total measurable fault information residual (ToMFIR) for fault isolation, whereas a bank of observers is used for residuals generation. 600 ToMFIR technique is presented in [73] too. This article affirms that the solution proposed can achieve a better solution for sensor fault400detection than observer-based approaches, as well as a more general FDD framework. A HIL platform is used for validation, composed of a Real Time Simulator, an 200 Real DC-link voltage Another data-driven approach is presented in [74], induction motor, a rectifier and a current sensor. Measured DC-link voltage where Independent Component0 Analysis (ICA) in combination with Kullback-Leibler divergence 70 40 50 20 30 60 (KLD) is presented to detect incipient faults in motor current sensors. ICA, in contrast to PCA, t (s) provides satisfactory results for non-Gaussian measurement signals. Furthermore, this approach (a) does not require the probability 50density function (PDF) calculation, reducing the computational cost and allowing an online implementation. Recently, a new approach based on PCA and called 0 probability relevant PCA [75], presents a comparison among different PCA-based approaches. It reduces the missing alarm ratio-50and false alarm rate in case of incipient faults in current and voltage sensors of an electrical drive for a high-speed train. -100 -150

-200

vbus (V)

20

30

40

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t (s)

(b)

Figure 13. (a) Real and measured DC-link voltage; (b) DC-link voltage sensor fault reconstruction. Figure 13. (a) Real and measured DC-link voltage; (b) DC-link voltage sensor fault reconstruction.

fvbus (V)

On the other hand, in [72] another approach is presented for a nonlinear system, presented as a Takagi-Sukeno Fuzzy model. This approach is based on the total measurable fault information residual (ToMFIR) for fault isolation, whereas a (a) bank of observers is used for residuals generation. ToMFIR technique is presented in [73] too. This article affirms that the solution proposed can achieve a better solution for sensor fault detection than observer-based approaches, as well as a more general FDD framework. A HIL platform is used for validation, composed of a Real Time Simulator, an induction motor, a rectifier and a current sensor. Another data-driven approach is presented in [74], where Independent Component Analysis (ICA) in combination with Kullback-Leibler divergence (KLD) is presented to detect incipient faults in motor current sensors. ICA, in contrast to PCA, provides satisfactory results for non-Gaussian measurement signals. Furthermore, this approach does not require the probability density function (PDF) calculation, reducing the computational cost and allowing an online implementation. Recently, a new approach based on PCA and called probability relevant PCA [75], presents a comparison among (b)different PCA-based approaches. It reduces the Figure 13. (a) Real and measured DC-link voltage; (b) DC-link voltage sensor fault reconstruction.

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missing alarm ratio and false alarm rate in case of incipient faults in current and voltage sensors of an electrical drive high-speed Appl. Sci. 2018, 8, x for FORaPEER REVIEW train. 13 of 19 4.3. Fault Diagnosis Diagnosis in in Power Power Electronics 4.3. Fault Electronics The in Railway drives are are the The main main power power converters converters in Railway traction traction drives the inverter inverter to to supply supply the the electric electric machines and the braking chopper, as it is shown in Figure 14. Usually more than one electric machines and the braking chopper, as it is shown in Figure 14. Usually more than onemachine electric is supplied with the same inverter. The goal of the chopper is to dissipate the energy during braking machine is supplied with the same inverter. The goal of the chopper is to dissipate the energy during period it isthat not itpossible to return catenary. IGBT based powerpower converters are mainly used brakingthat period is not possible to to return to catenary. IGBT based converters are mainly for Railway traction drivesdrives [89]. A rectifier needsneeds to be added in caseinofcase ACof supplied traction units. used for Railway traction [89]. A rectifier to be added AC supplied traction The most usual faults in the power electronics of Railway traction drive occur in capacitor and power units. The most usual faults in the power electronics of Railway traction drive occur in capacitor and switches [90]. The[90]. main faults in IGBTs given in DC-link capacitor main faults are summarized power switches The main faults are in IGBTs are[91]. given in [91]. DC-link capacitor main faults are in [92]. summarized in [92].

T1

T7

T5 IM M

u

RB

CB

T3 v

w IM M T2

DC- bus

Braking chopper

T4

T6

Inverter

Power electronics electronics for a traction drive connected to DC catenary. catenary. Figure 14. Power

In [32] is is proposed to detect faults in the of a single-phase PWM [32] aamodel-based model-basedestimator estimator proposed to detect faults inswitches the switches of a single-phase rectifier and later for cascaded H-bridge rectifiers [76]. The approach is validated in a HIL platform PWM rectifier and later for cascaded H-bridge rectifiers [76]. The approach is validated in a HIL composed of a Real Time Simulator and a TCU. Only catenary and switching platform composed of a Real Time Simulator and a TCU. Onlycurrent, catenaryDC-link current,voltage DC-link voltage and signals from TCU are needed. Wavelet entropy approach is used in [77] to detect open switches in a switching signals from TCU are needed. Wavelet entropy approach is used in [77] to detect open inverter for High-Speed railways. The detection method is based on the hypothesis that the motor switches in a inverter for High-Speed railways. The detection method is based on the hypothesis that current forcurrent faulty case has more components that for thethat fault-free In [78] a discrete the motor for faulty casefrequency has more frequency components for the case. fault-free case. In [78] wavelet transform is used and then the signal is decomposed into energy vectors. A support vector a discrete wavelet transform is used and then the signal is decomposed into energy vectors. A support machine (SVM) is applied for IGBTfor faults classification. In [79] a signal-based approach is presented vector machine (SVM) is applied IGBT faults classification. In [79] a signal-based approach is to detect open-circuit faults in IGBTs The detection based of is FFT analysis, to the presented to detect open-circuit faultsofinrectifier. IGBTs of rectifier. Theisdetection based of FFTdue analysis, frequency harmonicsharmonics change inchange case ofin faulty switch. patterns detected in case in ofcase IGBT due to the frequency case of faulty The switch. The patterns detected offaults IGBT are presented in [80]. in This research works affirms a latch-up generates overcurrent faults are presented [80]. This research worksthat affirms that process a latch-up processangenerates an whereas a secondary an overtemperature. In [81] a estimation of the capacitance overcurrent whereasbreakdown a secondaryproduces breakdown produces an overtemperature. In [81] a estimation of of the DC-link capacitor is done during its discharge, by mean of least mean square algorithm. In this the capacitance of the DC-link capacitor is done during its discharge, by mean of least mean square case, a real time fault diagnosis is not implement butisitnot is justified that but there several train stops,are so algorithm. In this case, a real time fault diagnosis implement it are is justified that there the calculation can be done. several train stops, so the calculation can be done. 5. Discussion and and Conclusions Conclusions 5. Discussion CBM applications butbut an an increasing importance in availability, as well CBM is is relatively relativelynew newininRailway Railway applications increasing importance in availability, as as maintenance cost reduction, has made it a key point. The importance of traction drive in Railway well as maintenance cost reduction, has made it a key point. The importance of traction drive in system led tohas develop fault diagnosis for the Health the tractionof drive. Railwayhas system led to develop faultapproaches diagnosis approaches forAssessment the HealthofAssessment the Although the literature is not so broad in Health Assessment in Railway traction drives, compared to traction drive. Although the literature is not so broad in Health Assessment in Railway traction aerospace systems, during the last decade a variety of research works have been done. drives, compared to aerospace systems, during the last decade a variety of research works have been

done. Despite there are interesting reviews in Health assessment for on-board mechanical systems, such as wheel, suspension or bogie frame, there is a need to summarize all the research work carried out, both in fault diagnosis and health assessment, in Railway Traction drives, as part of a CBM framework.

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Despite there are interesting reviews in Health assessment for on-board mechanical systems, such as wheel, suspension or bogie frame, there is a need to summarize all the research work carried out, both in fault diagnosis and health assessment, in Railway Traction drives, as part of a CBM framework. The framework for CBM implementation is based on Standard ISO13374, initially developed for diagnosis of machines. As moving vehicle, different topologies are presented for CBM implementation, depending on the location Health Assessment module of Standard ISO13374. Thus, it is possible to implement a remote or on-board diagnosis. Furthermore, the diagnosis can be distributed or centralized. In case of remote diagnosis, a huge quantity of data should be transferred to off-board maintenance centre, due to variety of variables and high sample time, with the constraints of a moving vehicle, such as connectivity or data storage on-board. Then, fault diagnosis approaches, mainly data-driven, can be used for Health Assessment. It is not possible to implement a real-time fault detection but it can be a suitable solution for prognosis or for faults with low dynamics. In the near future, due to the advances in communications between on-board and remote systems, data would be transferred with a latency of 100 ms, which could be enough to detect faults in systems with low dynamics. Nowadays, main rail equipment manufacturers provide supervision systems, which allow transferring data from on-board traction unit to remote maintenance centre, for a further analysis. On the other hand, in case of on-board diagnosis, the fault detection can be in real-time and there is no need of huge quantity of data to be transferred to off-board maintenance centre, as Health Assessment is done on-board. A faster fault detection and isolation is done, allowing a further reconfiguration for a fault tolerant system. Model-based or signal-based approaches, which do not need to store huge quantity of data, could be suitable. As a possible drawback, the computational cost to implement the diagnosis algorithms in the on-board control traction unit. Among the fault diagnosis approaches implemented for Railway traction drives, signal-based approaches are mainly used in electrical machines, in order to detect rotor bars breakage or eccentricities. Wavelet transform has been implemented to detect open-circuit faults in power switches. Wavelet transform is used in combination with classification techniques too. Among the model-based approaches implemented for sensor fault detection, observer-based approaches are mainly utilized. Moreover, fault reconstruction by mean of a sliding mode observer is implemented too. Fusion of different model-based approaches are presented too in order to detect and isolate the faults in all the sensors located in a traction drive. Finally, data-driven approaches PCA and ICA are used for sensor fault detection, applied to incipient fault detection. First, an offline stage for threshold setting is needed, based on historical fault-free data and finally the online detection is done by processing the actual data. The main drawback of these statistical analysis based approaches for on-board implementation could be the data storage capacity, until data can be transferred to remote maintenance centre and the high computational cost. Due to the fact that apart from score vectors calculation, normally PDF and statistics tests need to be calculated. On the other hand, it is not needed the knowledge of the physical system to build a model, as it is required in Model-based approaches. A combination of model-based and data-driven approaches can benefit from both methods, in order to detect and isolate faults with different dynamics. Moreover, among the scientific publications, most of the fault diagnosis presented are implemented in lab platforms and only few of them implemented the diagnosis approaches in a Railway traction control unit, considering the computational limitations of the real Railway application. In this case, HIL platforms composed of a commercial Railway traction control unit and a real time simulator are used for validation. Author Contributions: Conceptualization, F.G. and J.P.; Investigation, F.G., P.M. and J.d.O.; Writing—Original Draft Preparation, F.G. and J.P.; Writing—Review & Editing, P.M., J.d.O. and G.A. Funding: This research was supported by CAF Power and Automation. Acknowledgments: The authors are thankful to the colleagues from CAF Power and Automation, who provided material and expertise that greatly assisted the research. Conflicts of Interest: The authors declare no conflict of interest.

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8. 9.

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