Introducing Concepts and Methodologies of Fault

Introducing Concepts and Methodologies of Fault Detection into Electrical Engineering Education: the Induction Machine Example Gerasimos Pagiatakis, Leonidas Dritsas, George Chatzarakis Department of Electrical and Electronic Engineering Educators School of Pedagogical and Technological Education Athens, Greece [email protected], [email protected], [email protected] Abstract—The scope of this paper is to present the fundamental concepts and methodologies of fault detection, fault tolerant control and monitoring in an simplified manner, affordable to undergraduate electrical engineering students. The most common faults that may occur during the induction motor operation are depicted, since the induction machine is still the workforce of the industry. The throughout presentation is supported by various experimental and simulation results. Finite element analysis of a faulty induction motor is also presented in order to highlight the application of the method for modeling faulty conditions and the importance that it holds on the better understanding of the fault and the effects it has on the machine performance and electromagnetic field, as they can’t be observed directly. Keywords—fault detection; fault tolerant control; monitoring; induction machine; Finite element analysis

I.

WHY FAULT DETECTION CONCEPTS MUST BE INTRODUCED IN THE ELECTRICAL ENGINEERING EDUCATION Fault Detection & Diagnosis (FDD), Fault Tolerant Control (FTC) and Preventive-Maintenance/Monitoring (PM) have traditionally been an integral part in many important technologies such as chemical process control, nuclear engineering, automotive applications, and, above all, in Aerospace & Aviation. Recently, Fault Detection & Diagnosis (FDD/ FTC) and PM have become an important issue in many electrical engineering applications as well, and thus it is an active area of research in the electric power and the control systems community. More specifically, there exists an increased need for reliable fault diagnosis techniques and monitoring of electrical machines leading to the need of trained engineers who can understand and distinguish the numerous faults that may occur in electrical machines (motors/generators) and take appropriate action. Moreover, it is well known that the majority of tasks needed to be carried out by practicing industrial engineers mainly have to do with maintenance, health monitoring and fault detection/isolation in the machinery used. This is briefly the rationale for electrical engineering students to be exposed to the concepts of fault

George Todorov, Bozhidar Stoev Department of Electrical Machines Technical University of Sofia Faculty of Electrical Engineering Sofia, Bulgaria [email protected], [email protected]

detection & diagnosis in parallel to the undergraduate/graduate courses on electric machines, drives and electric power. II.

CLARIFICATION OF TERMS

An important first step in teaching FDD/FTC/PM to engineering students is the clarification of terms [1]. This is essential since there is a lack of consistency and common understanding when dealing with complex concepts and notions such as fault detection, fault tolerance and preventive maintenance in large scale electrical engineering applications. Some fundamental definitions, borrowed from the IFAC SAFEPROCESS Committee, are presented below [1]. The defined terms include system “states”, signals (to be monitored) and functions (to be carried out preferably in an automatic way by FTC systems). Fault: An unpermitted deviation of at least one characteristic property or parameter of the system from the acceptable, usual or standard condition. Failure: A permanent interruption of a system’s ability to perform a required function under specified operating conditions. Malfunction: An intermittent irregularity in the fulfilment of a system’s desired function. Disturbance: An unknown (and uncontrolled) input acting on a system. Residual: a fault indicator, based on a deviation between measurements and model-equation-based computations. Fault detection: Determination of the faults present in a system and the time of detection. Fault isolation: Determination of the kind, location and time of detection of a fault. Follows fault detection. Fault identification: Determination of the size and timevariant behavior of a fault. Follows fault isolation.

978-1-5090-5466-4/17/$31.00 ©2017 IEEE 25-28 April 2017, Athens, Greece 2017 IEEE Global Engineering Education Conference (EDUCON) Page 373

Fault diagnosis: Determination of the kind, size, location and time detection of a fault. Follows fault detection. Includes fault isolation and identification. Monitoring: A continuous real-time task of determining the conditions of a physical system by recording information, recognizing and indicating anomalies in the behavior. Supervision: Monitoring a physical system and taking appropriate actions to maintain the operation in case of faults. Protection: Means by which a potentially dangerous behavior of the system is suppressed if possible, or means by which the consequences of a dangerous behavior are avoided. A. Classification of Fault detection methods Hardware redundancy based fault detection: In this method, the process component is replaced by and identical hardware component. If there is any deviation of the output of the process component, information about a possible fault may be extracted and the fault can be isolated in time. The main advantages of this fault detection method are that it has a good reliability and the ability to isolate faults. The drawbacks of the method are the extra components, increased maintenance cost and additional space required to accommodate the redundant equipment. This limits the use of the method to number of key applications such as nuclear power plants or flight-control systems [2]. Plausibility test: The idea behind this technique is the evaluation of a measured process variable, using convincing values representing the healthy condition of the system and their mutual compatibility. The presence of a fault in a certain variable can be determined by using the plausibility check, on the assumption that a fault leads to the loss of plausibility [2]. It can be achieved by using simple rules with binary logic. This leads to a test procedure where it is determined whether the measured variable is within predefined limits determined by the variable variations at healthy conditions. The presence of a fault is evaluated if the measured value of the variable is outside of the specified limits. A drawback of this method is that the limits of variables variation are unique and have to be established for each examined object. Signal-based fault diagnosis: This procedure is based on extracting information about the presence of a fault from the process signals. For this purpose some signal properties (“symptoms”) are analyzed, which are divided into time and frequency domain characteristics of the processed signal(s). The time domain characteristics generally comprise magnitude, limit values, trends and statistical moments of the amplitude distributions. The frequency domain characteristics include spectral power densities and frequency spectral lines. Signalbased fault detection is used mostly for steady state operations of the process and its efficiency is limited when the process is operating in a wide range due to the possible variation of input signals. A good example for the application of this method is the detection of bearing faults, eccentricity faults and broken rotor bar faults. In order to detect these faults, the line stator currents are measured and the obtained waveforms are analyzed in the frequency domain, with the idea of detecting additional frequency components which indicate the presence of a fault [2, 3].

Model-based fault detection: The core idea of the modelbased technique is to replace the hardware redundancy by a process model which is implemented in software. The process model runs simultaneously with the process itself and is driven by the same process inputs. In this way, the process behavior can be reconstructed online. This technique is also known as “software redundancy” or “analytical redundancy”. Modelbased fault detection methods are more powerful than the signal-based methods due to the fact that they use more information about the process [2, 3]. There are two stages in the model-based fault detection: residual generation and residual evaluation. The “residual signal” is generated by comparing the process output with its estimate. The residual signals carry information about the fault, but since in a real process they are affected by the faults, disturbances, and measurement noises simultaneously, it is required to process them further in order to obtain useful information, which is done in the residual evaluation stage. A process model represents the qualitative and quantitative behavior of the process and can be obtained by utilizing system modeling techniques. The quantitative model of the process is represented by a set of differential equations while the qualitative model is expressed by functions, centered around different units in the process. This leads to the classification of the model based techniques: knowledge-based and analytical. Knowledge-based fault detection techniques are used when the precise model of the process is not available or hard to obtain. They include neural networks, petri nets, expert systems and fuzzy logic. On the other hand, analytical modelbased fault detection techniques make use of analytical models for the purpose of residual generation. They can be classified as parity space fault detection, observer-based fault detection and parameter-identification-based fault detection [2]. III.

COMMON FAULTS IN INDUCTION MACHINES

Nowadays, induction machines are widely used for in many industrial applications. Although, in recent years, other types of electric machines, such as permanent magnet synchronous machines and switched reluctance motors, may have replaced them in some fields of industry, the induction motor is still preferred due to its robustness and low manufacturing cost. This makes it imperative to introduce students to the common faults that may occur during the induction motor operation. A. Bearing faults Bearing faults are among the common faults in rotating electrical machines. They may occur due to impacts, overloading, overheating, inadequate lubrication, contamination and corrosion from abrasive particles or acid or improper installation, which leads to excessive misalignment errors. Bearing faults are the main source of vibration in rotating electrical machines and depending on where the fault takes place can be classified as outer raceway defect, inner raceway defect, ball defect and cage defect (see Fig. 1 and 2). The mechanical vibrations due to bearing faults can be considered as slight rotor displacements, which result in instant eccentricities in the air-gap and distort the air-gap symmetry and machine inductances. At early stage of the fault the vibrations are minor and nearly unrecognizable, but if the fault


is not detected in time, it grows to a stage that can lead to total machine failure and catastrophic consequences for the whole system. Each bearing fault has specific mechanical vibration frequency components that characterize each defect type and are functions of both bearing geometry and speed. Mechanical vibration, thermal and acoustic analyses are some of the commonly used methods to monitor the health of the bearings. The drawback of these methods is that they require additional sensors and transducers to be installed in the machine. This may be applicable for large power motors, but it is hard to implement for medium and low power machines. A technique presented in [3, 4, 5, 6] is based on the effect the rotor displacement due to a bearing fault has on the machine inductances. The air gap eccentricities lead to inductance variations, which are reflected to the stator line currents in terms of current harmonics. In the laboratory practice students are taught to bearing faults detection and analysis performing two types of experiments over induction motor with a faulty bearing: -

Frequency components analysis of mechanical vibrations taken from a piezoelectric sensor, mounted on the motor frame;

-

By measuring the line currents and performing spectral analysis on the waveforms, where additional frequency components appear and indicate the presence of a bearing fault.

B. Stator faults The stator fault can be broadly classified as the lamination or frame fault and stator winding fault. The stator winding faults are the most common stator fault in induction machines. They most frequently start due to insulation breakdowns between two or more adjacent copper conductors of the stator coil. This type of fault is referred to as inter turn short circuit which produces extra local heat and imbalance of the phase currents and the electromagnetic field inside the machine. The overheat leads to further insulation deterioration and increasing the number of short-circuited turns until the catastrophic machine failure occurs like phase to phase failure or phase to ground failure [7, 8]. Motor and generator stator winding faults during machine operation can lead to serious machine failure resulting in a costly damage. The prevention of such faults is a major concern for both machine manufacturers and users, since such a major fault may not only destroy the machine but may spread in the system. To train students in the detection of stator winding interturn short circuit, the authors have developed the following procedure, consisting of stator currents measurement and further analysis with the aid of Park transformations [8].

Fig. 3. Phase currents at no load for healthy winding

Fig. 1. Ball bearing cross section

Fig. 2. Example of a damaged bearing ball

Fig. 4. Phase currents at no load with inter turn short circuit


Fig. 7. Total current and current components for stator winding with inter turn short circuit at rated load Fig. 5. Phase currents at rated load for healthy winding

Fig. 8. Plot of current hodograf for stator winding with inter turn short circuit at rated load Fig. 6. Phase currents at rated load with inter turn short circuit

Experimentally obtained current waveforms of healthy winding and winding with inter turn short circuit are shown in Fig. 3 to 6, for no-load and rated load operation. It is evident that the inter turn short circuit leads to stator currents unbalance and appearance of negative sequence current components. For better understanding and quantitative analysis the measured currents are subjected to Park’s transformations of three phase coordinate systems - abc to αβ0 coordinate system (stationary coordinate system fixed to the stator). Park transformation of a balanced sinusoidal three phase current system (healthy winding) results in an equivalent two phase system where the components of the stator current isα and isβ are equal and have a 90 degrees phase shift between them. The plot of the stator current vector components shows a circular pattern, centred at the origin of the coordinate system. Park transformations are applied to the instantaneous values of the phase currents in the case of stator winding inter turn fault at rated load operation to illustrate the influence of the unbalance due to inter turn short circuit. The results are presented in Fig. 7 and 8.

The total stator current vector is denoted as is and its components are denoted as isα and isβ respectively (see Fig. 7). The variations in the amplitude of the stator current vector shows that due to the unbalance, resulting from the inter turn short circuit, the magnetic field of the motor is not circular. The negative sequence currents create a backward rotating field component and the resultant electromagnetic field exhibits as ellipsoidal (see Fig. 8). This procedure illustrates to students that by monitoring the deviations of the acquired patterns [3, 8] this type of current analysis allows the detection of faulty conditions, can warn for increasing risk and help prevent a major fault occurrence. C. Broken rotor bar fault A broken rotor bar can be considered as rotor asymmetry that causes unbalance in the line currents, torque pulsation and decreases average torque. The most common approach to determine the presence of broken rotor bars described in literature is the motor current signature analysis [6, 8]. This method utilizes the results of a spectral analysis performed on the stator line currents waveforms. During regular operation, a symmetrical stator winding excited at frequency f1 induces rotor bar currents at sf1 frequencies (where s denotes the slip).


With broken bars in the rotor there is an additional, backward rotating magnetic field produced, which is rotating at the slip speed with respect to the rotor. The backward rotating magnetic field speed produced by the rotor due to broken bars and with respect to the rotor is:

nb

n n2

n1 1 s sn1

n1 1 2s

(1)

The stationary winding now sees a rotating field at:

nb

n1 1 2s

(2)

or expressed in terms of frequency:

fb

f1 1 2s

(3)

A rotating magnetic field at that frequency induces a current at the frequency fb. This in fact means that fb is a twice slip frequency component spaced 2sf1 down from f1. Thus speed and torque oscillations occur at 2sf1 and this induces an upper sideband at 2sf1 above f1. By performing a spectral analysis of the line currents waveforms, these additional frequency components can be accounted for and indicate a presence of a broken rotor bar fault. This method can be directly used in practical applications and it is important to introduce students to this concept of bearing fault detection, but it does not provide any information regarding what effects a broken rotor bar has on the field distribution inside the machine volume. This can be observed by using numerical methods to simulate and better understand the fault manifestation.

The field distribution obtained by students as a result of the Finite element analysis for the cases of one, two, three and four broken rotor bars are presented in Fig. 9 to 13. The distortion of the electromagnetic field in the machine and the local saturation of the stator teeth and yoke is clearly visible (Fig. 12 and 13). As stated in [4, 6], a broken rotor bar fault leads to average torque decrease, presence of high order harmonics due to the distortion of the electromagnetic field, increased torque pulsation during steady state operation and current increase in the adjacent rotor bars. In order to observe the decrease of the average torque of the motor, students are advised to use the Weighted stress tensor volume integral, applied to the rotor of the examined cases [10]. This way they can observe that when a higher number of broken rotor bars are present (the case of three and four broken bars) the torque decreases to a stage, where the motor would not be able to supply the load. It’s explained to them that this could lead to an increase of the stator currents and overheating of the stator winding, which could cause insulation breakdown and inter turn short circuit. The obtained torque decrease is presented in per units in Fig. 14, with the value of the healthy rotor torque assumed as base value.

The Finite element method (FEM) is well established for analysis of different types of rotating electrical machines and can be used to provide sufficient information regarding the electromagnetic field distribution inside the induction machine and its performance in case of broken rotor bar fault [3, 4]. In a research presented in [4] is stated that a broken rotor bar is characterized by a high electrical resistance. The resistance of the healthy rotor bar is considered as 39.42 μΩ and the resistance of the broken rotor bar as 2500 μΩ. This statement is the starting point for the development of the teaching procedure, used to introduce students to what effects a broken rotor bar fault has on the performance of an induction motor. By using Finite element method, a numerical model of an aluminum squirrel cage induction motor with specifications, presented in Table I, is prepared. In a computer laboratory environment, students are advised to vary the values of the resistance of different number of rotor bars.

Fig. 9. Healthy rotor

TABLE I. Rated data of the examined induction motor 1

Rated power

P2

kW

7,5

2

Rated supply voltage

U1

V

400

3

Rated current

I1

A

8,075

4

Rated supply frequency

f1

Hz

50

5

Rated slip

s

-

0,0192

6

Pole pairs

p

-

2 Fig. 10. One broken bar (marked with green)


the cases of a healthy rotor, two and four broken bars. An increase of the odd number harmonics amplitudes is evident despite the delta connection of the stator winding. The increased harmonic content leads to increased torque pulsations during the motor steady state operation. A time-stepping FEM model is developed by the authors in order for students to obtain the steady state torque production and observe the effect of the broken rotor bar fault on the torque pulsations. In this model, students assign values of the rated phase currents, time period and time step. The results of the time-stepping Finite element analysis for the cases of healthy rotor, two and four broken bars are presented in Fig. 17. The torque ripples due to the increased harmonic content are clearly visible in the presented results.

Fig. 11. Two broken bars (marked with green)

Fig. 14. Average torque decrease

Fig. 12. Three broken bars (marked with green)

Fig. 15. Air gap flux density distribution

Fig. 13. Four broken bars (marked with green)

By extracting the air gap flux density distribution as a result of the Finite element analysis and performing spectral analysis of the obtained waveforms, students can account the increase of the high order harmonics content due to the presence of a broken rotor bar. The results are presented in Fig. 15 and 16 for

Fig. 16. Harmonic content


Fig. 17. Torque pulsations during induction motor steady state operation

The analysis with the aid of Finite element method allows the determination of the current increase in the rotor bars adjacent to the broken one. For example, calculations performed by students for the case of one broken bar report, that the current in the healthy bar on the left of the broken one has increased by 3,43% and for the healthy bar on the right the current has increased by 11,01% (see Fig. 10). D. Eccentricity fault Air-gap eccentricity is a condition of a non-uniform distance between the rotor and stator air-gap. There are two types of eccentricity: static eccentricity and dynamic eccentricity. Static eccentricity occurs when the centerline of the shaft is at a constant offset from the center of the stator, resulting in a constant non-uniform air-gap at all operational modes (Fig. 18). In the case of dynamic eccentricity, the offset occurs only when the rotor runs and revolves around the centerline of the shaft (Fig. 19). Both eccentricity faults result in an asymmetric magnetic field distribution in circumferential direction, which creates imbalances in the stator currents. As a result, eccentricity faults, alike the bearing faults, lead to additional torque pulsations, mechanical vibrations and acoustic noise. A large offset may lead to the rotor blocking within the stator, which is the worst case scenario when this kind of fault occurs during machine operation under load. Rotor blocking results in catastrophic effects on the motor and direct failure of the whole drive. If the motor with blocked

Fig. 18. Static eccentricity

Fig. 19. Dynamic eccentricity

rotor is not rapidly disconnected from the supply grid, this will lead to stator winding short circuit and possible insulation failure, which on the other hand leads to great downtime for the whole system and costly damage repair. The detection of eccentricity faults is easier than detecting bearing faults due to their high amplitude signature in the line currents waveforms. Any additional harmonics oscillating at the speed due to non-uniform structure are expected to take place at rotating frequency sidebands of the synchronous frequency [4, 6]. Mechanical vibration, acoustic noise and current monitoring can all be used in order to detect and isolate an eccentricity fault at early stage. IV.

CONCLUSIONS

The paper presents the fundamental concepts of Fault Detection & Diagnosis (FDD), Fault Tolerant Control (FTC) and Preventive-Maintenance/Monitoring (PM). A simplified explanation and clarification of the terms and methods is imperative when introducing electrical engineering students to the concepts, due to the vastness and complexity of the field. In order to have a better understanding of the presented concepts it is helpful to introduce students to the faults that occur during machine operation and the application of the presented methodology. This is achieved by presenting the most common faults that may occur during induction machines operation, since this type of rotating electrical machine is still the workforce of the industry and is widely used for different applications. The throughout presentation is supported by introducing measurement techniques and their application for fault detection. The application of the Finite element method


for modeling induction motor faults is also depicted in the presented paper, due to its high accuracy and possibility for observation of numerous effects that cannot be accounted for by other means. ACKNOWLEDGMENT The authors acknowledge financial support for the dissemination of this work from the Special Account for Research of ASPETE through the funding program “Strengthening Research of ASPETE Faculty Members” and the support of the “Erasmus+/KA1” program. REFERENCES [1]

[2] [3]

Iserman R., P. Balle, Trends in the application of model-based fault detection and diagnosis of technical processes, Control eng. Practice, Vol.5, No. 5, pp 709-719, 1997 Mahmoud M., Y. Xia, Analysis and synthesis of fault-tolerant control systems, John Wiley & Sons Ltd, 2014, ISBN: 978-1-118-54133-3 Benbouzid M., A Review of Induction motor signature analysis as a medium for fault detection, IEEE Transactions on Industrial Electronics, vol. 47, no 5, October 2000, pp 984-993

[4]

Toliyat H., S. Nandi, Electric machines. Modeling, condition monitoring and fault diagnosis, CRC Press, Taylor & Francis Group NW, 2013, ISBN: 978-1-4200-0628-5 [5] Wang W., D Li, Induction motors – Applications, control and fault diagnostics, Chapter 4: Health Condition Monitoring of Induction Motors, November 2015, ISBN-13: 978-953-51-2207-4 [6] Mustafa M., On fault detection, diagnosis and monitoring for induction motors – Doctoral thesis, Printed by Luleå University of Technology, Graphic Production 2015, ISBN 978-91-7583-227-2 [7] Arkan M., D. Kostic-Perovic, P.J. Unsworth, Modelling and simulation of induction motors with inter-turn faults for diagnostics, Electric Power Systems Research 75, May 2005, pp 57–66 [8] Todorov G., G Bojilov, Monitoring and analysis of the stator current of an induction motor for detecting the internal nonsymmetry or short circuit, Proc. of Technical University of Sofia, Volume 63, Issue 5, pp 241-248, 2013 [9] Szabo L., B. Dobai, Rotor fault detection in squirrel-cage induction motors by current signature analysis, IEEE-TTTC - International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, May 13 – 15, 2004 [10] Meeker, D. Finite Element Method Magnetics - FEMM. User’s manual, Foster-Miller, MA, USA, 2004
