Electrostatic Monitoring of Gas Path Debris for Aero ... - IEEE Xplore

IEEE TRANSACTIONS ON RELIABILITY, VOL. 60, NO. 1, MARCH 2011

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Electrostatic Monitoring of Gas Path Debris for Aero-engines Zhenhua Wen, Hongfu Zuo, and Michael G. Pecht, Fellow, IEEE

Abstract—We present advanced condition monitoring technology based on electrostatic induction for detecting the debris in aero-engines exhaust gas. We also discuss the key technologies related to electrostatic monitoring systems, such as sensing technology, signal processing, feature extraction, and abnormal particle identification. The finite element method and data fitting method are applied to analyze the sensing characteristics of the sensor. We apply empirical mode decomposition and independent component analysis to effectively remove the noise mixed in with the monitoring signal. Certain diagnostic features extracted from the de-noised signal are presented here. A knowledge-acquisition model based on rough sets theory and artificial neural networks is constructed to identify the abnormal particles. The experiment results show the effectiveness of the methods proposed in this paper, and provide some guidelines for future research in this field for the aviation industry. Index Terms—Aero-engine, condition monitoring, electrostatic sensor, feature extraction, knowledge acquisition, signal processing.

ACRONYM AND ABBREVIATION PHM

Prognostics and health management

IDMS

Ingested debris monitoring system

EDMS

Exhaust debris monitoring system

SCU

Signal conditioner unit

JSF

Joint Strike Fighter

FEM

Finite Element Method

FFT

Fast Fourier Transform

ICA

Independent component analysis

IMF

Intrinsic Mode Functions (IMF)

EMD

Empirical Mode Decomposition

RMS

Root Mean Square

Manuscript received March 30, 2010; revised June 12, 2010; July 15, 2010; accepted July 15, 2010. Date of publication January 31, 2011; date of current version March 02, 2011. This work was supported by the National Science Foundation of China and the Civil Aviation Administration of China (60939003), and partly supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU8/CRF/09). Associate Editor: W. Wang. Z. Wen is with the school of Mechatronics Engineering, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou, 450015 China (e-mail: [email protected]). H. Zuo is with the RMS Centre, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 China (e-mail: [email protected]). M. G. Pecht is with the PHM Centre, City University of Hong Kong, Hongkong, and the Center for Advanced Life Cycle Engineering at the University of Maryland, MD 20742 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TR.2011.2104830

AL

Activity Level

ER

Event Rate NOTATION Charge of the particle Sensitivity at position Time series The Charge moving velocity I. INTRODUCTION

A

ERO-ENGINE safety is of paramount importance because the engine is the ‘heart’ of an aircraft. Monitoring technology is the foundation for implementation of prognostics and health management (PHM). Appropriate health monitoring technologies can provide an early warning when incipient faults appear in the engine system. This early warning can facilitate preventive maintenance actions to minimize the associated costs, and the risk of failures. However, some key components (such as the combustion section) of an aero-engine gas path have not fully taken advantage of relevant monitoring technology because of the limited structural space, and the high temperature environment. As a result, conservative time-based preventive maintenance strategies are still a dominant maintenance policy for these components. This policy can lead to the waste of the useful residual life of the components, and may also increase the risk of failure before the maintenance. The use of monitoring techniques on engine degradation may overcome problems encountered in time-based preventive maintenance. Currently available engine monitoring techniques include vibration and thermal monitoring, oil analysis, and borescope. They have the advantages of steady performance, multi-test points, better generality, and low sampling rates [1]–[4]. However, they are not perfect in providing accurate, timely diagnoses. Both vibration and thermal based monitoring can only assess the overall condition of the engines, but they can not provide enough detailed information for further, accurate diagnosis at the component level. In addition, mathematical models for thermal monitoring are extremely complex because an aero-engine is a complex system, and its work conditions frequently change; therefore, the models are usually unable to find a good solution. Additionally, oil analysis and borescope are off-line monitoring techniques that cannot provide timely diagnostic information. Particles in exhaust gas are the direct products of a fault that may occur in the aero-engine gas path. These particles result in the electrical charge of aircraft engine gases [5]–[7]. Monitoring

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Although this technology and the related monitoring systems have been used in the Joint Strike Fighter (JSF) program for many years [4], [8]–[10], there have been no papers that present the technical details of electrostatic monitoring technology. In this paper, we discuss and present the relevant key and feasible technologies for the electrostatic monitoring of gas path debris of aero-engines. II. PRINCIPLES OF GAS PATH DEBRIS MONITORING

Fig. 1. Schematic plot of the electrostatic monitoring system.

Fig. 2. Framework of the electrostatic monitoring technology.

technology based on electrostatic induction has been employed in very limited cases to monitor the work conditions of key components in the aero-engine gas path. It has been reported in [8]–[10] that the electrostatic monitoring technique can provide instantaneous, accurate warning information before failure, thus effectively improving the PHM capability of aero-engines. Fig. 1 shows a scheme of a gas path monitoring system such as the ingested debris monitoring system (IDMS), and the exhaust debris monitoring system (EDMS) [9]. The main elements of the monitoring system are the electrostatic sensor, the signal conditioner unit (SCU), the data acquisition card, and a data processor. The electrostatic sensor mounted in the exhaust pipe detects electrostatic charges present within the exhaust gas. The SCU is a charge amplifier that converts the charge signal to a voltage suitable for subsequent acquisition, processing, and analysis. The processor integrates all condition information from various sensors and makes an on-line diagnostic analysis. From Fig. 1, we can see that the key issues with regard to implementing electrostatic monitoring technology for aero-engine gas paths are sensing technologies, signal-processing technologies, and the intelligent decision model for abnormal particle identification as shown in Fig. 2.

Electrostatic monitoring technology is based on measuring the charged particles in exhaust gases. The charged particles may be ions generated in the combustion chamber by chemoionization, soot particles, or charged particles generated by defects developing in aero-engine components [11]. The chemical content in engine exhaust includes mainly , nitrogen oxide (NO), vapor, hydrocarbon dioxide carbon, carbon monoxide (CO), sulfur oxides, and carbon particles. Normal carbon particles have two clusters with dito . ameter sizes ranging from However, the diameter of abnormal particles caused by faults is usually greater than or equal to 40 microns [12], [13]. Those larger particles may be the result of the abnormal operation of the combustion chamber due to a number of causes such as the fluctuation of the air-fuel ratio, mechanical failures of engine components, or the changed working condition of the engine [7], [14], [15]. The size of carbon particles is a good indicator of engine state. There is no readily available technique to monitor the carbon particle size directly, but there is a known relationship between the size of the particle and the charge of the particle; that charge can be measured on-line. In a high temperature environment, these particles can become charged via the interaction with random ions. The process can be described [16] as (1) where is the charge of the particle, is the diameter of the particle, is the Boltzmann constant, is the temperature, is the electronic charge, is the mean of the particles’ velocity, is the number of concentrations, and is time. Equation (1) shows a positive relationship between and . Generally, the charge of a healthy engine gas path has a charge level that varies with the operation condition of the engine. This charge level can then be used as a baseline reference or threshold, which is a function of the operational condition. In practice, a general representation of engine working condition is the engine speed. However, (1) only shows the theoretical relationship between and other parameters. It cannot be applied in practice because some parameters, such as and , are not measurable on-line. What we can measure in addition to is the engine speed, which is a function of the temperature, particle size, and concentration. In other words, we want to establish a relationship between and the engine speed, so that the state of the engine at a certain engine speed can be monitored by .

WEN et al.: ELECTROSTATIC MONITORING OF GAS PATH DEBRIS FOR AERO-ENGINES

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III. KEY TECHNOLOGIES OF ELECTROSTATIC MONITORING SYSTEMS A. Sensing Technology The electrostatic sensor is the foremost component in the electrostatic monitoring system, and is the fundamental component for acquiring parameters. Its performance directly affects the accuracy of the monitoring system. Due to the high temperature environment in the exhaust pipe, the materials for the electrode and isolation medium must be suitable for working under a high temperature environment. This is the first issue to be considered in the design process. Moreover, to optimize the sensor and measuring circuit design, a model for describing the sensing performance is also required. Materials Selection: For a sensing electrode in an electrostatic sensor, the time length for achieving electrostatic equilibrium determines the response speed to the electrostatic field change. The time length has no relation to the strength of the foreign electrostatic field or the geometric characteristics of the conductor, but does relate to the conductivity of the conductor material [17]. The higher the conductivity of the electrode, the lower the resistance, and the less time the electrode needs to achieve electrostatic equilibrium. That is to say, the material of the electrode should have good conductivity. In contrast, a high resistivity material is used for the isolation medium to prevent static. Due to the extreme work conditions in the aero-engine gas path, both the electrode material and the isolation medium material for the electrostatic sensor need to be able to endure high temperatures. To meet the above requirements, we select nickel alloy [13], and ceramic materials for the electrode, and isolation medium respectively. Sensing Characteristic Analysis: Sensitivity is an important parameter that describes the performance of the sensor. It is known that the sensed charge on the electrode is only related to the position of the inducing charge in the sensing zone, so the sensitivity of the electrostatic sensor can be defined as

Fig. 3. Sensitivity distributions of varying heights.

is the electrical potential; is the body where charge density; is the dielectric permitivity; , , and are the boundaries of the pipe wall, sensor shield cover, and sensor electrode respectively; and denotes that the electrode is an equivalent potential body. . Because it is hard to acquire an analytical solution to (3) by mathematical methods, the Finite Element Method (FEM) is usually employed to solve the Dirichlet problem of the Poisson equation. The data of spatial sensitivities of the sensor can be obtained by changing the point charge position, and then the sensitivity distribution can be acquired by fitting a distribution to the data. Fig. 3 shows the sensitivity distribution with at various heights to the electrode surface. As seen in Fig. 3, the shape of the sensitivity distribution is similar to the Gauss Pulse function. Therefore, we use a Gauss Pulse function to fit the relationship of the sensitivity and radial distance along the moving direction . According to the goodness-of-fit test, and considering computational complexity, the summation of two Gaussian pulse functions is applied to fit the sensitivity: (4)

(2) where is the sensitivity at position , is the sensed is the point charge charge on the surface of the sensor, , is the radial distance from the point charge at position to the sensor surface along the moving direction, and is the vertical height from the point charge to the sensor axis. Here we set the point charge at 1C; thus the sensitivity value is equal to the absolute value of . The sensitivity distribution function is required to optimize the sensor design. For the sake of convenience, a point charge is used here to analyze the induction characteristics of the sensor electrode. According to the electrostatic induction principle, the electric field formed by a point charge can be described by the Poisson equation, and Dirichlet boundary conditions [18]:

where , , , and are the fitting parameters. Based on (4), we can deduce the time-domain frequency response of the electrode as

(5) where is the point charge moving velocity. Usually the electrode has to be amplified. From (5), and the frequency response of the measurement circuit, the frequency response property of the measurement system can be expressed as

(3) (6)

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Based on the results above, we can deduce that the signal induced by more than one particle has a broader frequency spectrum range, and contains more components of high frequency compared with only one particle, which is very useful knowledge for further feature analysis and extraction. B. Signal Processing Technology

Fig. 4. Frequency response comparison of a single particle versus two particles.

where is the equivalent resistance of the measurement circuit. As shown by (6), the frequency response is closely related to the speed at which the particles move. All of the above analysis is only for a single point charged particle passing through the sensing zone of the sensor. However, the sensed signal is often induced by many charged particles at the same time within the sensing zone. Therefore, the sensed signal can be treated as the superposition of sensed charges by all charged particles in the sensing zone of the sensor. Hence, the sensed charge on the surface of the sensor can be expressed by (7) is the total sensed charge on the sensor electrode at where is the sensed charge caused by the ith particle, time , and is the delay time from the 0th particle passing through the sensor to the ith particle passing through the sensor. From the time shift property of the Fourier transform, (7) can be transformed into (8) Here we assume the existence of two charged particles, and , at the same time within the sensing zone. A condition to be satisfied is that the second particle must arrive before the first one leaves the sensing zone. If they do, then (9) where T is the time interval between the two charged particles arriving the sensing zone. From (6) and (9), the frequency property of the output signal of the sensor can be expressed as

(10) Compared with the case of a single charged particle, the signal caused by two continuous particles has a broader spectrum range, and contains more components of high frequency, as shown in Fig. 4, which is based on (10).

To effectively extract features, and to ensure the accuracy of fault classification, de-noising is the primary pre-processing action aimed at obtaining useful information from low-signalnoise-ratio signals. There is no single signal processing technology that is effective for all types of signals. For example, Fast Fourier Transform (FFT) is effective only for narrow band noises. Wavelet analysis needs to select a mother wavelet similar to the signal to be featured in order to achieve a better de-noising effect. Therefore, it is necessary to find a suitable, effective de-noising method for electrostatic monitoring signals. Independent component analysis (ICA) is a well-known multivariate statistic technique. It has been successfully applied in the field of blind source separation [19], [20]. It assumes -dependence among the source signals, and separates the sources from their mixtures by maximizing the -independence among the output signals. In the process of implementing ICA, the number of source signals should be more than the number of -independent output signals. However, in an electrostatic monitoring system, the source signal is usually one-dimensional, so here we employ the Empirical Mode Decomposition (EMD) technique to decompose the original signal into multiple empirical modes (also called Intrinsic Mode Functions (IMF)), with each mode representing a frequency-amplitude modulation in a narrow band. We can obtain certain IMFs and the residual by decomposing the original signal according to the decomposing procedure presented in [21], [22]: (11) is a time series to be analyzed, is the ith IMF where in the decomposed signals, and is the residual extracted from , . the sum of In practical experiments, the sensed signal does not stratify the constraint condition of the mode function, and therefore it is difficult to represent the electrostatic-induced signal using Mode Function (MF) components. As the MF has a good representation ability for sinusoidal signals, signals similar to sinusoid, and high frequency white noises [21], we can use the MF to represent the narrow band interference such as the power frequency interference and its harmonics, and the white noises as well. These are the main interferences mixed into the electrostatic monitoring signals. According to the frequency properties of the mode function and the wave shape, we can select some mode functions from the decomposed components to construct the reference noise signal. After that, we combine the original signal with the reference signal to form the mixed signal matrix, and then adopt the FASTICA algorithm [20] to obtain the separative matrices. This method enables us to separate the noise from the electrostatic signal. The signal processing procedure is


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Fig. 5. De-noising procedure.

Fig. 7. Signals after de-noising processing.

Fig. 6. Original signals.

shown in Fig. 5. The study in this section was carried out using MATLAB codes based on the FASTICA package. To validate the above method, three different signals were used to test the de-noising effect. Signal is generated by simulation, and is the sum of the differential signal of a Gauss pulse signal plus some noise, including white noise, and narrow band noise. Signal b is acquired by seeded particles in a normal temperature environment. Signal c is acquired by seeded particles in a high temperature environment. The three original signals are shown in Fig. 6. Fig. 7 shows the relevant signals after de-noising by the methods proposed earlier. The results clearly show that the de-noising method based on EMD and ICA can effectively remove noise, and extract the useful signal. C. Feature Extraction With the development of electronic detection technologies, weak signal acquisition is no longer an obstacle. However, in practice, it is not easy to identify the gas path component working state according to only the temporal domain characteristics of the monitoring signal. We wish to obtain more useful

features in order to improve the identification effect. A feature is a parameter extracted from measurements. Statistical features, such as statistical parameters, maximum/minimum value, mean, and Root Mean Square (RMS) value, can be used to yse the signal amplitudes. However, we present some specific, useful features in this study for the identification of abnormal conditions based on large-scale simulated experimental data. Activity Level (AL), and Event Rate (ER) are the two parameters that can be extracted from the electrostatic monitoring signal. AL is a measurement of high frequency content, which gives an indication of the amount of fine particulate in the exhaust gas, for example smoke, soot, or other small debris. ER relates to the number of larger particles (measuring approximately 40 microns or more) present in the exhaust gas per unit time [13]. Typically, events include carbon particles and larger debris from faults. The events are separated into positive events, and negative events according to polarity, and amplitude, which provides further information on the condition of the engine [13]. The AL, and ER can be computed as (12) where

is the number of samples in time , and (13)

is the number of samples in for which where . is a constant (typically 3, 5, , 10), and is obtained from a large number of experimental and statistical data. According to [7], the particles in the exhaust gas move in two modes. One is characterized by a continuous flow of charged particles that fill the jet volume without visible discontinuities. Sufficiently charged particles are entrained by the gas-carrier turbulent flow. The other mode is characterized by the formation of discrete charged-particle clusters at distances. From to the results in Section III-A, the signal induced by continuously

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Fig. 8. Signal induced by different types of particles.

flowing particles usually contains more components of high frequencies, so we take the frequency content as one of the significant features of that mode. The wavelet analysis method can decompose the signal into different frequency bands [23]. Here we use the wavelet method to acquire the energy distribution of the signal in different freis the energy of the high-frequency bands. We assume that quency wavelet coefficient series at the ith level.

Fig. 9. AL and ER of the simulated experiment signals. TABLE I ENERGY DISTRIBUTION

(14) is the th component in the high-frequency wavelet where coefficient series at the th level, and is the number of components in the series . We can similarly find the energy of the low-frequency coefficient series. We can also obtain the energy distribution characteristics of the monitoring signal. Summarizing the features above, we can identify the large particles, and small particles according to the parameters such as AL, and ER. We can discriminate carbon particles and metal particles by the polarity of ER (positive ER for metal particles, and negative ER for nonmetal particles, such as carbon), and classify the continuous and discrete modes by energy distribution characteristics for the identification of static or rotary component faults within the engine. In the simulated experiment, we used the oil burner to generate the abnormal carbon particles by adjusting the gas-oil ratio, and seeded Fe particles (size: approximately 75 um) to simulate the abnormal metal particles caused by faults such as tip rub. From the time interval of 20 to 40 seconds in Fig. 8, the sensed signal is caused by the carbon particles through a simulated burning experiment as the description above. Then in the time period of 90 to 110 seconds, the sensed signal is caused by discontinuously seeding Fe particles three times in the simulated experiment. Fig. 9 shows the changing trends of the parameters AL and and ) of the corresponding signal shown in Fig. ER ( increase obviously 8 in detail. We can see that AL and increases in the period from during 20 to 40 seconds, and 90 to 110 seconds. The changes are corresponding to the carbon particles, and Fe particles mentioned above, respectively. Both figures clearly show the difference in terms of polarity, activity level, and event rate for different types of particles. In addition, Table I provides a comparison of the energy distribution characteristics of different modes. In Table I, F1 F4

denote the different frequency bands from high frequency to low frequency. The four frequency bands are decided by the sampling frequency, and the decomposition levels while using the wavelet analysis method [24]. Usually, the background signal is viewed as an induced signal caused by continuous small carbon particles. So, from Table I, we can make three observations. First, the energy of the signal induced by continuous small carbon particles is distributed in every frequency band. Second, the energy of the signal caused by discrete larger carbon or Fe particles mostly distributes in the low frequency band. Third, compared to the energy distribution of the background signal, the energy distribution of the signal induced by the discrete larger particles increases substantially in the low frequency band. This observation implies that, when the energy distributed in the low frequency band increases, it is an indication of abnormal particles in the gas path. D. Abnormal Particle Identification Technology In an electrostatic monitoring system, the identification of an initial fault can be refined by the classification of the particles according to whether they are continuous or discrete, larger or smaller, metal or carbon particles, etc. Because of the complexity of the gas path environment, and numerous influencing factors, intelligent processing methods are helpful for conducting data analysis and reasoning, mining the relationship between features and fault modes, and acquiring knowledge rules. Fig. 10 describes the knowledge acquisition flow based on the rough sets theory, and neural networks proposed in this study. First, the primary features are reduced by the attribute reduction method based on the rough sets theory. Then a structure-adaptive network with a better generalization is constructed based on the genetic algorithm. This network is applied to generate new data sets that contain more potential


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IV. CONCLUSION

Fig. 10. Knowledge acquisition flowchart. TABLE II RESULTS OF SIMULATED EXPERIMENTS

information than the original datasets. The threshold sets and rule sets can be dynamically adjusted with the dataset updating procedure. The rule extraction method based on the rough sets theory can be applied to acquire “if-then” rule sets based on the new datasets [24]. Based on the coverage and the confidence of each rule, the selected “if-then” rules are used to identify the abnormal particle, and then isolate the potential fault. From Fig. 10, we extract rules from the simulated experiment data using three rules. , and , then the status is Rule 1: if normal. , and , then there are Rule 2: if excessive carbon particles. , and , then there are Rule 3: if excessive Fe particles. Ai is an attribute of the monitoring signal, e.g. A1 is the mean; A2 is the RMS; A6 is the energy distribution in some frequency band; and A9 is the Activity Level. As we know, the occurrence of excessive carbon particles is a distinctive characteristic indicating that the combustion efficiency has degraded, and the occurrence of excessive Fe particles is the result of the rub type fault. To verify these rules, we simulated three typical work conditions (normal status, combustion efficiency degradation, and rub type faults) on the simulated experiment platform, then analyzed and calculated the corresponding feature parameters of the electrostatic signals, as Table II shows. From the table, we can see the rules were tested by simulated fault experiments on the simulated experiment platform of an aero-engine gas path. The algorithm has been demonstrated to be able to detect certain simulated faults under laboratory experimental conditions.

The electrostatic monitoring technology for an aero-engine gas path is an advanced on-line monitoring technology that can provide effective warning information for initial faults. The aim of this work is to present an overview of the key technologies for a gas path debris monitoring system based on electrostatic induction, and to provide a guideline or reference for the aviation industry in this research area. In this paper, we propose a systematic framework of electrostatic monitoring technology involving sensing technology, de-noising methods, feature extraction, and a rule-based system for fault identification. The sensitivity distribution is similar to the Gauss Pulse function, and can be fitted well by the summation of two Gaussian pulse functions. The de-noise method based on ICA and EMD can remove the noise mixed in the electrostatic monitoring signal. Primary features such as activity level, event rate, and energy distribution can be applied to differentiate abnormal particles, and the knowledge acquisition mode based on Rough sets theory, and neural network can be used to extract rules from experiment data for further fault identification. They can be used for providing a guideline for further electrostatic monitoring technology research. This research is new; not many published papers have appeared in the literature in this area. Limited military applications have been reported [8]–[10], but only of a descriptive nature. The methodology has the potential to be used by commercial airliners. However, to be able to implement the technology, experiments on the actual gas path environment are required in addition to the sensor design, the amplifier circuit design, and the installation location. Solutions to improving the signal processing speed, extracting more significant features, and integrating the knowledge model within the existing monitoring system also require further developments to explore the full potential of real-time condition monitoring for aero-engines. The experimental study is a key research methodology in this research. Electrostatic monitoring techniques require a large number of tests before actual applications in aero-engines. This research has been partially validated by experiments conducted in the lab in Nanjing University of Aeronautics and Astronautics. Full field testing is being conducted on an engine test stand. With the availability of a large number of experiments in the future, we may obtain more information on the relationships between signal features and various initial fault modes. The technique, if implemented, will improve aircraft safety and reduce operational costs. The technique may also be applied to other types of engines and turbines to ensure their continuous safe operation. ACKNOWLEDGMENT The authors would like to thank Prof. W. Wang for his valuable suggestions. REFERENCES [1] D. A. Clifton, “Condition monitoring of gas-turbine engines,” Transfer Report Department of Engineering Science, University of Oxford, 2006. [2] J. M. Barragan and M. Munich, “Engine vibration monitoring and diagnosis based on on-board captured data,” MTU Aero Engines GMBH Munich (Germany) 2003.

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[3] M. Wito and R. Szczepanik, “Turbine engine health/maintenance status monitoring with use of phase-discrete method of blade vibration monitoring,” Solid State Phenomena., vol. 147, pp. 530–541, 2009. [4] L. C. Jaw, “Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step,” in Proceedings of Turbo Expo, 2005. [5] A. B. Vatazhin, V. I. Grabovskii, and V. A. Likhter et al., “Electrical fluctuations in turbulent electrogasdynamic flows,” Fluid Dynamics., vol. 12, no. 2, pp. 285–295, 1977. [6] A. B. Vatazhin, M. Rushailo, and , “An electrostatic probe for determining particle characteristics in disperses flow Turbulent jet flows,” Turbulent jet flows with condensation and electro physical effects Moscow, Tsentral’nyi Institut Aviatsionnogo Motorostroeniia, 1991, pp. 22–47. [7] A. V. Vatazhin, D. A. Golentsov, and V. A. Likhter et al., “Noncontact electrostatic engine diagnostics: Theoretical and laboratory simulation,” Fluid Dynamics, vol. 32, no. 21997, pp. 223–232. [8] H. E. G. Powrie and C. E. Fisher, “Engine health monitoring towards total prognostics,” in IEEE Aerospace Applications Conference Proceedings, CA, USA, 1999, vol. 3, pp. 11–20, IEEE. [9] A. Novis and H. Powrie, “PHM sensor Implementation in the real world-a status report,” in Proceedings of IEEE Aerospace Conference, Montana, USA, 2006, vol. 3, pp. 1–9. [10] H. Powrie and A. Novis, “Gas path debris monitoring for F-35 joint strike fighter propulsion system PHM,” in Proceedings of IEEE Aerospace Conference, Montana, USA, 2006, vol. 2, pp. 1–8. [11] J. Lawton and F. J. Weinberg, Electrical Aspects of Combustion. Oxford: Clarendon Press, 1969. [12] A. Sorokin and F. Arnold, “Electrically charged small soot particles in the exhaust of an aircraft gas-turbine engine combustor: Comparison of model and experiment,” Atmospheric Environment, vol. 38, no. 17, pp. 2611–2618, 2004, F.. [13] H. E. G. Powrie and K. Mcnicholas, “Gas path condition monitoring during accelerated mission testing of a demonstrator engine,” presented at the The 33rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Seattle, WA, USA, 1997, AIAA, 2904, unpublished. [14] A. Vatazhin, A. Lebedev, and V. Likhter et al., “Turbulent air-steam jets with a condensed dispersed phase: Theory, experiment, numerical modeling,” Journal of Aerosol Science, vol. 26, no. 1, pp. 71–93, 1995. [15] A. B. Vatazhin, D. A. Golentsov, and V. A. Likhter et al., “Aircraft engine state nonobstructive electrostatic monitoring theoretical and laboratory modelling,” Journal of Electrostiacs, vol. 40, no. 41, pp. 711–716, 1997. [16] J. Ren, J. Shen, and S. C. Lu, Particle dispersion Science and Technology (in (in Chinese)). Beijing: Chemical Industry Press, 2005. [17] Z. S. Zhang, “The time to reach electrostatic equilibrium of a conductor,” Physics and Engineering, vol. 12, no. 2, pp. 20–22, 2003. [18] W. R. Smythe, Static and Dynamic Electricity. New York: McGrawHill, 1968. [19] A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, 1997. [20] A. Hyvärinen and E. Oja, “Independent component analysis: Algorithms and applications,” Neural Networks, vol. 13, no. 4, pp. 411–430, 2000. [21] N. E. Huang, Z. Shen, and S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” in Proceedings: Mathematical, Physical and Engineering Sciences, 1998, vol. 454, no. 1971, pp. 903–995.

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Zhenhua Wen is currently a Lecturer in the school of Mechatronics Engineering at Zhengzhou Institute of Aeronautical Industry Management. He was a Senior Research Associate of City University Centre for Prognostics and System Health Management at the City University of Hong Kong from 2009 to 2010. He received an M.S. degree in Traffic Information Engineering and Control and a Ph.D. degree in Vehicle Operation Engineering, both from Nanjing University of Aeronautics and Astronautics, China, in 2006 and 2009, respectively. He has a wide interest in Condition Monitoring, Fault Diagnosis, and Condition-Based Maintenance for aero-engines, including the fusion of multiple data sets, signal processing, and knowledge acquisition.

Hongfu Zuo has a Ph.D. in Mechanical Engineering from China University of Mining and Technology. He is currently Executive Vice Dean of the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, in China. His research interests include condition monitoring, fault diagnosis, reliability engineering, and logistic.

Michael G. Pecht (S’78–M’83–SM’90–F’92) is currently a Visiting Professor in Electronic Engineering at City University in Hong Kong. He has an MS in Electrical Engineering, and an MS and Ph.D. in Engineering Mechanics from the University of Wisconsin at Madison. He is a Professional Engineer, an IEEE Fellow, an ASME Fellow, an SAE Fellow and an IMAPS Fellow. He was awarded the highest reliability honor, the IEEE Reliability Society’s Lifetime Achievement Award in 2008. He has previously received the European Micro and Nano-Reliability Award for outstanding contributions to reliability research, 3M Research Award for electronics packaging, and the IMAPS William D. Ashman Memorial Achievement Award for his contributions in electronics reliability analysis. He served as chief editor of the IEEE TRANS. RELIABILITY for eight years and on the advisory board of IEEE Spectrum. He is Chief Editor for Microelectronics Reliability and an Associate Editor for the IEEE TRANS. COMPONENTS AND PACKAGING TECHNOLOGY. He is the founder of CALCE (Center for Advanced Life Cycle Engineering) at the University of Maryland, which is funded by over 150 of the world’s leading electronics companies at more than USdollar 6 M/year. He is also a Chair Professor in Mechanical Engineering and a Professor in Applied Mathematics at the University of Maryland. He has written more than twenty books on electronic products development and use, and supply chain management, as well as over 400 technical articles. He consults for 22 major international electronics companies, providing expertise in strategic planning, design, test, prognostics, IP, and risk assessment of electronic products and systems.