Data-driven Fault Diagnosis in a Hybrid Electric Vehicle ...

Data-driven Fault Diagnosis in a Hybrid Electric Vehicle Regenerative Braking System Chaitanya Sankavaram1, Bharath Pattipati1, Krishna Pattipati1, Yilu Zhang2, Mark Howell2, and Mutasim Salman2 1 Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Road, U-2157, Storrs, CT 06269, USA 2 GM R&D, General Motors Company, 30500 Mound Rd, Warren, MI 48090, USA {chaitanya, krishna}@engr.uconn.edu sensed measurements and the outputs of a mathematical model. The expectation is that inconsistencies are large in the presence of malfunctions and small in the presence of normal disturbances, noise and modeling errors. Two main methods of generating the consistency checks are based on observers (e.g., Kalman filters, reduced-order unknown input observers, interacting multiple models, particle filters) and parity relations (dynamic consistency checks among measured variables stemming from hardware or information redundancy relations). A data-driven approach is preferred when the system monitoring data for nominal and degraded conditions is available. Neural network and statistical classification methods are illustrative of data-driven techniques. The knowledge-based approach uses graphical models such as dependency graphs (digraphs), Petri nets, multi-signal (multi-functional) flow graphs, and Bayesian networks for diagnostic knowledge representation and inference [3][4][5].

Abstract— Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. In this paper, we discuss a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The process involves data reduction techniques, exemplified by multi-way partial least squares, multi-way principal component analysis, for implementation in memoryconstrained electronic control units and well-known fault classification techniques based on reduced data, such as support vector machines, k-nearest neighbor, partial least squares, principal component analysis and probabilistic neural network, to isolate faults in the braking system. The results demonstrate that highly accurate fault diagnosis is possible with the pattern recognition-based techniques. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems. TABLE OF CONTENTS

In this paper, we develop a systematic data-driven fault detection and diagnosis (FDD) process for diagnosing faults in a regenerative braking system. For FDD analysis, Powertrain System Analysis Toolkit (PSAT) [6], a vehicle simulation software tool, was used to create a Matlab/Simulink® model of RBS with series-parallel drivetrain configuration. The fault diagnosis process involves data reduction techniques, such as multi-way partial least squares, multi-way principal component analysis [7],

1. INTRODUCTION.................................................................1 2. MODELING OF REGENERATIVE BRAKING SYSTEM ........2 3. FAULT UNIVERSE AND MONITORED SIGNALS ................3 4. FAULT DETECTION AND DIAGNOSIS PROCESS ................4 5. EXPERIMENTAL RESULTS ................................................8 6. CONCLUSIONS ..................................................................9 ACKNOWLEDGMENTS ..........................................................9 REFERENCES ........................................................................9 BIOGRAPHIES .....................................................................10

1. INTRODUCTION Hybrid electric vehicles (HEVs) employ regenerative braking to improve fuel economy, enable energy regeneration and provide environmental protection [1]. The primary function of a regenerative braking system (RBS) is to convert kinetic energy into electrical energy and store it in batteries during braking mode for later use in propelling the vehicle (see Fig 1) [2]. Failures in a regenerative braking system may significantly degrade the performance and efficiency of vehicles. Hence, an intelligent diagnostic process is crucial to quickly detect and isolate faults in order to aid in vehicle health monitoring and, consequently, enhance the reliability of vehicular systems. Diagnostic methods have mainly evolved upon three major paradigms, viz., physics-based modeling, data-driven and knowledge-based approaches. The physics-based modeling approach employs consistency checks between the 978-1-4577-0557-1/12/$26.00 ©2012 IEEE

Fig. 1: Regenerative Braking System - Energy Flow Diagram in HEVs 1

Fig. 2: Regenerative Braking System Matlab/Simulink® model from PSAT and well-known fault classification techniques based on reduced data, exemplified by support vector machines (SVM), k-nearest neighbor (KNN), partial least squares (PLS), principal component analysis (PCA) and probabilistic neural network (PNN) to isolate faults in the braking system [8][9]. The current electronic control units (ECUs) for control and diagnosis have 1-2 MB of memory and 24-50 MHz of processor speed (due to cost constraints); therefore, intelligent data reduction techniques are crucial for effective fault diagnosis in automotive systems. Although the fault detection and diagnosis approach presented in this paper is targeted at automotive regenerative braking systems, it has direct applications to aerospace, building and other cyber-physical systems.

2. MODELING OF REGENERATIVE BRAKING SYSTEM We modeled the regenerative braking system with a series-parallel drivetrain [10] configuration using PSAT software (see Fig 2). The details of the vehicle drivetrain components are given in Table I. The RBS model consists of a driver model, powertrain controller, component controller, and the powertrain model. The powertrain controller is the supervisory controller making the high-level decisions that affect the general state of the powertrain (e.g. engine on/off), the operating mode of the vehicle (e.g. propelling, regenerative braking etc.), and accordingly deliver the torque requests to the component controller. Subsequently, the torque requests are converted into component commands at the component controller. These commands are treated as the actuator commands by the individual components in the powertrain model to achieve the requested torque and consequently, report the system status (e.g., engine speed, battery state of charge) to the supervisory controller. The models are described briefly in the following subsections [11]. Mathematical details will be presented in [12].

Organization of the paper The paper is organized as follows. In Section 2, we briefly present the nominal model of Regenerative Braking System (RBS). Section 3 describes the fault universe and the monitored variables in the RBS model. Section 4 presents the data-driven FDD process to detect and isolate faults in the RBS. Section 5 describes the experimental results and finally, Section 6 concludes the paper with a summary. 2

Fig. 3: Vehicle Powertrain Model with Series-Parallel Configuration Driver Model:

Table I. Series-Parallel Drivetrain Configuration

The driver model simulates the drive cycles (e.g., Urban Dynamometer Driving Schedule (UDDS) [13]), by setting accelerator and brake pedal positions to achieve the desired vehicle speed. The output from this block is the driver’s torque demand at the wheels, which constitutes the input to the powertrain controller (PTC).

COMPONENT Motor1

Motor2

Powertrain Controller: The key functionality of the PTC is to send commands to the component controller so that the desired torque demands are met. The PTC has the control strategies for (a) propelling, when torque at the wheels is positive, (b) shifting, i.e., gear selection strategy, and (c) braking, when torque at the wheels is negative. In addition, PTC also computes the physical limits for each of the components, for instance, the maximum available torque for the engine and the maximum propelling torque for the generator.

Engine

Energy Storage

Gearbox Wheel Axle Vehicle

Component Controller: In the component controller, the desired torque demands from the PTC are converted into component commands, e.g., the desired torque from the engine is converted into percentage throttle command. These commands are processed by the component blocks in the powertrain model.

SPECIFICATIONS Permanent Magnet (PM) Electric Motor with Continuous Power = 25KW Peak Power = 50 KW UQM Power-phase PM motor Continuous Power = 55KW Peak Power = 100 KW Gasoline Engine 1.497 liter 57 KW SAFT Li-ion Battery Capacity = 6 Ah Number of cells = 75 2 Gear Manual Transmission Gear ratios = 1.86 and 1 2 Wheel drive Vehicle body mass = 800 Kg Frontal area = 1.8 m2 Drag coefficient = 0.38

3. FAULT UNIVERSE AND MONITORED SIGNALS The RBS model is subjected to 12 sensor and parametric faults listed in Table II. There are 25 signals that are monitored in the braking system including (a) sensor signals, such as temperature, speed, and current measurements from the hardware components in the powertrain model; (b) motor, wheel, and engine torque demands sent from the powertrain controller to the component controller; and (c) component commands sent from the component controller to the hardware components in the powertrain model. Table III shows the list of

Powertrain Model: This model comprises all the components that mimic the behavior of hardware components, such as the engine, the battery and the motor. Fig 3 shows the individual component blocks in the powertrain model (with a seriesparallel drivetrain configuration).

3

monitored signals in the Regenerative Braking System.

F7 F8 F9 F10 F11 F12

The sensor and parametric faults are injected via simulation-based fault injection experiments. The most common faults in the sensors are additive faults; these are the discrepancies between the measured and the true values of the monitored signals. Therefore, the sensor faults (F1, F2, and F4-F9) are simulated as additive biases on the measured signals. For instance, a 10% increase in the engine speed is used to model the engine speed sensor fault. Similarly, the parametric faults F3 and F10 are simulated as 10% increase in their nominal values, whereas 10% decrease in the wheel radius is considered as wheel radius fault (F12). Mathematically, the fault scenarios are simulated using the following equation,

X faulty  X nominal (1  ).

Table III. List of Monitored Signals in RBS

(1)

In (1), Xfaulty is the parameter value under faulty condition, Xnominal is the nominal parameter value and Δ is the percent change in the parameter value (fault severity). Our approach is generic and does not preclude more sophisticated fault models (e.g., coupling faults, multiplicative faults, stuck with constant bias fault, abrupt/incipient faults, persistent/intermittent faults and so on [14]). The simulations are conducted for 1400 seconds and the data is collected every 0.1 seconds. Hence, there are a total of 14001 data points for each nominal and faulty case. The data is arranged in the form of a tensor as

X R

Motor2 Current Sensor Fault Motor2 Speed Sensor Fault Vehicle Speed Sensor Fault Wheel Inertia Fault Battery SOC Fault Wheel Radius Fault

I  J K

where I, J and K are the nominal and fault cases, measured (monitored) signals, and time samples, respectively (see Fig 4).

Monitored Signal S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

Battery SOC Motor 2 Torque Demand Wheel Torque Demand Motor 1 Torque Demand Engine Torque Demand Battery Temperature Battery Current Driver Torque Demand Motor1 Command Gearbox Speed Wheel Input Speed Wheel Output Speed

S13

Wheel Torque

S14

Vehicle Linear Speed

S15

Motor1 Speed

S16

Motor1 Current

S17

Clutch Speed

S18

Engine Command

S19

Motor2 Command

S20

Motor2 Speed

S21

Motor2 Current

S22

Engine Speed

S23

Clutch Speed

S24

Mechanical Accessory Torque

S25

Wheel Command

Signal Description

4. FAULT DETECTION AND DIAGNOSIS PROCESS Fig. 4: Three Dimensional Data Arrangement

The data-driven FDD process for the RBS system consists of an offline training phase and an online testing phase, as shown in Fig. 5. The process primarily involves three major steps: data reduction, fault detection and fault classification.

Table II. Fault List for Regenerative Braking System FAULT F1 F2 F3 F4 F5 F6

FAULT DESCRIPTION Battery Current Sensor Fault Battery Temperature Sensor Fault Clutch Inertia Fault Engine Speed Sensor Fault Motor1 Current Sensor Fault Motor1 Speed Sensor Fault

In the off-line training phase, data reduction techniques are employed on the residuals (deviation of actual measurements from the expected ones) from different fault scenarios; and the reduced data is then used to train the fault 4

Fig. 5: Fault Detection and Diagnosis Process for Regenerative Braking System classifiers (SVM, KNN, PLS, PCA, and PNN). In the online testing phase, the trained classifiers are used to classify the faults based on reduced data. The following subsections describe briefly the steps involved in the FDD process.

preserves the correlation structures among the monitored variables. The PCA is optimal in terms of capturing the variation in data. The MPLS is another dimensionality reduction technique that considers both pattern (independent data) and class (response) information. MPLS technique is widely used for its ability to enhance classification accuracy on high-dimensional datasets, and its computational efficiency. The reduced data can be processed with classifiers for categorizing the various fault classes. Furthermore, both MPLS and MPCA techniques can be used for fault detection. For example, in the MPLS technique, if the distance of the score vectors is close to the origin of the new reduced feature space, then the system is judged to be in the nominal state. The distance to these score vectors from the origin is computed via Hotelling statistic [7]. In this paper, we employed the MPLS technique on the residuals primarily for data reduction (from 559 MB to 12 KB).

Data Reduction Techniques Data reduction techniques are of significant importance in real world applications to overcome the issue with highdimensional datasets (due to multiple modes of system operation and sensor data collected over time) [15][16]. Often, all the measurements are not salient for understanding the essential phenomena of interest, and features extracted using the data reduction techniques enable real-time implementation of data-driven diagnostic algorithms via compact memory footprint, improved computational efficiency and generally enhanced diagnostic accuracy [17]. Statistical data reduction techniques, such as multi-way principal component analysis (MPCA) and multi-way partial least squares (MPLS), are among the widely investigated data reduction techniques [17][18]. These techniques reduce the dimensionality of datasets by transforming the data to a lower-dimensional feature space, and this reduced space often gives information about the salient structure of the high-dimensional data space. In other words, a number of correlated variables in the original data are transformed into a smaller number of uncorrelated new variables in the reduced space, called principal components or the score vectors. These reduced variables capture good amount of variation in the data. The MPCA is used to reduce the dimensionality of data, and produces a representation that

Fault detection Techniques Fault detection is performed by monitoring the amount/rate of deviation of a parameter from its nominal value, also known as residuals. We employ cumulative sum (CUSUM) test [19], and simple thresholds for the detection of faults. Here, the residuals of monitored variables are computed at each measurement time, and the cumulative sum of squared residuals is calculated. Let Sik be the cumulative sum of squared residual of monitored signal i at time k, mathematically written as,

5

highest a posteriori class probability P(ci|xnew).

k

Sik   rij 2

(2)

Principal Component Analysis—PCA is a multivariate statistical procedure that transforms the training data into a lower-dimensional space by transforming a number of correlated variables into a smaller number of uncorrelated new variables called principal components. These components represent the selection of a new coordinate system obtained by rotating the original variables and projecting them onto the reduced space defined by the first few principal components. Here, the first principal component describes the largest amount of variation in the data, the second one the second largest amount of variation in the data and so on. Each principal component is represented as a linear combination of the columns (J), and has a specific numerical value for each of the rows (I). In matrix form, the PCA model can be written as,

j 1

where rij is the residual of the signal i at jth time instant, and, is the measurement difference between the actual sensor output and nominal no-fault output. In the no-fault case, the expected value of rij is zero, whereas in the presence of a fault, the rij does not have zero mean. Hence, a simple threshold test on the cumulative sum of the squared residuals can determine whether the system is in a faulty state or nominal state i.e., if Sik exceeds a threshold T then a fault is detected (see equation (3)).

Sik  T  fault detected Sik  T  no fault detected

(3)

Fault Classification Statistical and pattern classification techniques such as support vector machines, k-nearest neighbors, principal component analysis, partial least squares, and probabilistic neural networks are employed for isolating faults in the RBS model. A brief explanation of these techniques is given in the following subsections.

X s  i, j  

pf  E

(5)

Partial Least Squares—PLS is a regression technique used to find a set of components that explain the covariance between the independent training data matrix (sensor data) X, and the dependent matrix (fault classes) Y. The goal of the PLS algorithm is to determine highly correlated latent variables (or the score vectors) that not only capture the variations in the input data X, but also the variations that are most predictive of the output data Y. Once the latent variables are extracted, a least squares regression is performed to estimate the fault class. The score vectors are determined using nonlinear iterative partial least squares (NIPALS) algorithm [21].

k - Nearest Neighbor—The KNN classifier is a simple nonparametric method that classifies test vectors based on the samples from the training data [8]. The classifier finds the knearest points to a test vector from the training data, and the class with the maximum a posteriori probability within those k points is declared as the most-likely class. Normally, k is chosen as an odd number to avoid ties. Mathematically, the posterior class probabilities P(ci|xnew) are given by,

ki p  ci  k

f 1

T

f

where L is the number of principal components. The loading vectors (pf) are orthonormal and provide the directions with maximum variability. The score vectors (tf) from the different principal components are the coordinates for the objects in the reduced space. A classification of a new test pattern is done by obtaining its predicted scores and residuals. If the test pattern is similar to a specific class in the training network, the scores will be located near the origin of the reduced space, and the residual should be small. The distance of test data from the origin of the reduced space can be measured by Hotelling statistic [7].

Support Vector Machine—Support vector machine is one of the most widely used supervised learning algorithms for classification. Given a set of training samples belonging to different classes, support vector machines find an optimal decision boundary (also called hyperplane) that maximizes the margin between the classes [20]. Therefore, SVM is also known as the maximum-margin classifier. The vectors that define the optimal separating hyperplane are known as the support vectors. These support vectors lie closest to the decision surface and can be used to estimate the fault class of a test feature vector. In the case of non-linear classification, where a linear boundary is not appropriate, a kernel function is used for mapping the data onto a higher dimensional space and the optimal hyperplane is then constructed. Some of the kernel functions that are commonly used are polynomial, Gaussian, radial basis functions, and hyperbolic tangent.

P  ci | x new  

L

t

Probabilistic Neural Network—PNN is a supervised method that computes the likelihood of an input vector belonging to a specific class based on the learned probability distributions of each class. The learned patterns can also be weighted with a priori probability (relative frequency) of each category and misclassification costs to determine the most likely class for a given input vector. If the relative frequency of the categories is unknown, then all the categories can be assumed to be equally likely and the determination of category is solely based on the closeness of the input feature vector to the distribution function of a class [8].

(4)

where ki is the number of vectors belonging to class ci within the k-nearest points. P(ci) is the prior probability of class ci. A new test sample xnew is assigned to the class ci with the 6

Fig. 6: Driver Profile (Profile 1)

Fig. 7: Driver Profile (Profile 2)

Fig. 8: Battery SOC with and without Noise (Profile 1)

Fig. 9: Cumulative Sum of Squared Residuals from 25 Monitored Signals for Battery Current Sensor Fault (F1) 7

Fig. 10: Cumulative Sum of Squared Residuals from Battery SOC Signal (S1) for all the Faults Table IV. Classification Accuracy of each Classifier for each Fault Classifier

SVM

Fault F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12

100 100 100 100 100 100 100 100 100 100 100 100

KNN (k=1) 100 100 100 100 100 100 100 100 100 100 100 100

KNN (k=2) 100 100 100 100 100 100 100 100 100 100 100 100

5. EXPERIMENTAL RESULTS

KNN (k=3) 100 100 100 100 100 100 100 100 100 100 100 100

PLS

PCA

PNN

100 100 83.33 100 100 100 100 100 100 100 100 50

100 100 100 100 100 100 100 100 100 100 100 100

100 100 100 100 100 83.33 83.33 100 100 100 100 100

defined and the cumulative sum of the squared residual is compared against this threshold to determine whether the system is in faulty state or in the nominal state. Fig. 9 shows the cumulative sum of the squared residuals for battery current sensor fault. Here, x-axis represents the sample number and y-axis represents the cumulative sum of squared residuals from 25 monitored signals. A suitable threshold on the residuals detects the presence of fault(s), for instance, a threshold of 500 on signal S22 indicates the presence of a fault at t = 89.4 seconds. Similarly, a threshold of 5 on S1 detects F11, the battery SOC fault (see Fig 10). Once a fault is detected, data reduction techniques and classification techniques can be employed to isolate the fault.

The RBS model was simulated under nominal and 12 faulty scenarios with two driver profiles: Profile1 and Profile2 shown in Figures 6 and 7. These driver profiles (drive cycles) are collected from a Chevy 2007 model year vehicle and acts as input to the driver block of the RBS model. The duration of the driver profile is 1400 seconds and the data is sampled every 0.1 seconds. We monitored 25 signals for each fault case; hence, the total data collected from the fault simulations is 13x25x14001 for each profile. A Gaussian measurement noise with random seed is added to the signals with a variance of 0.6% of the squares of the magnitude of the signals (this corresponds to a signal-tonoise (SNR) ratio of 22.2dB). Fig 8 is a snapshot of a sample signal with and without noise added to the signal.

Data Reduction and Fault Classification Results MPLS technique is employed to reduce the data into fewer components. Here, the data is reduced to 10 score vectors (559 MB → 12 KB) and this reduced data is used for fault isolation. As mentioned earlier, the classifiers SVM, KNN, PLS, PCA and PNN are employed to isolate

Fault Detection Here, CUSUM test is employed for fault detection. For CUSUM, the cumulative sum of the squared residuals for the 25 monitored variables is collected. A threshold is 8

the sensor and parametric faults. Here, the number of patterns available is 156 (26 patterns from Profiles 1 and 2 and 130 patterns by adding noise to the measured signals). This data is divided into two equal subsets, i.e., 50% for the training of classification algorithms and the remaining 50% of the data is used for validating the trained classification algorithms. Table IV presents the individual classification accuracies of each classifier for each fault. It is seen that SVM, KNN and PCA performed best by achieving 100% accuracy on each fault. Table V summarizes the overall classification accuracy achieved by each of the individual classifiers. Here, all the classifiers performed well with greater than 97% accuracy, except for PLS. The sample of faults and signals considered in this paper are to test the viability of the presented approach. It is necessary to explore more realistic scenarios, such as different driving conditions, road scenarios, a variety of fault types, etc. and validate the proposed approach. However, the preliminary experimental results are very promising.

classification accuracy at a reduced computational load/ time. In future, we plan to investigate the robustness of the FDD scheme with varying driver profiles. In addition, we plan to model the RBS with multiple ECUs and CAN bus communication, and investigate the application of our FDD scheme for isolating software and communication-related faults. Finally, we will also predict the degradation of RBS parameters and estimate its remaining useful life.

The systematic FDD process presented in this paper featuring fault detection, multivariate statistical data reduction and fault classification techniques is a generic approach for fault analysis and can be easily applied to any real-world engineering system. Indeed, variants of this process have been applied to a number of engineering systems [17][18] [22]-[27].

REFERENCES

ACKNOWLEDGMENTS The work reported in this paper was supported by National science foundation (NSF) under grants ECCS0931956 (NSF CPS) and ECCS-1001445 (NSF GOALI). We thank GM R&D and NSF for their support of this work. Any opinions expressed in this paper are solely those of the authors and do not represent those of the sponsor.

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Table V. Overall Classification Accuracy Classification Algorithm SVM

Classification Accuracy (%) 100

KNN (k=1)

100

KNN (k=2)

100

KNN (k=3)

100

PLS PCA PNN

94.87 100 97.44

6. CONCLUSIONS In this paper, a systematic data-driven fault detection and diagnosis approach for an automotive regenerative braking system is presented. The RBS system is modeled using PSAT software and simulated under nominal and faulty scenarios. We employed MPLS technique for data reduction, and statistical and pattern recognition techniques for fault isolation. The approach presented here demonstrated high diagnostic accuracy (100% fault classification accuracy with SVM, KNN and PCA classifiers) and can be used for fault analysis in any vehicle systems. It has the potential for real-time implementation in automotive and aerospace systems due to the significant reduction of the data size without compromising on the fault 9

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of multiple classifier systems,‖ ASME Turbo Expo 2008: Power for Land, Sea and Air, Berlin, Germany, June 2008. [26] A. Kodali, S. Vemana, K. Choi, K. R. Pattipati, ―Diagnostic ambiguity and parameter optimization in classifier fusion: Application to gas turbine engine data,‖ IEEE Autotestcon Conference, Salt Lake City, Utah, pp. 433-438, September 2008. [27] R. Ghimire, C. Sankavaram, A. Ghahari, K. Pattipati, Y. Ghoneim, M. Howell, and M. Salman, ―Integrated Model-based and Data-driven Fault Detection and Diagnosis Approach for an Automotive Electric Power Steering System‖, IEEE Autotestcon Conference, Baltimore, MD, September 2011.

BIOGRAPHIES Chaitanya Sankavaram received the B.Tech. degree in Electrical and Electronics Engineering from Sri Venkateswara University, Tirupathi, India, in 2005. She is currently working toward the Ph.D. degree in Electrical and Computer Engineering at the University of Connecticut, Storrs. She was a Project Engineer with Wipro Technologies, Bangalore, India, for two years. Her current research interests include fault diagnosis and prognosis, reliability analysis, data mining, pattern recognition, and optimization theory. Bharath Pattipati received the B.E. degree in Electrical and Electronics engineering from M.S. Ramaiah Institute of Technology, Bangalore, India, in 2005 and the Master’s degree in Electrical and Computer Engineering from the University of Connecticut, Storrs, in 2009, where he is currently working toward the Ph.D. degree in Electrical and Computer Engineering. His current research interests include the application of systems theory and optimization techniques to complex largescale systems, application-driven analysis of neural networks, pattern recognition, and fault diagnosis and prognosis. Krishna R. Pattipati (S’77-M’80-SM’91-F’95) received the B.Tech. degree in Electrical Engineering with highest honors from the Indian Institute of Technology, Kharagpur, India, in 1975 and the M.S. and Ph.D. degrees in Systems Engineering from the University of Connecticut, Storrs, in 1977 and 1980, respectively. From 1980 to 1986, he was with ALPHATECH, Inc., Burlington, MA. Since 1986, he has been with the University of Connecticut, where he is currently the UTC Professor in Systems Engineering in the Electrical and Computer Engineering Department. He was a Consultant to Alphatech, Inc., Aptima, Inc., and IBM Research and Development. He is a Co-Founder of Qualtech Systems, Inc., which is a small business specializing in intelligent diagnostic software tools. His research interests include the areas of adaptive organizations for dynamic and 10

uncertain environments, multiuser detection in wireless communications, signal processing and diagnosis techniques for complex system monitoring, and multi object tracking. He is a Fellow of IEEE. Yilu Zhang received his B.S., and M.S. degrees in Electrical Engineering from Zhejiang University, China, in 1994, and 1997, respectively; and his Ph.D. degree in Computer Science from Michigan State University in 2002. He joined the R&D center of General Motors Co. at Warren, Michigan in 2002, and currently holds a position of Senior Researcher. Dr. Zhang’s research interests include integrated system health management, human machine interactions, statistical pattern recognition, machine learning, speech/image processing, and their applications. Dr. Zhang is a Senior Member of IEEE, and a member of SAE. He is a winner of 2008 “Boss” Kettering Award – the highest technology award in GM – for his contribution to Connected Vehicle Battery Monitor, a remote vehicle diagnostics technology. Mark Howell is a Staff Researcher with the Electrical and Controls Lab at General Motors Research, specializing in Fault Tolerant Vehicle Control and Prognostic Systems. He previously worked as a Research Fellow in Automotive Engineering at Loughborough University, pursuing research on intelligent active suspension control and integrated chassis control systems. He has a MSc and PhD in Control Systems from the University of Sheffield, England, and a Bachelors degree in Cybernetics from the University of Reading, England. Mutasim Salman received his bachelor’s degree in Electrical Engineering from University of Texas at Austin; M.S. and PhD in Electrical Engineering with specialization in Systems and control from University of Illinois at Urbana- Champaign. He also has an Executive MBA. He holds sixteen patents and has coauthored more than 36 refereed technical publications and a book. He joined the GM R&D staff in 1984. He is currently a group manager and a Technical Fellow. He is a senior member of IEEE. His research interest includes performance monitoring, diagnosis and prognosis as well as fault tolerant control as applied to Energy and Automotive Systems.

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