Fault Diagnosis and Prognosis in a Network of Embedded Systems in Automotive Vehicles

Abstract

The relentless competition among automotive companies and increasing demands from customers for driver assistance functions and dynamically-controlled safety systems in vehicles are creating mounting time-to-market pressures and, consequently, shortened development times. With the increased vehicle complexity and shortened development times, guaranteeing hardware-software integrity and, hence, vehicle performance has become a salient issue. This is because poor vehicle performance increases warranty costs to automotive manufacturers and maintenance costs to customers; these, in turn, result in customer dissatisfaction and in reduced competitiveness of US automotive industry. GM has identified the development of methods to enhance the reliability of embedded systems (i.e., hardware, software and interfaces) designed and implemented by different suppliers, and vehicle diagnostics and prognostics as key research challenges. The objectives of this collaborative project between the University of Connecticut (UConn) and the GM Research and Development R&D) are two-fold: (i) Develop an integrated diagnostic and prognostic (D&P) modeling framework for designing high-integrity heterogeneous network of embedded systems so that (hardware, software and interface) faults can be detected and isolated rapidly, and (ii) Investigate on-line and off-line inference methods for diagnosing faults and for predicting the remaining useful life of (hardware) components based on the inferred failing components and the various tracked paths of degradation. The latter methods can be embedded in the vehicle or accessed over a network. To achieve the objectives, our high-level approach is to identify the potential failure modes associated with software, hardware and HW/SW interfaces, develop functional dependency models between the failure modes of a network of embedded systems and the concomitant monitoring mechanisms (called "tests"), perform testability analysis, generate model-based failure modes, effects and criticality analysis (FMECA), and validate the diagnostic and prognostic inference methods via fault injection prior to deployment in the field.

The first objective is addressed by formulating an integrated D&P process for fault diagnosis and prognosis of embedded systems that combines data-driven techniques, graph-based system-level functional dependency models and mathematical/physical models (for components with well-understood dynamics). A regenerative braking system (RBS) in hybrid electric vehicles is selected as the target system for experimentation with the proposed D&P framework because of its complexity, and the need for the interaction among multiple subsystems, electronic control units (ECUs) and the controller area network (CAN) bus. The Simulink^(r)-based RBS model was developed using Powertrain System Analysis Toolkit (PSAT), a vehicle simulation software tool. In order to emulate a real automotive network of embedded systems, the RBS model is segregated into multiple ECUs and hardware components. The ensuing model consists of a driver model, a physical system model and six ECUs, namely battery control unit, engine ECU, motor1 control unit, motor2 control unit, mechanical brake control unit, and powertrain controller. The communication between the ECUs is carried out via signals on a simulated CAN bus using the Vector CANoe software (ECU network development, testing, and analysis tool). A variety of physical system faults (parametric and sensor-related faults), software logic faults, network communication faults (babbling idiot, missing message, burst loss and outdated message faults) and bus faults were injected into the system via simulation-based fault injection experiments. A data-driven approach is applied for fault detection and diagnosis and the results demonstrated that the faults can be isolated with a reasonably good accuracy (>97% correct fault isolation).

The second objective is addressed by designing inference algorithms suitable for coupled systems with delays. We have made four novel contributions. The first contribution addressed the problem of diagnosing coupled faults (coupled fault diagnosis) in complex cyber-physical systems, such as automotive systems where failure of one component (either hardware or software) may probabilistically trigger the malfunctioning of another component. The formulation involves a mixed memory Markov model within a factorial hidden Markov modeling formalism to represent coupled fault dependencies. The second contribution formulated and solved the dynamic set covering (DSC) problem, a series of set-covering problems that are coupled over time, using Lagrangian relaxation and a Viterbi decoder-based algorithm. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of faults that explains the observed test outcomes over time. The novel feature of the DSC problem is that the test outcomes may be observed with time delays induced by network transmission and sensor processing delays. The third contribution is a delay dynamic coupled fault diagnosis algorithm (DDCFD) to deal with the problem of coupled fault diagnosis with fault propagation/transmission delays and observation delays with imperfect test outcomes, a realistic representation of practical networked embedded systems. We proposed two methods to solve the problem: Partial-sampling method and a method based on block coordinate ascent and the Viterbi algorithm. Finally, we have formalized a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic (time-series) data. The framework employs Cox proportional hazards model and soft dynamic multiple fault diagnosis algorithm to infer the degraded state trajectories (complementary survival functions) of components and to estimate their remaining useful life.

This research enhances the competitiveness of American automotive industry by (i) minimizing life cycle cost of vehicle systems, (ii) enhancing safety and reliability of vehicular systems and (iii) improving customer satisfaction through enhanced vehicle availability. It has utility far beyond the immediate automotive application area being pursued here. Representative applications include aerospace systems, electrification of transportation, medical equipment, smart buildings/smart grid and communication networks, to name a few.

Book Chapter

1. A. Patterson-Hine, G. Aaseng, G. Biswas, S. Narasimhan and K.R. Pattipati, "Diagnostics and Testability, Chapter 16 in Stephen B. Johnson (Ed.), System Health Management with Aerospace Applications, Wiley, 2011, pp. 265-277.

Journal Publications

1. B. Pattipati, C. Sankavaram and K.R. Pattipati, "System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics," IEEE Trans. on SMC: Part C, Vol. 41, No. 6, November 2011, pp. 869-884.
2. B. Wang, W. Wei, H. Dinh, W. Zeng and K.R. Pattipati, "Fault Localization using Passive End-to-End Measurements and Sequential Testing for Wireless Networks", IEEE Trans. On Mobile Computing, Vol. 11, No. 3, March 2012, pp. 439-452.
3. A. Kodali, K. Pattipati, and S. Singh, "A Coupled Factorial Hidden Markov Model (CFHMM) for Diagnosing Coupled Faults", accepted for publication in IEEE Transactions on Systems, Man, and Cybernetics: Part A, July 2012.
4. A. Kodali, S. Singh, and K. Pattipati, "Dynamic set-covering for real-time multiple fault diagnosis with delayed test outcomes", accepted for publication in IEEE Transactions on Systems, Man, and Cybernetics: Part A, July 2012.
5. A. Kodali, Y. Zhang, C. Sankavaram, K. Pattipati, and M. Salman, "Fault Diagnosis in Cyber-physical Systems: Application to Automotive Electric Power Generation and Storage System (EPGS)", accepted for publication in IEEE/ASME Trans. on Mechatronics, 2012.
6. S. Zhang, K.R. Pattipati, Z. Hu, X. Wen, "Optimal Selection of Imperfect Tests for Fault Detection and Isolation," submitted to IEEE Transactions on Systems, Man and Cybernetics, part A - Systems and Humans, October 2011 (revised July 2012).
7. S. Zhang, K. Pattipati, Z. Hu, X. Wen, and C. Sankavaram, "Dynamic Coupled Fault Diagnosis with Propagation and Observation Delays", submitted to IEEE Transactions on Systems, Man and Cybernetics: Part A, May 2011.
8. C. Sankavaram, K. Pattipati, Y. Zhang, M. Howell, and M. Salman, "Fault Diagnosis and Prognosis in Cyber-Physical Systems with an Application to Hybrid Electric Vehicle Regenerative Braking System", to be submitted to Elsevier Journal of Information Sciences, 2012.

Conference Publications

1. C. Sankavaram, A. Kodali, and K. Pattipati, "An Integrated Health Management Process for Automotive Cyber-Physical Systems", subimitted to IEEE ICNC 2013 International Workshop on Cyber-Physical Systems (CPS), January 2013.
2. B. Pattipati, B. Balasingam, C. Sankavaram, K.R. Pattipati and Y. Bar-Shalom, "An EM Approach for Dynamic Battery Management Systems," 15^th International Conference on Information Fusion, Singapore, July 9-12, 2012.
3. C. Sankavaram, B. Pattipati, K. Pattipati, Y. Zhang, M. Howell, and M. Salman, "Data-driven Fault Diagnosis in a Hybrid Electric Vehicle Regenerative Braking System", in Proceedings of IEEE Aerospace Conference, Bigsky, Montana, March 2012
4. C. Sankavaram, A. Kodali, K. Pattipati, B. Wang, M. Azam, and S. Singh, "A Prognostic Framework for Health Management of Coupled Systems", IEEE International Conference on Prognostics and Health Management, Denver, CO, June 2011.
5. S. Deb, S. Ghoshal, M. Azam, V. Malepati and K.R. Pattipati, "Cost of Not Having a Sensor," Proceedings of the Infotech@Aerospace 2011,St. Louis, Missouri, March 2011.
6. B. Pattipati, C. Sankavaram, K. Pattipati, Y. Zhang, M. Howell, and M. Salman, "Multiple Model Moving Horizon Estimation Approach to Prognostics in Coupled Systems", IEEE Autotestcon, Baltimore, MD, September 2011 (winner of Walter E. Petersen Best Technical Paper Award).
7. A. Kodali, K. Pattipati and S. Singh, "A Coupled Factorial Hidden Markov Model (CFHMM) for Diagnosing Coupled Faults", IEEE Aerospace Conference, Big Sky, Montana, March 2010.

Award ID: 0931956