Intelligent model-based diagnostics for vehicle health management

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.

[1]  Michèle Basseville,et al.  Early warning of slight changes in systems , 1994, Autom..

[2]  Takaji Umeno,et al.  Observer Based Estimation of Parameter Variations and Its Application to Tire Pressure Diagnosis , 1998 .

[3]  Krishna R. Pattipati,et al.  Multi-signal flow graphs: a novel approach for system testability analysis and fault diagnosis , 1994 .

[4]  Peter Young,et al.  Parameter estimation for continuous-time models - A survey , 1979, Autom..

[5]  Ian D. Walker,et al.  Diagnosis of automotive electronic throttle control systems , 2004 .

[6]  Zdzislaw Kowalczuk,et al.  Model based diagnosis for automotive engines-algorithm development and testing on a production vehicle , 1995, IEEE Trans. Control. Syst. Technol..

[7]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[8]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[9]  Rolf Isermann,et al.  Model Based Fault Detection of Vehicle Suspension and Hydraulic Brake Systems , 2000 .

[10]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[11]  Krishna R. Pattipati,et al.  Simulation-based testability analysis and fault diagnosis , 1996, Conference Record. AUTOTESTCON '96.

[12]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[13]  Krishna R. Pattipati,et al.  Decentralized real-time monitoring and diagnosis , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[14]  Michèle Basseville,et al.  Fault Detection and Isolation in Nonlinear Dynamic Systems: A Combined Input-Output and Local Approach , 1998, Autom..

[15]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[16]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[17]  Peter Struss,et al.  Model-Based Tools for the Integration of Design and Diagnosis into a Common Process - A Project Report , 2002 .

[18]  Rolf Isermann,et al.  Model based fault detection of vehicle suspension and hydraulic brake systems , 2002 .

[19]  G Rizzoni,et al.  Nonlinear parity equation based residual generation for diagnosis of automotive engine faults , 1995 .

[20]  Thomas Pfeufer.  Application of model-based fault detection and diagnosis to the quality assurance of an automotive actuator , 1997 .

[21]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[22]  D. Wolfe,et al.  ON DERIVATIVE ESTIMATION IN SPLINE REGRESSION , 2000 .

[23]  J. Gertler.  Fault detection and isolation using parity relations , 1997 .

[24]  Janos Gertler,et al.  Generating directional residuals with dynamic parity relations , 1995, Autom..

[25]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[26]  Vadim I. Utkin,et al.  Automotive engine diagnosis and control via nonlinear estimation , 1998 .