This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Model-Based Component Fault Detection and Isolation in the Air-Intake System of an SI Engine Using the Statistical Local Approach
Technical Paper
2003-01-1057
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present. In particular, the local approach is shown to simplify and reduce the complicated FDI problem to the standard problem of detecting changes in the mean value of a Gaussian vector with a constant covariance matrix. In doing so, the least square score is implemented as our primary residual, and the statistical properties of the so-called improved residuals are used to detect changes in the parameters of a parametric model. Applying the local approach, in both offline and online configurations, to a mean-value model of a spark-ignition engine, component faults in the air-intake system, namely small abrupt changes in the volumetric efficiency coefficient ηo and the throttle discharge coefficient Cd, have been successfully detected and isolated. The FDI methodology shows robustness to modeling errors and noise, proving the fact that a perfect model is not required to monitor a given system. Since unknown plant parameters are usually determined by identification techniques, which never yield perfect results, modeling errors are indeed expected in practice. Consequently, the local approach sheds promises for robust on-board diagnostics in the face of the overall nonlinear powertrain problem.
Authors
Topic
Citation
Radwan, A., Soliman, A., and Rizzoni, G., "Model-Based Component Fault Detection and Isolation in the Air-Intake System of an SI Engine Using the Statistical Local Approach," SAE Technical Paper 2003-01-1057, 2003, https://doi.org/10.4271/2003-01-1057.Also In
References
- Alcorta Garcia, E. Frank P. M. 1997 Deterministic nonlinear observer-based approaches to fault diagnosis: A survey Control Eng. Practice 5 663 670
- Basseville, M. Nikiforov I. 1993 Detection of Abrupt Changes - Theory and Applications Prentice-Hall Information and System Sciences Series Englewood Cliffs, NJ
- Basseville, M. 1998 On-board component fault detection and isolation using the statistical local approach Automatica 34 11
- Benveniste, A. Basseville M. Moustakides G. 1987 The asymptotic local approach to change detection and model validation IEEE Trans. Automatic Control AC-32 7 583 592
- Chen, J. Patton R. 1999 Robust Model-Based Fault Diagnosis for Dynamic Systems Kluwer Academic Publishers
- Cussenot, C. 1996 Surveillance et diagnostic de la chaine de depollution d'une automobile Universite de Rennes France
- Devauchelle-Gash, B. 1991 Diagnostic mechanique des fatigues sur les structures soumises a des vibrations en ambiance de travail Univ. Paris IX Dauphine
- Ding, S. X. Frank P. M. Köppen-Seliger B. 2000 Current developments in the theory of FDI IFAC Safeprocess Symp. 1 16 27 Budapest, Hungary
- Dobner, D. J. 1986 An engine model for dynamic engine control development ASME Winter Annual Meeting
- Frank, P. M. 1990 Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - A survey Automatica 26 459 474
- Frank, P. M. Ding X. 1997 Survey of robust residual generation and evaluation methods in observer-based fault detection systems Journal of Process Control 403 424
- Frank, P. M. Schreier G. Alcorta E. 1999 Nonlinear observers for fault detection and isolation New Directions in Nonlinear Observer Design Springer
- Garg, V. Hedrick J. K. 1995 Fault detection filters for a class of nonlinear systems Proc. of ACC '95 1647 1651
- Gertler, Janos J. 1998 Fault Detection and Diagnosis in Engineering Systems Marcel Dekker Inc.
- Henry, D. Frank P. M. 1986 Component failure detection via nonlinear observer Proc. of the IFAC Workshop on Fault Detection and Safety in Chemical Plants
- Krishnaswami, V. Rizzoni G. 1994 Nonlinear parity equation residual generation for fault detection and isolation Proceedings of the IFAC/IMACS Symposium on Fault Detection, Supervision and Safety for Technical Processes - Safeprocess'94 317 322 Espoo, Finland
- Moskwa, J. J. Hedrick J. K. 1989 Modeling and validation of automotive engines for control algorithm development Advanced Automotive Technologies 237 247 13
- Nyberg, M. 1999 Model based diagnosis of both sensor-faults and leakage in the air-intake system of an SI-engine SAE Paper No. 1999-01-0860
- Powell, B. K. Cook J. A. 1987 Nonlinear low frequency phenomenological engine modeling and analysis Proc. American Control Conference 332 340 Minneapolis, MN June
- Radwan, A. 2002 Model-Based On-Board Component Fault Detection and Isolation in the Internal Combustion Engine Air-Intake System Using the Statistical Local Approach Ohio State University
- Rizzoni, G. 1998 Powertrain Dynamics Notes for the Course ME 781 Mechanical Engineering Department, Ohio State University
- Russell, John D. 2000 On Automotive Engine Intake Manifold Dynamic Modeling, Estimation, and Control Ohio State University
- Taylor, Charles F. 1994 The Internal Combustion Engine in Theory and Practice 1 The MIT Press
- Zhang, Q. Basseville M. Benveniste A. 1994 Early Warning of Slight Changes in Systems Automatica 30 1 95 114
- Zhang, Q. Basseville M. 1998 Monitoring nonlinear dynamic systems: a combined observer-based and local approach IEEE CDC'98 1149 1154
- Zhang, Q. Basseville M. Benveniste A. 1998 Fault detection and isolation in nonlinear dynamic systems: a combined input-output and local approach Automatica 34 11 1359 1373