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Integrated Model-Based and Data-Driven Diagnosis of Automotive Antilock Braking Systems

Model-based fault diagnosis, using statistical hypothesis testing, residual generation (by analytical redundancy), and parameter estimation, has been an active area of research for the past four decades. However, these techniques are developed in isolation, and generally, a single technique cannot address the diagnostic problems in complex systems. In this paper, we investigate a hybrid approach, which combines model-based and data-driven techniques to obtain better diagnostic performance than the use of a single technique alone, and demonstrate it on an antilock braking system. In this approach, we first combine the parity equations and a nonlinear observer to generate the residuals. Statistical tests, particularly the generalized likelihood ratio tests, are used to detect and isolate a subset of faults that are easier to detect. Support vector machines are used for fault isolation of less-sensitive parametric faults. Finally, subset selection (via fault detection and isolation) is used to accurately estimate fault severity.