Application of a hybrid neuro-fuzzy system to the fault diagnosis of an automotive electromechanical actuator

Abstract

Modern automatic fault detection and diagnosis methods are based on analytic and heuristic models of the process under consideration. Usually, a lot of fault symptoms can be generated using analytic symptom generation methods like parameter estimation, state estimation and parity equations as well for the evaluation of sampled input/output signals of the process. However, some relations, especially the cause-effect relations between the underlying faults and the observable symptoms, are quite difficult to be represented by analytic models. A rule-based approach is more suitable to acquire, represent and process the diagnostic knowledge base. In order to cope with uncertainty and to allow automatic knowledge extraction from experimental data, a neuro-fuzzy-structure is applied to the classification of faults, based on symptoms generated by identifying a mathematical model. The hybrid neuro-fuzzy scheme SARAH used consists of three layers corresponding to the three fuzzy inference steps. All parameters are automatically determined based on experimental data by clustering and learning. Finally, the performance of the diagnosis scheme is illustrated on the example of an automobile actuator with several different faults.