Hybrid Neural Network-Based Fault Diagnosis and Fault-Tolerance Design with Application in Electro-Hydraulic Servovalve

ABSTRACT

In this study, to cope with the needs of the predictive maintenance for complex systems, a hybrid dynamic Artificial Neural Network (ANN) based fault and degradation diagnosis and tolerance method is designed. The multi-layer feed forward ANN and recurrent ANN are combined, so as to be able to form a dynamic identification model for the nonlinear time-varying system. It has three work modes, and can perform the fault and degradation diagnosis and tolerance by using these modes alternately. The result of its application in an Electro-Hydraulic Servovalve of a Hydroelectric Generation Unit shows that it is effective and feasible, has the advantages of the simple and fast algorithms, working online, and no disturbance signals importing to the system.