A HYBRID FAULT-DIAGNOSIS APPROACH USING NEURAL NETWORKS

A hybrid fault diagnosis method is proposed in this paper which is bas ed on the parity equations and neural networks. Analytical redundancy is employed by using parity equations. Neural networks then are used t o maximise the signal-to-noise ratio of the residual and to isolate di fferent faults. Effectiveness of the method is demonstrated by applyin g it to fault detection and isolation for a hydraulic test rig. Real d ata simulation shows that the sensitivity of the residual to the fault s is maximised, whilst that to the unknown input is minimised. The sim ulated faults are successfully isolated by a bank of neural nets.