Research on Rotating Machinery Fault Diagnosis System
Fault diagnosis has been the research hotspot in the industry fields, but, with the gradual complication in modern industry equipments and systems, it is more hard to quickly diagnose complicated or exceptional faults. For overcoming the diagnosis weakness of traditional fault diagnosis methods in the rotating machinery, this paper presents a hybrid method that combines the wavelet with neural networks theory. Both the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations were solved and this method was used in rotating machinery fault diagnosis. The research shows that this method is feasible and effective and can be applied to the other rotating machinery fault diagnosis.
Z. Yao and Z. H. Wang, "Research on Rotating Machinery Fault Diagnosis System", Applied Mechanics and Materials, Vols. 55-57, pp. 1310-1314, 2011