Applications of time-frequency and time-scale representations to fault detection and classification

The authors propose the use of generalized time-frequency and time-scale representations coupled with a hierarchy of neural nets to solve the problem of the automatic detection and classification of faults in mechanical systems such as the gearboxes and transmissions onboard helicopters. With this technique, no underlying model for the events of interest is assumed. Rather the system learns to detect and classify faults by examination and fusion of features from training data which have known fault conditions. Results of processing real helicopter gearbox vibration data with seeded faults are given