Some new signal processing approaches for gear fault diagnosis

This paper presents three new signal-processing approaches to gear fault diagnosis, viz., the wavelet analysis, the resonance demodulation and a model-based method using an autoregressive (AR) technique. The results show that all three approaches are effective for detecting and diagnosing localised gear faults under low-load conditions, and are more sensitive and robust than current gear diagnostic techniques. The AR model based approach is probably the most promising one for implementation into an automated diagnostic system due to its simplicity in formulation and in application