Monte Carlo-Discrete Wavelet Transform for Diagnosis of Inner/Outer Race Bearings Faults in Induction Motors

摘要

Detection and precise diagnosis of faults in induction motors has turned into a major field in the electric machinery world, due to the importance of these motors in industry and research. Faults are usually classified in two groups as mechanical and electrical faults. Bearing failures that consists more than half of mechanical faults can be recognized by applying high magnitude, short-time frequency bursts to supply sources. Methods like (short-time) Fourier, (continuous-discrete) Wavelet, and Park transforms are among the most common strategies for fault detection. The deficit that these methods usually face is usually related to the fact that either these methods cannot derive non-stationary behavior of fau whereas most of bearing faults are non-stationary and low energy signals. In the proposed method, a new approach has been offered that consists of decomposing the frequency spectrum of output stator current signal into several levels. Considering this matter that faults have non-certain behavior, it seems necessary to model this non-deterministic behavior. Monte-Carlo model is a strong approach toward deterministic modeling. In this paper, the fault probabilistic behavior has been modeled using this approach.