Induction Machine Bearing Fault Detection Using Empirical Wavelet Transform

Abstract

The detection of faults related to the optimal condition of induction motors is an important task to avoid the malfunction or loss of the motor, thus avoiding high repair or replacement costs and faults in the efficiency of the process to which they belong. These faults are not limited to a single area; mechanical and electrical problems can cause a fault. Specifically, the bearing of a motor is subjected to several effects that cause bearing faults, which cause significant breakdowns in the machinery. This article proposes a methodology for detecting bearing faults on an induction motor. The first part of the methodology uses a signal processing method called empirical wavelet transform (EWT), which decomposes the vibration signal into multiple components to extract a series of amplitude and frequency modulated components (AM-FM) with a Fourier spectrum. First, the vibration signal data are collected in a normal operating condition and the other with bearing damage due to perforation. Then, three types of goodness-of-fit tests are used, Kuiper, Kolmogorov–Smirnov, and Pearson chi-square, to classify the signals and determine which ones belong to a damaged engine. Finally, the experimental results show that the EWT in conjunction with the proposed goodness tests achieves competitive precision and efficiency in diagnosing induction motor-bearing faults.

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

“…Similarly, Lopez-Gutierrez et al proposed to apply an empirical wavelet transform to the vibration signal of an IM to enhance bearing fault detection. The method is able to extract a series of amplitudes and frequencies and obtain the Fourier spectrum for further analysis [23]. Agah et al proposed a hybrid method based on stator current signals for rotor rod fracture and rotor mixed eccentricity faults of three-phase squirrel cage asynchronous motors.…”

Fault diagnosis of rolling element bearing based on the empirical wavelet transform technique and correlation coefficient

“…The results suggested that the novel method extracted the bearing fault features successfully. Lopez et al [14] suggested an empirical wavelet transform (EWT)-based methodology for detecting bearing faults. The experimental results revealed that the proposed technique efficient in diagnosing induction motor-bearing defects.…”