Wavelet Based Instantaneous Power Analysis for Induction Machine Fault Diagnosis

Abstract

The aim of this paper is to present a wavelet based approach to detect broken bar faults in squirrel-cage induction machines. This approach uses instantaneous power as a medium for fault detection. A multi-resolution instantaneous power decomposition based on wavelet transform provides the different frequency bands whose energies are affected directly by the broken bar fault. Actually, the instantaneous power has low frequency components which are difficult to localize in the frequency domain analysis unless there is a long signal acquisition of current and voltage. By the wavelet transform, the study of energy related to the low frequency band is free of noise and improves the fault diagnosis without having the slip information. This can be especially useful when the induction machine is not fully loaded. In this paper, it is shown that for these reasons the wavelet approach applied to instantaneous power is superior to the frequency domain in the case of one and three broken bars for a three-phase squirrel cage induction machine.

Digital Signal Processing for Induction Machines Diagnosis - A Review

“…In [23], it was shown that the signal can be reconstructed by the sum of the approximation and detail coefficients ( [24], it is shown that the related high-pass and low-pass filters based on Daubechies function give good results for this process. This method is applied to the instantaneous power with three broken bars in [25] (Fig. 13).…”

Broken bars fault diagnosis based on uncertainty bounds violation for three-phase induction motors

“…Until now, the following strategies for fault diagnosis have been presented: Stator current ‐ which is the analysis of the frequency spectrum of the stator current, has been mostly conducted by traditional signal processing methods, based on discrete and fast Fourier transformation. In spite of their simplicity, these techniques suffer from a number of issues, including spectrum leakage, frequency resolution, and noise, which restrict their overall reliability . Moreover, the methods based on the motor current spectrum are not capable in distinguishing a supply voltage unbalance condition and effectively detecting the broken bar fault in double cage induction motors.…”

The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient

“…For the detection of these faults and more specifically, for the case of broken bars and eccentricity, various input signals have been used quite successfully, such as induced voltages [7], vibration signals [8], currents and vibration signals [9], instantaneous angular speed or power [10]. However, methods that rely only on the use of currents, like the Motor Current Signature Analysis (MCSA) [11], [12] are usually preferred mainly due to their non-invasive nature.…”