Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

Abstract

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.

Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

“…In [6], the Zhao-Atlas-Marks (ZAM) distribution was investigated for BRB faults diagnosis based on vibration transient signals. In a recent work, Rangel-Magdaleno [7] developed a demodulation methodology using discrete wavelet transform (DWT) and autocorrelation methods for the incipient broken bar detection based on MVSA. Delgado-Arredondo [8] presented the complete ensemble empirical mode decomposition (CEEMD) for BRB faults diagnosis, which solves the drawbacks of empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) for obtaining accurate analysis results.…”

“…Similarly, the popular methods based on DWT, EMD, HHT, SST, etc., are also faced with low processing efficiency. The estimated parameters used in [7,12] cannot present a clear classification of the BRB fault severities. However, the conventional methods based on a fast Fourier transform (FFT) show its inability to process non-linear and non-stationary vibration signals.…”

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

“…Being rich in information on machinery health conditions, vibration signals are often processed by advanced signal processing techniques to extract features from the rotating machinery [6,7]. Up to now, multiple methods have been extensively applied for vibration signal processing, such as fast Fourier transform (FFT), short-time Fourier transform (STFT) [8], wavelet transform (WT) [9], Wigner-Ville distribution (WVD) [10], empirical mode decomposition (EMD) [11], multiwavelets transform (MWT) [12], etc. However, in spite of all the achievements these methods have made, they are fixed and independent of the measured signals.…”

Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction

“…Although different physical magnitudes have been investigated for IM condition monitoring [3], [7]- [9]; vibration analysis remains as the most industrially accepted approach. The vibration analysis is a useful and reliable tool to assess the IM condition since the characteristic vibration modes of any rotating machine changes in presence of faults [10]- [13]. Yet, although several methodologies applied to diagnose faults in electric motors have been presented during the last decades, most of these methodologies are focused on the analysis of a specific fault mode [7], [10], [14]- [15].…”

“…The vibration analysis is a useful and reliable tool to assess the IM condition since the characteristic vibration modes of any rotating machine changes in presence of faults [10]- [13]. Yet, although several methodologies applied to diagnose faults in electric motors have been presented during the last decades, most of these methodologies are focused on the analysis of a specific fault mode [7], [10], [14]- [15]. Indeed, the application of such health monitoring schemes to industrial scenarios presents new challenges that must be addressed, where different faults may appear hiding or overlapping the expected characteristic fault patterns.…”