Comparative Study of Time-Frequency Decomposition Techniques for Fault Detection in Induction Motors Using Vibration Analysis during Startup Transient

Abstract

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different timefrequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

“…From an engineering point of view, it is suitable for developing low-cost feature extractions on a regular basis. In several documented WFT applications, the Gaussian window has been highlighted as having the best compensation among several constraints, i.e., the Gabor transform [27,28]. An example of them is the analysis tool presented in [29] by Lima et al for signals captured with impacts and vibrations from a mechanical manipulator.…”

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

“…The most popular techniques for fault detection in induction motors are the motor current signature analysis (MCSA) and the motor vibration signature analysis (MVSA). There are several current analysis techniques in literature about the identification of faults in induction motors such as broken bars [2,[5][6][7][8][9][10][11] and also vibration analysis techniques [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. When it comes to vibration analysis techniques, there are several methodologies for the detection of broken bars.…”

“…Ordaz-Moreno et al [6] developed a methodology based on discrete wavelet transform (DWT) for broken bar detection during transient state using current signature analysis. Delgado-Arredondo et al [15] presented a comparative study of time-frequency decomposition techniques for fault detection in induction motor using vibration analysis. A review of wavelet analysis applied to induction motor fault detection is presented in [26].…”

Fuzzy Multicriteria Model for Selection of Vibration Technology

“…Furthermore, its versatility with regard to the machines it can be applied to and the typology of faults or anomalies that can be detected [6], along with the possibility of developing an automatic diagnostic system [7], have made it the most widely used predictive method [8]. This has led to a large number of contributions in the literature [3], analysing new diagnostic techniques [9,10], many case studies of fault diagnosis in different components and types of machinery [5,7,11,12], and studies of prognostics or fault prediction before it occurs [4].…”