2017
DOI: 10.1016/j.ymssp.2016.06.032
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Methodology for fault detection in induction motors via sound and vibration signals

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Cited by 214 publications
(130 citation statements)
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“…The first-class includes the detection of single faults by analyzing one or multiple parameters; the second class covers the detection of different faults with multiple parameters and processing techniques, and the last one contains the mixed techniques of various computing-intensive approaches to analyze different electrical and mechanical parameters in order to detect multiple faults [61][62][63][64]. In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts are made for the application of intelligent algorithms [66,67] including new approaches to fault detection and isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77].…”
Section: Introductionmentioning
confidence: 99%
“…The first-class includes the detection of single faults by analyzing one or multiple parameters; the second class covers the detection of different faults with multiple parameters and processing techniques, and the last one contains the mixed techniques of various computing-intensive approaches to analyze different electrical and mechanical parameters in order to detect multiple faults [61][62][63][64]. In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts are made for the application of intelligent algorithms [66,67] including new approaches to fault detection and isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77].…”
Section: Introductionmentioning
confidence: 99%
“…In the literature, different types of signals have been used as an indicator for motor fault diagnosis. These indicators include magnetic flux, vibration, stator current (negative and zero sequence or third harmonic component), zero sequence component of voltage, thermal image processing, instantaneous real power and reactive power, phase shift between stator and supply voltages, and acoustic noise [9,11,[15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Moreover, many researchers used the instantaneous torque as a fault indicator in induction motors [18,21,[29][30][31].…”
Section: Introductionmentioning
confidence: 99%
“…The application of vibration signal and acoustic signal to the fault feature extraction of rotating machinery fault is of great value [1]. Rolling bearing is one of the most widely used parts in mechanical equipment.…”
Section: Introductionmentioning
confidence: 99%