2013
DOI: 10.1186/1687-5281-2013-29
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Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis

Abstract: Fault diagnosis of induction motors in the practical industrial fields is always a challenging task due to the difficulty that lies in exact identification of fault signatures at various motor operating conditions in the presence of background noise produced by other mechanical subsystems. Several signal processing approaches have been adopted so far to mitigate the effect of this background noise in the acquired sensor signal so that fault-related features can be extracted effectively. Addressing this issue, … Show more

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Cited by 37 publications
(32 citation statements)
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“…2 One of the motors operated under normal condition as a benchmark for comparison with other faulty motors. The others faulty motors had faults such as bowed rotor shaft, broken rotor bar, bearing outer race fault, rotor unbalance, adjustable eccentricity motor (misalignment), and phase unbalance.…”
Section: Experiments and Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…2 One of the motors operated under normal condition as a benchmark for comparison with other faulty motors. The others faulty motors had faults such as bowed rotor shaft, broken rotor bar, bearing outer race fault, rotor unbalance, adjustable eccentricity motor (misalignment), and phase unbalance.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…Signal processing is a crucial component for these three types but with different impacts and roles. 2 The main purpose of the signal processing step in a fault diagnosis system is to reveal fault signatures from the measured quantities obtained from a motor in operation. For this purpose, time-frequency analysis tools are popular as they can provide both time and frequency resolution simultaneously.…”
Section: Introductionmentioning
confidence: 99%
“…Very recently, a twodimension approach was reported in [155] for fault diagnosis of induction motors, where time-domain vibration signals acquired from the operating motor were firstly converted into two-dimension gray-scale images, and the discriminating texture features were then extracted from these images utilizing local binary patterns (LBP) technique. The extracted texture features were finally used for fault diagnosis with the aid of a classifier.…”
Section: A Time-domain Signal Based Methodsmentioning
confidence: 99%
“…Some researchers have investigated the potentiality of reading vibration signals to diagnose electrical faults [15][16][17]. However, the techniques are invasive since they depend on the signals measured and acquired by accelerometers, which placed at the equipment structures.…”
Section: Introductionmentioning
confidence: 99%