Bearing fault detection in induction motor using pattern recognition techniques

Zarei, Jafar; Poshtan, Javad; Poshtan, Majid

doi:10.1109/pecon.2008.4762564

Cited by 11 publications

(14 citation statements)

References 12 publications

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“…The inner race defect in the bearing can be detected by identifying the characteristic frequency in vibration spectrum known as Ball Pass Frequency of Inner race (BPFI) computed by the relation given by [1][2][3][4], [8][9][10][11][12] .…”

Section: Bearing Fault Vibrational Characteristic Frequenciesmentioning

confidence: 99%

“…Thus, a poor quality supply may distort the shape of pattern and therefore difficult to identify fault location reliably. The other approaches employed by researchers are pattern recognition [12] and wavelet approach [13]. These approaches are complex; require more computational time, large computer memory and experienced engineer for fault analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preliminary investigation on bearing fault analysis in three phase induction motor by MEMS accelerometer

Maruthi¹,

Hegde²

2014

2014 International Conference on Advances in Electronics Computers and Communications

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This paper proposes a simple method to detect single point defect in inner race of the bearing of smaller horse power induction motor by employing motor vibration and current signals. Experimental investigations have been carried out under no load, full load and unbalance voltage conditions. The effect of inner race bearing fault on the air gap length modulation has been investigated. The spectral analysis of both vibration and current signal has been carried out by using radix-2 decimation in time fast Fourier transform (DIT-FFT) algorithm. The results of both the methods are compared with theoretical values and found to be closely matching. This indicates that proposed method can be used as an alternative to conventional vibration and current signature analysis for condition monitoring of bearings. Keywords-Inner race defect; MEMS accelerometer; vibration analysis; DIT FFT algorithm; three phase induction motor; motor current signature analysis.I.

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Section: Bearing Fault Vibrational Characteristic Frequenciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preliminary investigation on bearing fault analysis in three phase induction motor by MEMS accelerometer

Maruthi¹,

Hegde²

2014

2014 International Conference on Advances in Electronics Computers and Communications

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“…MLP neural networks are quite fundamental and widely used, because of their simple and effective algorithm. In the research of [9] where authors identified the bearing conditions of the motor by using MLP neural networks and by designing their model to classify vibration signals to three different classes-broken outer ring, broken inner ring of the bearing, and healthy. This model used standard parameters including the Levenberg-Marquardt training algorithm.…”

Section: Introductionmentioning

confidence: 99%

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

et al. 2020

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In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

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“…The artificial neural network (ANN) is a statistical supervised learning approach, where faults can be detected by training the network to classify and detect faults. ANN has been prevalently used for detection of motor faults such as bearings [14], 5roken rotor bar [13] and stator winding short circuit [11,16]. In [17] authors used motor vibration data for extracting frequency domain features and classified the motor bearing defects with the help of ANN.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Classification using Pattern Recognition Neural Network

Grover¹,

Mathew²,

Chaterji³

2019

IJITEE

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The industrial growth has escalated the use of induction motors as prime movers in modern industry. This is due to its low cost, simple construction and ruggedness. Although rugged, these may fail earlier than expected life due to, excessive mechanical, electrical and environmental stresses. Automatic Artificial Intelligence (AI)-based systems are nowadays widely employed in the domain of induction motor fault identification with high success rate. Artificial neural network are utilized extensively for the detection and diagnosis of various induction motor faults. These systems generally use supervised learning, where the models are pre-trained such that these are skilled enough to classify the absence or presence of faults in motor under investigation. In this paper, a highly effective approach for detection of different motor fault conditions, based on pattern recognition technique is presented. In the proposed method the statistical time domain features are computed from three phase motor current and used as inputs of ANN. Seven different classes of motor conditions: healthy, broken rotor bar, broken rotor bar with stator winding short circuit and inner and outer race bearing defects were considered. The results indicates that the proposed methodology is highly effective for diagnosis of various induction motor faults with high success rate.

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Bearing fault detection in induction motor using pattern recognition techniques

Cited by 11 publications

References 12 publications

Preliminary investigation on bearing fault analysis in three phase induction motor by MEMS accelerometer

Preliminary investigation on bearing fault analysis in three phase induction motor by MEMS accelerometer

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

Induction Motor Fault Classification using Pattern Recognition Neural Network

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