A Review of Condition Monitoring and Fault Diagnosis Methods for Induction Motor

Jigyasu, Rajvardhan; Mathew, Lini; Chatterji, S.

doi:10.1109/iccons.2018.8662833

Cited by 33 publications

(19 citation statements)

References 82 publications

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“…Of the various undesirable stresses that the IM is subjected to, a broken rotor bar is the most frequent fault that squirrel cage induction motors (SCIMs) encounter in underground coal mine scenarios. This is due to thermal, magnetic, residual, dynamic, mechanical, and environmental stresses arising from overload, supply unbalance, sparking, unbalanced magnetic pull, electromagnetic noise, vibration, manufacturing defects, unbalanced shaft torque, centrifugal forces, loose laminations, contamination, and abrasion [4][5][6][7][8]. Moreover, the rotor bars of heavy-duty SCIMs are stressed thermally and mechanically pertaining to the direct-on-line (DOL) start for various applications in underground coal mines, unlike their applications for serving other industrial needs, wherein mostly reduced voltage starting is preferred [9,10].…”

Section: Introductionmentioning

confidence: 99%

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

Sinha¹,

Hati

Benbouzid

et al. 2021

Machines

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The requisite of direct-on-line (DOL) starting for various applications in underground mines subjects the rotor bars of heavy-duty squirrel cage induction motors (SCIMs) to severe stresses, resulting in sustained fault in the rotor bars, unlike the applications where mostly reduced voltage starting is preferred. Furthermore, SCIMs working in underground mines are also affected by unforeseen frequency fluctuations. Hence, the paper proposes a discrete wavelet transform (DWT)-based broken rotor bar detection scheme using the stator current analysis of SCIM when subjected to a frequency regulation (±4% of 50 Hz supply) in steady-state, as prevalent in underground mines. In this regard, the level-seven detailed coefficient obtained by the DWT-based multi-resolution analysis of stator current corresponding to the healthy rotor is compared with that of the faulty rotor to extract the necessary features to identify the fault. Further implementation of the proposed scheme is done using artificial neural network (ANN)-based pattern recognition techniques, wherein both feed-forward backdrops and cascaded forward backdrop type ANNs have been used for fault pinpointing based on the feature extraction results obtained from DWT. The scheme is developed and analysed in MATLAB/Simulink using 5.5 kW, 415 V, 50 Hz SCIM, which is further validated using the LabVIEW-based real-time implementation.

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Section: Introductionmentioning

confidence: 99%

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

Sinha¹,

Hati

Benbouzid

et al. 2021

Machines

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“…This not only reduces the costly maintenance but also helps in minimizing the unwanted downtime and catastrophic failure [8].Condition Monitoring (CM) and fault diagnosis of induction motor has gained attention of various researcher in the past few decades. A number of fault detection techniques such as vibration, current, acoustic and magnetic flux have been developed [9]. A practical automatic condition monitoring method requires the monitoring of aforementioned parameters along with a computer based intelligent algorithms for detection and diagnosis of motor conditions.…”

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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Section: Introductionmentioning

confidence: 99%

“…The Fault classification and detection [1] is currently most favourite area of research now days because of its application. It helps in smooth working, increases efficiency, economical product [1,2]. Induction motors (IM) now a day's became an important part of automation and are most noteworthy prime movers in industrial applications due to their simplicity, ruggedness and reliable construction [3].…”

Section: Introductionmentioning

confidence: 99%

“…The fault diagnosis and condition monitoring of induction motor become necessary to prevent the unpredicted breakdowns and reduce unscheduled downtime. The most important techniques for fault diagnosis are thermal monitoring, vibration monitoring, noise monitoring, torque monitoring and flux monitoring, current signature analysis [1]. There are many signal processing techniques which are very useful for fault detection purpose.…”

Section: Introductionmentioning

confidence: 99%

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Smart Classifiers Based Classification and Condition Monitoring of Induction Motor Faults

Chikkam*¹,

Singh²,

Jigyasu³

2019

IJITEE

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With the advancements in the field of automation in the industries the use of machines is very high and if the machine which require some rotatory action for the load, the Induction motor comes in to play because of the advantages such as robustness, low maintenance, low cost etc. But with the increase in the dependency over the motors it becomes highly recommended to have machines with reliability because break in the work can lead to huge amount of loss. In order to increase the reliability of the motors predictive maintenance comes into play which requires fault classification or detection which is easily and accurately possible using the Machine Learning algorithms. With the requirements of the present scenario for predictive maintenance, this paper presents the fault classification of induction motor using Support Vector Machine SVM) and K- Nearest Neighbour (KNN) technique of classification. Here in this paper the bearing fault (BF) and broken rotor bar (BRB) fault is considered. The results collected are on the basis of validation and Principle Component Analysis (PCA) technique. And it is found that the SVM technique is better than the KNN for fault classification of Induction motor.

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A Review of Condition Monitoring and Fault Diagnosis Methods for Induction Motor

Cited by 33 publications

References 82 publications

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

Induction Motor Fault Classification using Pattern Recognition Neural Network

Smart Classifiers Based Classification and Condition Monitoring of Induction Motor Faults

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