Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

Kumar, Prashant; Hati, Ananda Shankar

doi:10.1007/s11831-020-09446-w

Cited by 107 publications

(48 citation statements)

References 98 publications

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“…SVM was first introduced by Vladimir Vapnik in 1994 and it has become one of the most popular tools for classification [67]. Theoretically, the basic statistical learning principle of SVM is the structural risk minimization (SRM), which is achieved by minimizing the upper bound of the generalization error [68].…”

Section: Support Vector Machinementioning

confidence: 99%

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

Meng

Oladejo

et al. 2021

ChemBioEng Reviews

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Enhancement in boiler efficiency via controlled operation could lead to energy savings and reduction in pollutant emission. Activities such as scheduled maintenance could be improved by increasing boiler availability and reducing running costs. However, the time interval between recommended maintenance is varied depending on boilers. The application of fault detection, diagnosis and prognosis (FDDP) in industrial boilers plays an important role in optimizing operation, early-warning of faults, and identification of root causes. This review discusses the application of machine learning (ML)based algorithms (knowledge-driven and data-driven) for FDDP, thus allowing the identification of fit-for-purpose techniques for specific applications leading to improved efficiency, operability, and safety.

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Section: Support Vector Machinementioning

confidence: 99%

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

Meng

Oladejo

et al. 2021

ChemBioEng Reviews

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“…A three-layered ANN having 10 neurons in each layer is designed in the present work. Two types of ANN are designed and tested in the present work using cascaded forward backdrop and feed-forward backdrop-based designs, as shown in Figure 5a,b, respectively [14,27,29,31,[40][41][42][43][44][45]. ANN training is done using four different algorithms: Bayesian Regulation, Polak-Ribiere Restarts, Gradient Descent with momentum and adaptive learning rate, and finally, Levenberg Marquardt algorithm.…”

Section: Ann-based Analysismentioning

confidence: 99%

“…Moreover, all these DWT-based approaches are complex and computation-intensive. Furthermore, many researchers have dedicated the artificial neural network (ANN) in the recent past towards the design of an effective fault detection algorithm [26][27][28][29]. The works done in [26,27] deploy convolutional neural networks with an inherent adaptive design for the fusion of feature extraction and classification phases of the fault detection into a single learning body.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the authors of [28] concentrate on stator winding fault detection using a fuzzy detection system. Moreover, a comprehensive review of ANN-based techniques used to detect bearing damage and broken rotor bars is presented in [29]. Herein, vibration signals are used for feature extraction, followed by a hybrid feature reduction technique.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Various types of induction motors are widely used in manufacturing and therefore failure analysis of induction motors might be important (Benbouzid, 2000). Some of the common fault causes seen in induction motors are listed as follows (Kumar and Hati, 2021): In order to detect these faults studies generally focus on vibration (Rodriguez, Belahcen, and Arkkio, 2006). Velocity, acceleration or displacement due to mechanical faults reflects into vibration signal (Katalin, 2015).…”

Section: Figure 1 Maintenance Approachesmentioning

confidence: 99%

Maki̇ne Öğrenmesi̇ Yaklaşimlari İle İndüksi̇yon Motorlari İçi̇n Akilli Hata Tespi̇ti̇ Ve Siniflandirmada Deneysel Bi̇r Değerlendi̇rme

Kasap

Çinar

Yazıcı

et al. 2021

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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The concept of Industry 4.0 aims fully digital and autonomous production. For manufacturing systems to work properly, their maintenance must be done correctly. However, while unnecessary maintenance causes waste of money and time, skipping necessary maintenance can also cause unexpected down times in production. Predictive maintenance (PdM) aims to predict and diagnose faults at an early stage and also the time remaining for future failures of equipment which might provide significant cost savings compared to traditional maintenance approaches. Today's sensor and data collection technologies have become more accessible and reliable which paved the way for manufacturers to continuously monitor their equipment, collect and store large volume of data in their production systems. Using this data with machine learning (ML) algorithms and analyzing the fingerprints of equipment faults can help making more informed decision regarding maintenance in manufacturing which might help increasing production quality and capacity. In our study, induction motors (IM) which are widely used in factories for different purposes and their failure scenarios are targeted. Triaxial vibration data collected from two similar induction motors under different operating conditions are examined. Various features of vibration data are extracted, scaled and labeled with a status information of the operation state. The obtained dataset is analyzed with six different ML algorithms. Performances are examined and compared against each other. In this study, we present our promising experimental results and experimentally show that the abnormal operating conditions of IMs can be successfully detected utilizing ML algorithms for a PdM application.

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Review on Machine Learning Algorithm Based Fault Detection in Induction Motors

Cited by 107 publications

References 98 publications

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

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

Maki̇ne Öğrenmesi̇ Yaklaşimlari İle İndüksi̇yon Motorlari İçi̇n Akilli Hata Tespi̇ti̇ Ve Siniflandirmada Deneysel Bi̇r Değerlendi̇rme

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