Distinct Bearing Faults Detection in Induction Motor by a Hybrid Optimized SWPT and aiNet-DAG SVM

Abid, Firas Ben; Zgarni, Slaheddine; Braham, Ahmed

doi:10.1109/tec.2018.2839083

Cited by 60 publications

(20 citation statements)

References 20 publications

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“…One classical work on the use of SVM towards identifying bearing faults can be found in [47], where classification results obtained by the SVM are optimal in all of the cases, with an overall improvement over the performance of ANN. Other similar SVM based papers [48]- [60] also illustrated the effectiveness and efficiency of employing SVM to serve as the fault classifier.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 86%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

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Section: Support Vector Machines (Svm)mentioning

confidence: 86%

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

et al. 2020

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“…As a result, the latest FDD systems demand more artificial intelligent solutions to incorporate multiple fault events or dynamically changing load profiles in case of incomplete or noisy measurements [44][45][46][47][48][49]. Commonly, the diagnosis and predictions are calculated through motor current signature analysis (MCSA) [50,51], i.e., examining the output signals of the motor stator's current while running on a steady-state operating mood [52][53][54][55][56]. MCSA analyses the time-frequency decomposition of the current signals or by faults' frequencies in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

“…MCSA analyses the time-frequency decomposition of the current signals or by faults' frequencies in the frequency domain. MCSA works based on a single input source and representing a simple, low-cost and non-invasive monitoring method [50,57,58]. An enhanced method of MCSA in the case of multiphase electrical machines is called electrical signature analysis (ESA) [59].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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“…Most of the demonstrated work in stator winding fault detection is from a frequency analysis domain. Signal transforming methods like fast Fourier transform (FFT), S-transform, short-time Fourier transform (STFT), wavelet transform, and Hilbert transforms have been adopted in combination with various classification techniques such as expert systems, artificial neural network, fuzzy logic, and support vector machine [20][21][22][23][24][25][26] for the motor degradation.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

Rajamany

Srinivasan

Rajamany

et al. 2019

Journal of Electrical and Computer Engineering

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The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.

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Distinct Bearing Faults Detection in Induction Motor by a Hybrid Optimized SWPT and aiNet-DAG SVM

Cited by 60 publications

References 20 publications

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

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