ANN Assisted Multi Sensor Information Fusion for BLDC Motor Fault Diagnosis

Shifat, Tanvir Alam; Hur, Jin

doi:10.1109/access.2021.3050243

Cited by 73 publications

(41 citation statements)

References 34 publications

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“…It is common practice to capture as many features as possible [30]. This ensures that the underlying characteristics in the signals are well represented from diverse perspectives while also capturing the time-dependent information in the signals.…”

Section: Feature Name Definitionmentioning

confidence: 99%

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

2021

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The ability of a switch-mode AC/DC power supply to shrink supplies is a benefit and a requirement for most electronic devices with limited space. Major failures in switch-mode power supply (SMPS) during adverse working conditions are subject to mostly the switching devices and capacitors. For effective condition monitoring of the SMPS, dual (or multiple) sensing provides a more reliable standpoint against the traditional single sensing techniques as it provides a more comprehensive paradigm for accurate condition monitoring. This study proposes an integrated approach to SMPS condition monitoring by exploiting statistically extracted features from current and voltage signals for system fault diagnosis based on electrical stress. Following a correlation-based feature selection approach, salient features were utilized for improved fault detection and isolation (FDI) using ML-based classifiers. Diagnostic results by the classifiers reveal that the random forest and gradient boosting classifiers are highly reliable but computationally expensive when compared with the others while the decision tree was quite cost-efficient with reliable diagnostic results. The proposed framework is effectively applicable for use in diagnosing the switching devices and classification at different states.

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Section: Feature Name Definitionmentioning

confidence: 99%

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

2021

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“…The usage of the above-mentioned methods can minimize human participation in fault diagnosis and help in automating this process. Therefore, the usage of selected MLbased classifiers, shallow and deep neural networks, has been verified to detect various types of electric motor faults [10,[35][36][37][38][39][40][41][42][43][44][45]. Taking into account an electric motor fault other than mechanical failure, there are still very few scientific papers in which the usage of simple machine learning algorithms to detect PMSM stator winding faults is presented, especially taking into account the analysis of the key parameter selection of fault classifiers on their effectiveness.…”

Section: Introductionmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

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“…Mechanical faults include air-gap deformation, bearing failures, shaft misalignment, and mechanical imbalance, as presented in the literature [ 4 , 5 , 6 ]. Furthermore, electrical faults usually include the stator, rotor, and electrical supply faults, which were analyzed in [ 7 , 8 , 9 ]. Various diagnostic techniques for mechanical systems have been presented in the literature [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Wang

Kao

Perng

2021

Sensors

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The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

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ANN Assisted Multi Sensor Information Fusion for BLDC Motor Fault Diagnosis

Cited by 73 publications

References 34 publications

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

An Integrated Cost-Aware Dual Monitoring Framework for SMPS Switching Device Diagnosis

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

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