Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Hsueh, Yu-Min; Ittangihala, Veeresha Ramesha; Wu, Wenwang; Chang, Hong‐Chan; Kuo, Cheng‐Chien

doi:10.3390/sym11101212

Cited by 89 publications

(52 citation statements)

References 53 publications

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“…C. Lessmeier et al [47] employed wavelet packet decomposition (WPD) for feature extraction and then applied ensemble learning on the motor current signal and achieved an accuracy of 86.03%. A similar type of research conducted in [53] has proposed a fault detection method for induction motors utilizing empirical wavelet transform and CNN achieved 97.37% accuracy, which is also comparable with our result. In [54], authors used a vibration signal and CNN for fault detection and diagnosis, achieving accuracy between 88-99% for different ratios of data.…”

Section: Resultssupporting

confidence: 89%

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Toma

Prosvirin

Kim

2020

Sensors

165

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Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

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Section: Resultssupporting

confidence: 89%

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Toma

Prosvirin

Kim

2020

Sensors

165

View full text Add to dashboard Cite

show abstract

“…Information fusion (IF) and DL approaches were used [51] on a motor current signal, which result almost 98.3% accuracy. Another study on the same dataset applied an empirical wavelet transform and CNN for fault classification, showing 97.37% accuracy [52]. Therefore, by comparison with the recent research on the motor current signal, our approach provides a better result, with greater than 99% accuracy for ensemble classifiers.…”

Section: Accuracy (%)mentioning

confidence: 69%

“…WPD + SVM-PSO [50] 86.03 IF+MLP [51] 98.3 SVM+ IF [51] 98.0 KNN+ IF [51] 97.7 CNN + EWT [52] 97.3 DWT + XGBoost [This paper] 99.3…”

Section: Applied Methodologymentioning

confidence: 99%

“…After that, to estimate the performances, two ensemble learning algorithms, RF and XGBoost, were trained to classify. Finally, a comparison is made with some recent works on motor current signal, where Lessmeier et al [50] applied particle swarm optimization based support vector machine (SVM-PSO) and in [51,52], the authors used deep learning based approaches for IM fault classification.…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Toma

Kim

2020

Applied Sciences

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Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

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“…Authors [49] propounded a fault identification technique using CNN and FFT for bearing fault detection in induction motor. In [50], authors have propounded a bearing fault diagnosis of induction motor using CNN and empirical WT. Authors [51] investigated the bearing fault diagnosis using CNN model with the help of FFT analysis of vibration and utilizing root mean square data from the FFT analysis.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor

Kumar

Hati

2020

IET Electric Power Appl

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Early fault detection in an induction motor is the need of modern industries for minimal downtime and maximum production. A learning technique known as the Convolutional Neural network (CNN) provides automated and reliable feature extraction and selection. Considering these inherent traits of CNN, this study proposes a CNN in combination with batch normalisation (BN)‐based fault detection approach for simultaneous detection of bearing fault and broken rotor bars in squirrel cage induction motors (SCIMs). The SCIM vibration signals have different patterns for different defects, and the architecture of CNN is used in this study for fault diagnosis. For an efficient fault feature extraction, the proposed method uses CNN having multiple stacked layers with BN for faster training. In the proposed method, a CNN model with small kernel size is used along with adaptive gradient optimizer and BN to avoid performance degradation and optimum results. For the validation of the proposed technique, a test set‐up is used along with different fault conditions. The proposed method is also compared with the existing state‐of‐the‐art methods to illustrate its effectiveness.

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Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Cited by 89 publications

References 53 publications

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor

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