Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors

Tran, Minh-Quang; Liu, Mengkun; Tran, Quoc-Viet; Nguyen, Toan-Khoa

doi:10.1109/tim.2021.3139706

Cited by 59 publications

(30 citation statements)

References 42 publications

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“…It can be seen that the proposed NBCNN method effectively accelerates the model training procedure. This subsection validates the fault diagnosis performance of the NBCNN model in comparison with three conventional methods, i.e., CNN without noise injection, 1D-CNN [37], and CANN [22]. In the 1D-CNN method, the raw vibration signal is directly inputted into the model.…”

Section: ) Nbcnn Improves Fault Diagnosis Accuracymentioning

confidence: 66%

Noise-Boosted Convolutional Neural Network for Edge-Based Motor Fault Diagnosis With Limited Samples

Huang

et al. 2023

IEEE Trans. Ind. Inf.

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Convolutional neural networks (CNNs) have been widely applied in motor fault diagnosis. However, to obtain high recognition accuracy, massive training data are typically required and transmitted to the cloud/local server for training, which may suffer from security and privacy problems. In this study, a noise-boosted CNN (NBCNN) model is developed to achieve accelerated training and improved recognition accuracy with limited training samples. First, the NBCNN model with a noise-injection fully connected layer is established. Then, a strategy for noise selection and injection is proposed to obtain an optimal matching among the data, model, and noise. Finally, the optimal injected noise accelerates the convergence of model training and improves the accuracy of motor fault diagnosis. Compared with the conventional CNN without noise injection and the state-of-the-art models, the effectiveness and superiority of the proposed NBCNN model are validated by two benchmark datasets. In addition, the algorithm is deployed onto an edge device and the results show that the training speed of the developed NBCNN can reach nine times faster than the conventional CNN. The proposed method shows remarkable potential for distributed model training, federal learning, and real-time motor fault diagnosis.

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Section: ) Nbcnn Improves Fault Diagnosis Accuracymentioning

confidence: 66%

Noise-Boosted Convolutional Neural Network for Edge-Based Motor Fault Diagnosis With Limited Samples

Huang

et al. 2023

IEEE Trans. Ind. Inf.

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“…The first one is the convolutional attention neural network (CANN) method [30]. This method proposed by Tran et al achieved higher fault diagnosis accuracy for IM diagnosis by combining the continuous wavelet transform with a CANN model.…”

Section: A > < Introduction Of Comparative Methodsmentioning

confidence: 99%

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Zhu

Wang

et al. 2023

IEEE Internet Things J.

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Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99 %. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

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“…Different types of wavelet transforms have been applied as data-to-image conversion techniques which mostly produce two-dimensional images and will later be used as the input of classifiers [43,49]. In [50], the Morlet function was applied in CWT to convert the time-series vibration signal to scalogram images. It was also used as the input of the convolutional attention neural network (CANN) to classify motor faults.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Toma

Piltan

et al. 2022

Sensors

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Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.

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Effective Fault Diagnosis Based on Wavelet and Convolutional Attention Neural Network for Induction Motors

Cited by 59 publications

References 42 publications

Noise-Boosted Convolutional Neural Network for Edge-Based Motor Fault Diagnosis With Limited Samples

Noise-Boosted Convolutional Neural Network for Edge-Based Motor Fault Diagnosis With Limited Samples

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

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