Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors

Abstract

<jats:p>Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.</jats:p>

Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

“…Different tools have been proposed to overcome these issues and improve the reliability of fault diagnostic methods based on transient analysis. These tools include adaptive transforms [15,16], non-linear signal decompositions [17,18], demodulation schemes [19][20][21], statistical methods [22][23][24], intelligent algorithms [25][26][27][28], and combined techniques. However, all these methodologies focus on the analysis of the stator current signal.…”

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

“…Nevertheless, the identification of relevant features into the EMG signals represent a challenging task in associating them with stress detection [14]. For example, Katsis et al [26] On the other hand, it should be noted that with the development of deep learning frameworks, there is a new and updated version of machine learning [12], different applications for detecting pedestrians [28] or processing medical images for detecting mammographic lesions [29] have been recently proposed. The former uses a body part-detector for a convolutional neural network-based classifier, reporting a reduction of the misclassification error of about 10%, whereas the latter used a modified version of the VGG neural network to perform the lesion detection using contrast, patient information, texture, and On the other hand, it should be noted that with the development of deep learning frameworks, there is a new and updated version of machine learning [12], different applications for detecting pedestrians [28] or processing medical images for detecting mammographic lesions [29] have been recently proposed.…”

“…For example, Katsis et al [26] On the other hand, it should be noted that with the development of deep learning frameworks, there is a new and updated version of machine learning [12], different applications for detecting pedestrians [28] or processing medical images for detecting mammographic lesions [29] have been recently proposed. The former uses a body part-detector for a convolutional neural network-based classifier, reporting a reduction of the misclassification error of about 10%, whereas the latter used a modified version of the VGG neural network to perform the lesion detection using contrast, patient information, texture, and On the other hand, it should be noted that with the development of deep learning frameworks, there is a new and updated version of machine learning [12], different applications for detecting pedestrians [28] or processing medical images for detecting mammographic lesions [29] have been recently proposed. The former uses a body part-detector for a convolutional neural network-based classifier, reporting a reduction of the misclassification error of about 10%, whereas the latter used a modified version of the VGG neural network to perform the lesion detection using contrast, patient information, texture, and geometrical features, obtaining an area-under-the-curve (AUC) of 0.94 (the closer to 1, the better the classifier).…”

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

“…Since it considers the structure and spatial information between different sensor measurement points, the fault diagnosis with high accuracy and speed is realized. Valtierra-Rodriguez et al [ 9 ] proposed a methodology based on convolutional neural networks for automatic detection of broken rotor bars by considering different severity levels. This method applies a notch filter to remove the fundamental frequency component of the current signal, and the short-time Fourier transform (STFT) is used to obtain time-frequency plane.…”