Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

Abstract

This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN). First, this study compares two feature-extraction methods: the MRA and the Hilbert Huang transform (HHT) for induction-motor-current signature analysis. Furthermore, feature-selection methods are compared to reduce the number of features and maintain the best accuracy of the detection system to lower operating costs. Finally, the proposed detection system is tested with additive white Gaussian noise, and the signal-processing method and feature-selection method with good performance are selected to establish the best detection system. According to the results, features extracted from MRA can achieve better performance than HHT using CFFS and ANN. In the proposed detection system, CFFS significantly reduces the operation cost (95% of the number of features) and maintains 93% accuracy using ANN.

Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

“…Hence, the energy will be preserved through early fault deletion using this proposed method during the operations. ANN [14] Faster-RCNN [17] Multiscale Entropy and SVM [13]…”

“…Conversely, the authors determined only on rolling bearing failures. Many existing methods have used the ANN and SVM (Support Vector Machine) classifier for the induction motor fault classification [13][14][15]. Agrawal and Jayaswal [16] proposed a comparative analysis between SVM and ANN by applying the energy entropy models and the continuous wavelet transforms to detect and classify the rolling component bearings.…”

Diagnosing of BLDC Motor Faults based on LSSVM Model and Vibration Signal

“…It can be replaced by a relatively simple computational method that does not necessitate a thorough grasp of system behaviour. Artificial neural networks (ANN) [15][16][17][18], random forests [19,20], support vector machines (SVM) [21][22][23][24][25][26], and deep learning [27][28][29] are the most commonly utilised methodologies in defect identification. ANN and SVM have received the most attention from researchers among these methods.…”

Editorial for the Special Issue “Advances in Machine Learning and Mathematical Modeling for Optimization Problems”

“…In recent years, various signal-processing methods have received much attention in the problem of fault-detection systems. In the article by Lee et al [7], "Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS", the authors propose a fault-detection system for faulty induction motors (bearing faults, inter-turn shorts, and broken rotor bars) based on a multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN).…”