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Open AccessArticle

A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

1
Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
2
Research and Development Division, TOENEC Corporation, Nagoya 457-0819, Japan
*
Author to whom correspondence should be addressed.
Energies 2019, 12(11), 2105; https://doi.org/10.3390/en12112105
Received: 30 April 2019 / Revised: 26 May 2019 / Accepted: 30 May 2019 / Published: 1 June 2019
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors)
Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use. View Full-Text
Keywords: induction motor; minor bearing fault; frequency spectrum analysis; machine learning algorithm; artificial intelligence induction motor; minor bearing fault; frequency spectrum analysis; machine learning algorithm; artificial intelligence
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MDPI and ACS Style

Esakimuthu Pandarakone, S.; Mizuno, Y.; Nakamura, H. A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors. Energies 2019, 12, 2105.

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