Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Muhammad Zuhaib, Faraz Ahmed Shaikh, Wajiha Tanweer, Abdullah M. Alnajim (), Saleh Alyahya, Sheroz Khan, Muhammad Usman, Muhammad Islam and Mohammad Kamrul Hasan
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Muhammad Zuhaib: Department of Electrical Engineering, PNEC, National University of Science and Technology, Karachi 75350, Pakistan
Faraz Ahmed Shaikh: Department of Electrical Engineering, Nazeer Hussain University (NHU), Karachi 75950, Pakistan
Wajiha Tanweer: Department of Electrical Engineering, PNEC, National University of Science and Technology, Karachi 75350, Pakistan
Abdullah M. Alnajim: Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
Saleh Alyahya: Department of Electrical Engineering, College of Engineering & Information Technology, Onaizah Colleges, Unayzah 51911, Saudi Arabia
Sheroz Khan: Department of Electrical Engineering, College of Engineering & Information Technology, Onaizah Colleges, Unayzah 51911, Saudi Arabia
Muhammad Usman: Department of Mechatronics and Control Engineering, Faisalabad Campus, University of Engineering and Technology (UET), Lahore 39161, Pakistan
Muhammad Islam: Department of Electrical Engineering, College of Engineering & Information Technology, Onaizah Colleges, Unayzah 51911, Saudi Arabia
Mohammad Kamrul Hasan: Center for Cyber Security, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia

Energies, 2022, vol. 15, issue 21, 1-32

Abstract: Motivation: This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%.

Keywords: induction motors; MCSA; DWT; ANN; IoT security; resources availability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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