A Meta-Heuristic Sustainable Intelligent Internet of Things Framework for Bearing Fault Diagnosis of Electric Motor under Variable Load Conditions

Swarnali Deb Bristi, Mehtar Jahin Tatha, Md. Firoj Ali, Uzair Aslam Bhatti (), Subrata K. Sarker (), Mehdi Masud, Yazeed Yasin Ghadi, Abdulmohsen Algarni and Dip K. Saha
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Swarnali Deb Bristi: Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Mehtar Jahin Tatha: Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Md. Firoj Ali: Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Uzair Aslam Bhatti: School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Subrata K. Sarker: Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Mehdi Masud: Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Yazeed Yasin Ghadi: Department of Computer Science, Al Ain University, Al Ain 15551, United Arab Emirates
Abdulmohsen Algarni: Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
Dip K. Saha: Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

Sustainability, 2023, vol. 15, issue 24, 1-25

Abstract: The study introduces an Intelligent Diagnosis Framework (IDF) optimized using the Grasshopper Optimization Algorithm (GOA), an advanced swarm intelligence method, to enhance the precision of bearing defect diagnosis in electrical machinery. This area is vital for the energy sector and IoT manufacturing, but the evolving designs of electric motors add complexity to fault identification. Machine learning offers potential solutions but faces challenges due to computational intensity and the need for fine-tuning hyperparameters. The optimized framework, named GOA-IDF, is rigorously tested using experimental bearing fault data from the CWRU database, focusing on the 12,000 drive end and fan end datasets. Compared to existing machine learning algorithms, GOA-IDF shows superior diagnostic capabilities, especially in processing high-frequency data that are susceptible to noise interference. This research confirms that GOA-IDF excels in accurately categorizing faults and operates with increased computational efficiency. This advancement is a significant contribution to fault diagnosis in electrical motors. It suggests that integrating intelligent frameworks with meta-heuristic optimization techniques can greatly improve the standards of health monitoring and maintenance in the electrical machinery domain.

Keywords: machine learning; bearing fault; intelligent framework; electric motor; variable load conditions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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