An Optimized Deep Learning Approach for Predicting the Electric Motor Temperature Using IOT Sensors

Abstract

Based on that, the Internet of Things (IoT) is used in industrial applications for monitoring and controlling various sensor operations. In existing work, IoT-based monitoring and controlling operations for industries are proposed. But, in this, real-time monitoring of the data is performed to take necessary actions. This may fail at a fraction of a second when the device crossed its breakpoint or threshold value. Hence, in this, an optimized deep learning approach (Convolutional Neural Network) is proposed for monitoring and controlling the temperature in electrical motors. Here, the controlling is performed by predicting the temperature using a deep learning approach. This helps to improve the controlling operations in the IoT environment and protect the device from Malfunctioning. The proposed approach is tested on the Kaggle Sensor dataset for electrical motors. The optimal hyperparameters for the CNN are determined through the hybrid particle swarm and genetic algorithm by minimizing the cost function. The cost function is to reduce the RMSE rate. This method’s presentation is evaluated and the fidelity of root mean square merits. The whole process is implemented using MATLAB R2020a version under Windows 10 environment.

Keywords:

• electrical drive
• motor
• sensors
• temperature monitoring
• optimization
• deep learning
• controlling
• IoT sensors

ACKNOWLEDGMENT

There is no acknowledgement applicable in this work.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

No participation of humans takes place in this implementation process.

HUMAN AND ANIMAL RIGHTS

No violation of Human and Animal Rights is involved.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

AUTHORS’ CONTRIBUTIONS

All authors are contributed equally to this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

No funding is involved in this work.

Notes on contributors

Mayapandi Mokkamayan

Mayapandi Mokkamayan was born in Nagapattinam, India in April 1978. He received the National Trade Certificate (NTC) Wireman Trade in Industrial Training Institute (I.T.I), Nagapattinam, India in 1997. He obtained his Diploma in Electrical and Electronics Engineering from Valivalam Desikar Ploy technique, Nagapattinam, India in 2003. He obtained his B.Tech degree in Electrical and Electronics Engineering from SASTRA University, Thanjavur, India in 2006. He obtained his M.Tech degree in Power Electronics and Drives from PRIST University, Thanjavur, India in 2009. Currently he is working as Junior Training Officer in Government Industrial Training Institute, Ariyalur, India. He is having more than seven years of teaching experience as an Assistant Professor in Engineering College and five years in Industrial Training Institute and till date. His current research interests include power electronic converters, artificial intelligence (AI) applications in power electronic systems and electric vehicles etc.

Suresh Padmanabhan Thankappan

Suresh Padmanabhan Thankappan was born in Nagercoil, India in June 1980. He received the B.E degree in Electrical and Electronics Engineering from Noorul Islam College of Engineering, affiliated to Manonmaniam Sundaranar University, Tirunelveli, India, in 2001. He obtained his M.E degree in Power Electronics and Industrial Drives from Sathyabama Institute of Science and Technology, Chennai, India, in 2005. He obtained Ph.D. degree in Electrical and Electronics Engineering from Pondicherry University, Pondicherry, India. Currently he is working as Professor and Head in Electrical and Electronics Engineering department. He is having more than eighteen years of teaching experience. In addition, he has authored/co-authored in several international journals and conferences. His current research interests include power electronic converters, artificial intelligence (AI) applications in power electronic systems and electric vehicles etc.