Design of an Effective Refrigeration System with Predictive Maintenance by Integrating IoT and Machine Learning

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : Abhijit L Dandavate, Arati D.K., Manisha Mehrotra, Vishvas V Kalunge, Archana Priyadarshni, Supriya Agre

10.14445/23488549/IJECE-V11I12P113

How to Cite?

Abhijit L Dandavate, Arati D.K., Manisha Mehrotra, Vishvas V Kalunge, Archana Priyadarshni, Supriya Agre, "Design of an Effective Refrigeration System with Predictive Maintenance by Integrating IoT and Machine Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 12, pp. 135-145, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I12P113

Abstract:

As the refrigeration industry grows quickly, essential components such as the compressor —the heart of the system— are highly challenging to maintain. Reactive maintenance, the conventional approach, often results in expensive downtime and surprise repairs. This study examined the potential to improve asset management in refrigeration systems leveraging IoT-supported predictive maintenance. Using IoT sensors and data analytics, the system monitors parameters, including energy consumption, pressure, and temperature, to identify early signs of malfunction. Maintenance can be addressed early, and systems can be maintained without downtime. The sustainability impact itself is also a core area within this discussion, leading into a deeper debate around particular metrics and methodologies - including metrics such as energy consumption, reduction in the carbon footprint of production and operational processes, as well as cost savings - that can be used to gauge and quantify the aforementioned benefits. This has many advantages, such as better energy efficacy, reduced maintenance cost, system reliability, etc. In future work, more sophisticated machine learning models will be incorporated to enhance predictive capability further, leading to increased efficiency and sustainability of refrigeration systems.

Keywords:

Predictive maintenance, Internet of Things (IoT), Refrigeration systems, Machine Learning, Asset management.

References:

[1] Mark O. McLinden, and Marcia L. Huber, “(R)Evolution of Refrigerants,” Journal of Chemical & Engineering Data, vol. 65, no. 9, pp. 4176-4193, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Lucas W. Davis ldavis, and Paul J. Gertler, “Contribution of Air Conditioning Adoption to Future Energy Use Under Global Warming,” Proceedings of the National Academy of Sciences, vol. 112, no. 19, pp. 5962-5967, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jin-Sik Oh et al., “Improving the Energy Efficiency of Industrial Refrigeration Systems,” Energy, vol. 112, pp. 826-835, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[4] M.S. Fernandes et al., “Review and Future Trends of Solar Adsorption Refrigeration Systems,” Renewable and Sustainable Energy Reviews, vol. 39, pp. 102-123, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Kedar Kulkarni et al., “Predictive Maintenance for Supermarket Refrigeration Systems using only Case Temperature Data,” 2018 Annual American Control Conference (ACC), Milwaukee, WI, USA, pp. 4640-4645, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Shohin Aheleroff et al., “IoT-Enabled Smart Appliances under Industry 4.0: A Case Study,” Advanced Engineering Informatics, vol. 43, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ameeth Kanawaday, and Aditya Sane, “Machine Learning for Predictive Maintenance of Industrial Machines using IoT Sensor Data,” 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, pp. 87-90, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Tullio Facchinetti, Marco Arazzi, and Antonino Nocera, “Time Series Forecasting for Predictive Maintenance of Refrigeration Systems,” 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Tarun Bansal et al., “AI based Diagnostic Service for IOT Enabled Smart Refrigerators,” 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy, pp. 163-168, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Kahiomba Sonia Kiangala, and Zenghui Wang, “An Effective Predictive Maintenance Framework for Conveyor Motors using Dual Time-Series Imaging and Convolutional Neural Network in an Industry 4.0 Environment,” IEEE Access, vol. 8, pp. 121033-121049, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Sule Selcuk, “Predictive Maintenance, its Implementation, and Latest Trends,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 231, no. 9, pp. 1670-1679, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jingyang Wang et al., “NGCU: A New RNN Model for Time-Series Data Prediction,” Big Data Research, vol. 27, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Mario Pérez-Gomariz, Antonio López-Gómez, and Fernando Cerdán-Cartagena, “Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review,” Clean Technologies, vol. 5, no. 1, pp. 116-136, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Rakshitha Godahewa et al., “A Generative Deep Learning Framework Across Time Series to Optimize the Energy Consumption of Air Conditioning Systems,” IEEE Access, vol. 10, pp. 6842-6855, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Chonggang Zhou et al., “Using Long Short-Term Memory Networks to Predict Energy Consumption of Air-Conditioning Systems,” Sustainable Cities and Society, vol. 55, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Jian Qi Wang, Yu Du, and Jing Wang, “LSTM based Long-Term Energy Consumption Prediction with Periodicity,” Energy, vol. 197, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Nicolò Oreste Pinciroli Vago, Francesca Forbicini, and Piero Fraternali, “Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study,” Machines, vol. 12, no. 6, pp. 1-28, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Francesca Forbicini, “Predicting Failures in Industrial Compressor-Based Machines,” Master’s Thesis, School of Industrial and Information Engineering, pp. 1-114, 2022.
[Google Scholar] [Publisher Link]
[19] Dimitris Mourtzisa, John Angelopoulosa, and Nikos Panopoulos, “Design and Development of an IoT Enabled Platform for Remote Monitoring and Predictive Maintenance of Industrial Equipment,” Procedia Manufacturing, vol. 54, pp. 166-171, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Tianwen Zhu et al., “A Survey of Predictive Maintenance: Systems, Purposes and Approaches,” arXiv, pp. 1-38, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jihong Yan et al., “Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance,” IEEE Access, vol. 5, pp. 23484-23491, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[22] R. van de Sand, “A Predictive Maintenance Model for Heterogeneous Industrial Refrigeration Systems,” Doctoral Thesis, Tor Vergata University of Rome, pp. 1-154, 2021.
[Google Scholar] [Publisher Link]
[23] Nelson Sierra, and Felipe Gonzalez, “Development of a Low Cost IOT Device Focused on Protection and Monitoring Compressors in Refrigeration and Air-Conditioning,” Journal of Embedded Systems, vol. 6, no. 1, pp. 1-8, 2023.
[Google Scholar] [Publisher Link]
[24] Aysegul Ucar, Mehmet Karakose, and Necim Kırımça, “Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends,” Applied Sciences, vol. 14, no. 2, pp. 1-40, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Kun Liang, “A Review of Linear Compressors for Refrigeration,” International Journal of Refrigeration, vol. 84, pp. 253-273, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Ahryman Seixas Busse de Siqueira Nascimento et al., “Evaporating Temperature Estimation of Refrigeration Systems based on Vibration Data-Driven Soft Sensors,” International Journal of Refrigeration, vol. 168, pp. 288-296, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Jui-Sheng Chou, Yu-Chien Hsu, and Liang-Tse Lin, “Smart Meter Monitoring and Data Mining Techniques for Predicting Refrigeration System Performance,” Expert Systems with Applications, vol. 41, no. 5, pp. 2144-2156, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[28] D.S. Adelekan, O.S. Ohunakin, and B.S. Paul, “Artificial Intelligence Models for Refrigeration, Air Conditioning and Heat Pump Systems,” Energy Reports, vol. 8, pp. 8451-8466, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Shrishti Trivedi et al., “Predictive Maintenance of Air Conditioning Systems using Supervised Machine Learning,” 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP), New Delhi, India, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Thomas Pursche et al., “Identification of Overtemperature Disturbances in Industrial Food Refrigeration Processes,” 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, Japan, pp. 636-641, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Dasheng Lee, Ming-Hsiang Chen, and Guan-Wei Lai, “Achieving Energy Savings through Artificial-Intelligence-Assisted Fault Detection and Diagnosis: Case Study on Refrigeration Systems,” Case Studies in Thermal Engineering, vol. 40, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Raid Al-Aomar, Marah AlTal, and Jochen Abel, “A Data-Driven Predictive Maintenance Model for Hospital HVAC System with Machine Learning,” Building Research & Information, vol. 52, no. 1-2, pp. 207-224, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Luigi Salmaso et al., “Design of Experiments and Machine Learning to Improve Robustness of Predictive Maintenance with Application to A Real Case Study,” Communications in Statistics-Simulation and Computation, vol. 51, no. 2, pp. 570-582, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Tim Salsbury, Prashant Mhaskar, and S. Joe Qin, “Predictive Control Methods to Improve Energy Efficiency and Reduce Demand in Buildings,” Computers & Chemical Engineering, vol. 51, pp. 77-85, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Xiaohui She et al., “Energy-Efficient and-Economic Technologies for Air Conditioning with Vapor Compression Refrigeration: A Comprehensive Review,” Applied Energy, vol. 232, pp. 157-186, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Zeki Murat Çınar et al., “Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0,” Sustainability, vol. 12, no. 19, pp. 1-42, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Simon Elias Bibri, “The IoT for Smart Sustainable Cities of the Future: An Analytical Framework for Sensor-Based Big Data Applications for Environmental Sustainability,” Sustainable Cities and Society, vol. 38, pp. 230-253, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Yunli Wang, Chunsheng Yang, and Weiming Shen, “A Deep Learning Approach for Heating and Cooling Equipment Monitoring,” 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada, pp. 228-234, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Jamal S. Rahhal, and Dia Abualnadi, “IOT Based Predictive Maintenance using LSTM RNN Estimator,” 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Luca Bergonzi et al., “Data and Knowledge in IIoT-Based Maintenance Application,” ASME International Mechanical Engineering Congress and Exposition, pp. 1-9, 2017.
[CrossRef] [Google Scholar] [Publisher Link]