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Open Access Journal Article

Machine Learning Techniques for Predictive Maintenance in Industrial IoT

by Michael Smith 1,*
1
Michael Smith
*
Author to whom correspondence should be addressed.
ISTI  2022, 33; 4(2), 33; https://doi.org/10.69610/j.isti.20221021
Received: 17 August 2022 / Accepted: 22 September 2022 / Published Online: 21 October 2022
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Abstract

This paper explores the integration of machine learning techniques in predictive maintenance within the context of Industrial Internet of Things (IIoT). With the increasing complexity and scale of industrial systems, maintaining their operational reliability has become a critical challenge. Predictive maintenance aims to proactively identify potential equipment failures before they occur, thereby minimizing downtime and maintenance costs. This abstract discusses the application of machine learning algorithms to analyze data collected from various sensors deployed in IIoT environments. The focus is on methods that can effectively predict equipment failures based on historical data and real-time monitoring. The study evaluates different machine learning approaches, including supervised learning, unsupervised learning, and hybrid models, to determine their suitability for predictive maintenance tasks. Furthermore, it examines the challenges faced during the implementation of these techniques and proposes strategies to address them. The findings indicate that machine learning holds great potential for enhancing the predictive maintenance process in IIoT by enabling better decision-making, optimizing maintenance schedules, and increasing the overall efficiency of industrial systems.

 

Keywords: Machine Learning, Predictive Maintenance, Industrial Internet of Things (IIoT), Supervised Learning, Unsupervised Learning.;
Copyright: © 2022 by Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Smith, M. Machine Learning Techniques for Predictive Maintenance in Industrial IoT. Information Sciences and Technological Innovations, 2022, 4, 33. https://doi.org/10.69610/j.isti.20221021
AMA Style
Smith M. Machine Learning Techniques for Predictive Maintenance in Industrial IoT. Information Sciences and Technological Innovations; 2022, 4(2):33. https://doi.org/10.69610/j.isti.20221021
Chicago/Turabian Style
Smith, Michael 2022. "Machine Learning Techniques for Predictive Maintenance in Industrial IoT" Information Sciences and Technological Innovations 4, no.2:33. https://doi.org/10.69610/j.isti.20221021
APA style
Smith, M. (2022). Machine Learning Techniques for Predictive Maintenance in Industrial IoT. Information Sciences and Technological Innovations, 4(2), 33. https://doi.org/10.69610/j.isti.20221021

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References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Arulmozhi, V., Viswanathan, R., & Renganathan, R. (2018). A review on the application of machine learning techniques for predictive maintenance of rotating machines. In Proceedings of the 2018 International Conference on Industrial and System Engineering (ICISE) (pp. 1-6).
  3. Fawcett, T., Provost, F., & Kohavi, R. (2018). An introduction to predictive analytics. In K. W. Church, P. A. Flach, & N. D. Lawrence (Eds.), Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (pp. 1-24). John Wiley & Sons.
  4. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.
  5. Ghasemzadeh, H., Talebi, H., & Seyed Hosseini, S. M. (2011). An analysis of vibration data based on K-means clustering and its application in bearing fault detection. Journal of Vibration and Control, 17(10), 1289-1303.
  6. Japkowicz, N., & Shah, M. (2000). Evaluating classification model selection criteria: An empirical comparison. Machine Learning, 42(2), 123-151.
  7. K-means, J. (1967). Optimization and its application to clustering. Journal of the American Statistical Association, 62(318), 583-618.
  8. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Journal of Artificial Intelligence, 8(1), 1-27.
  9. Mousavi, S. M., & Najafabadi, M. M. (2016). A survey on machine learning techniques for predicting equipment failures in manufacturing systems. Expert Systems with Applications, 43(11), 5640-5668.
  10. Wang, X., Han, Y., & Zhang, H. (2018). A review of data-driven predictive maintenance: Methods, cases, and future directions. IEEE Transactions on Industrial Informatics, 14(1), 24-35.
  11. Wang, X., Wang, R., Zhang, Y., & Wang, Y. (2013). A novel method for predictive maintenance of diesel engine based on decision tree. In Proceedings of the 2013 International Conference on Machine Learning and Cybernetics (pp. 381-386).
  12. Zeng, Y., Li, Y., & Wang, J. (2017). A hybrid method of machine learning for remaining useful life prediction of rotating machinery. Expert Systems with Applications, 89, 233-242.