Predictive maintenance of industrial equipment using machine learning in industrial environment of Awka Metropolis, Nigeria
1 Department of Computer Science, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
2 Department of Mechanical Engineering, First Technical University, Ibadan, Oyo State, Nigeria.
3Department of Science Education, Nnamdi Azikiwe University Awka, Anambra state. Nigeria.
4 Department of Civil Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria.
5 Department of Physics and Electronics, Institute of Management and Technology, Enugu, Enugu State Nigeria.
6 Institute of Geology and Petroleum Technologies, Kazan Federal University, Kazan, Russia.
7 Department of Petroleum Engineering, University of Port Harcourt, River State, Nigeria.
Review
International Journal of Science and Technology Research Archive, 2023, 05(01), 001–009.
Article DOI: 10.53771/ijstra.2023.5.2.0080
Publication history:
Received on 26 August 2023; revised on 13 October 2023; accepted on 16 October 2023
Abstract:
Failures of industrial equipment may lead to substantial operational interruptions and financial losses for enterprises. A viable solution to reduce such issues includes the deployment of machine learning-driven predictive maintenance. This study dives into the implementation of predictive maintenance in the particular industrial environment of Awka Metropolis, Nigeria. This research involves the design of a comprehensive approach, involving the collecting and preparation of data, along with the application of machine learning models. The core of our predictive maintenance approach resides in past equipment performance data, combined with sensor-generated data. Various machine learning methods, including decision trees, random forests, and recurrent neural networks, are used to anticipate future equipment faults. The study findings illustrate the usefulness of the predictive maintenance model in properly identifying approaching equipment problems, even under the specific circumstances of Awka Metropolis. Evaluation criteria such as precision, recall, and accuracy support the robustness of the model, underlining its trustworthiness. The paper also tackles the practical obstacles found during implementation, giving insights into their resolution, especially within parallel industrial situations. The findings underline the potential for cost reductions and heightened operational efficiency within regional industries by implementing proactive maintenance practices. Furthermore, the report suggests avenues for future inquiry and underlines the applicability of the model to varied businesses and geographical areas. The successful implementation of predictive maintenance in Awka Metropolis provides local firms a chance to boost equipment reliability while lowering downtime, so making major contributions to economic development and sustainability. As companies progressively embrace digital transformation, this study serves as a significant resource for practitioners and scholars alike, seeking to improve equipment maintenance in rising markets.
Keywords:
Predictive Maintenance; Industrial Equipment; Machine Learning; Awka Metropolis; Nigeria; Sensor Data
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0