Predictive maintenance of industrial equipment using machine learning in industrial environment of Awka Metropolis, Nigeria

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.