Machine Learning Models for Predictive Maintenance in Industrial Engineering

Abstract

Purpose: The general objective of this study was to investigate machine learning models for predictive maintenance in industrial engineering.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning models for predictive maintenance in industrial engineering. The research highlighted the transformative potential of machine learning models in optimizing predictive maintenance for industrial engineering, demonstrating significant reductions in unplanned downtime and maintenance costs. It identified the strengths of various machine learning approaches, such as supervised, unsupervised, and reinforcement learning, in predicting equipment failures and optimizing maintenance schedules. Despite the benefits, challenges such as data quality, integration complexity, and the need for specialized skills were noted. Future advancements in machine learning, IoT data, and computational power were expected to further enhance predictive maintenance systems, making them more accurate, efficient, and widely adopted across industries.
Unique Contribution to Theory, Practice and Policy: The Systems Theory, Diffusion of Innovations Theory and Resource-Based View (RBV) Theory may be used to anchor future studies on machine learning models for predictive maintenance in industrial engineering. This study provided several recommendations that contributed to theory, practice, and policy. It emphasized the development of hybrid machine learning models, integration of domain-specific knowledge, and real-time data collection using IoT technologies. It suggested standardized data protocols and personnel training for better implementation and efficiency. Policy recommendations included regulatory frameworks, incentives for technology adoption, data sharing, and robust data privacy guidelines. These contributions aimed to enhance the accuracy and applicability of predictive maintenance models, improve industrial maintenance practices, and support technological innovation through supportive policies.