Machine Learning Algorithms for Predictive Maintenance in Manufacturing

Purpose: This study sought to explore machine learning algorithms for predictive maintenance in manufacturing. 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 algorithms for predictive maintenance in manufacturing. Preliminary empirical review revealed that the integration of advanced machine learning techniques notably improved the effectiveness of predictive maintenance strategies. The research demonstrated that sophisticated models, such as deep learning and ensemble methods, provided superior accuracy in predicting equipment failures and optimizing maintenance schedules. It also highlighted the importance of data quality and real-time monitoring for enhancing predictive capabilities. Despite these advancements, the study identified challenges related to computational resources and implementation complexity, suggesting that overcoming these barriers is crucial for fully leveraging the benefits of machine learning technologies in manufacturing. Unique Contribution to Theory, Practice and Policy: The Theory of Predictive Analytics, Theory of Machine Learning Classification and Theory of Anomaly Detection may be used to explore machine learning algorithms for predictive maintenance in manufacturing. The study recommended several actions for enhancing the application of machine learning algorithms in predictive maintenance. It advised practitioners to adopt advanced algorithms like Neural Networks and ensemble methods, invest in high-quality data collection, and address challenges such as computational demands and model complexity. For policymakers, it suggested developing frameworks to support the effective implementation of these technologies while addressing data privacy and cybersecurity concerns. The study also highlighted the need for further research into hybrid approaches, technology integration, and organizational factors to improve predictive maintenance outcomes and drive future advancements in the field.