Optimizing Machine Learning Algorithms for Predictive Maintenance in Industrial Systems in UK

Purpose: The purpose of this article was to analyze optimizing machine learning algorithms for predictive maintenance in industrial systems in UK Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Optimizing machine learning algorithms for predictive maintenance in industrial systems in the UK has shown significant potential in improving operational efficiency and reducing downtime. Key findings suggest that leveraging techniques like anomaly detection, supervised learning, and deep learning models can enhance the accuracy of predicting equipment failures. The integration of real-time data from sensors and IoT devices enables more proactive maintenance schedules, minimizing unplanned outages. Unique Contribution to Theory, Practice and Policy: Theory of predictive analytics, machine learning theory & systems theory may be used to anchor future studies on the optimizing machine learning algorithms for predictive maintenance in industrial systems in UK. In practice, industry practitioners should prioritize integrating machine learning models into real-time monitoring systems, which would allow for continuous updates and immediate responses to emerging failure risks. At the policy level, there is a clear need for the establishment of industry-wide standards for the implementation of machine learning in predictive maintenance.