Predictive Maintenance in Industrial Systems Using Machine Learning

Abstract

Now, a lot of different areas need predictive maintenance (PdM). The goal is to cut down on downtime and make work go faster by finding out when things will break. This study looks at how machine learning can be used to figure out when to fix manufacturing systems. The study is all about using old business records, monitoring data, and upkeep records to make good prediction models. To make prediction tools that can quickly and accurately find places where industrial machinery might break down, we plan to carefully use advanced machine learning techniques such as supervised learning, time series analysis, and anomaly detection. Our idea could make it easier to stick to repair plans. Breakdowns would happen less often, and overall, running costs would go down in many fields. To prove that our expected method for maintenance works and can be used in the real world, we use careful case studies and thorough empirical validations. This research is a big step toward making models for planned maintenance, giving ways for proactive maintenance, and improving the dependability and efficiency of industrial systems in the real world.