Comparison of Different Machine Learning Algorithms for Predictive Maintenance

It is common to utilize manufacturing equipment without a clear maintenance plan. Such a method typically results in unplanned downtime due to unforeseen breakdowns. By replacing parts frequently as part of scheduled maintenance, unplanned equipment failures are avoided. However, this results in more downtime and more expensive maintenance. Predictive maintenance helps avoiding such circumstances on prior basis for smooth functioning of industry. Predictive maintenance strategies that assist lower the cost of downtime and raise the availability (utilization rate) of industrial equipment are getting more attention In this paper study of AI-based algorithms for preventative maintenance keep an eye on two essential parts of machine systems: machine failure and the quality of tools. A data-driven modelling approach will be described for the investigation of tool wear and bearing failures.

I. Introduction

The process of predictive maintenance involves adjusting, cleaning, and replacing parts to stop defects from developing. The lifespan of a machine, an animal, or any other creature can be forecast via predictive maintenance. Depending on the information received from various condition monitoring sensors and procedures, specific actions must be made. Achieving predictive maintenance can be done by Planned maintenance necessitates extensive human oversight and interaction. Failure of the machine has an economic impact on the company. Recognizing Abnormal Behaviors: The foundation of predictive maintenance is the ability to recognize anomalous behavior using specific machine learning approaches and deep learning algorithms. There is hardly any human involvement in this procedure. The forecast is made using the provided data set, and specific actions must be taken as a result.