Predictive Maintenance is attracting a great deal of interest, as it is beneficial to detect anomalies and possible defects in the equipment before they fail. To do so, we can use Machine learning models to analyze the data patterns and predict the equipment maintenance status. In this study, we aim to predict an industrial machine's maintenance status with the help of air temperature, process temperature, relational speed, torque, and tool wear. The main parameters to foresee the failure are Tool Wear Failure (TWF), Heat Dissipation Failure (HDF), Power Failure (PWF), Overstrain Failure (OSF), and Random Failures (RNF). We aim to explore different machine learning algorithms to predict the machine maintenance status and pave the way for a new methodology to anticipate the maintenance schedule in order to reduce factory downtime.
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