Machine Learning based Predictive Maintenance in Manufacturing Industry

Predictive maintenance normally uses machine learning to learn from existing data to find patterns that can assist in predicting equipment failures in advance. Predictive maintenance maximizes equipment's lifespan by monitoring its condition thus reducing unplanned downtime and repair cost while increasing efficiency and overall productive capacity. This paper first presents the machine learning based methods to predict unplanned failures before they occur. Afterwards, to confront the everlasting downtime problem, it discusses anomaly detection in greater detail. It also explains the selection criteria of these methods. In addition, the techniques presented in this paper have been tested by using well-known data-sets with promising results.