Multi-Classifier Predictive Maintenance Strategy for a Manufacturing Plant

Predictive Maintenance Management in an industry can play a pivotal role in asset management and revenue generation. This work proposes a data-driven-based multi classifier model for implementing predictive maintenance to simultaneously reduce the downtime and idle time of the machines in a manufacturing plant. A case study of the plant comprising of 100 machines has been done to identify the early prediction of failure, its nature, and the attributing cause. Gradient Boosting Tree Classifier and Random Forest Classifier machine learning algorithms have been used to develop the models for fault prediction. A comparative analysis of results obtained using these methods has also been done. Random Forest Classifier outperforms Gradient Boost tree classifier in all evaluation parameters - accuracy, precision and recall.