Advanced Machine Learning Algorithms for Predictive Maintenance in Industrial Manufacturing Systems

Abstract

In industrial manufacturing systems, predictive maintenance is the process of increasing the rate of productivity and minimizing the time that equipment takes to be out of order through early identification of the equipment that is likely to fail. The main focus of this research is to analyze the possibility of using modern approaches in machine learning to enhance the methods of predictive maintenance. We compare multiple current approaches of deep learning, ensemble methods, and anomaly detection to determine their effectiveness in predicting the maintenance requirements utilizing the sensor and operational data. With the help of a large amount of data, we consider the results of the work of each algorithm for the assessment of the predictive accuracy, the ranking of features, and the detection of anomalies. The findings highlight disparities in the effectiveness of the algorithms in terms of accuracy, precision, and recall, and the deep learning models’ ability to grasp intricate and anomalous patterns. The performance of the maintenance predictions is depicted by the use of visualizations of the performance metrics and feature importance. It also describes the drawbacks of the existing models, such as the problem of data quality and generalization. The study draws attention to the possibility of applying sophisticated machine-learning methods to improve the effectiveness of PM in industrial environments. Possible directions of future research are to enhance the generalization ability of the developed models and to expand the usage of modern trends in the machine learning field to enhance maintenance strategies.