Optimizing Predictive Maintenance With Machine Learning for Reliability Improvement

Abstract

Predictive maintenance, as a form of pro-active maintenance, has increasing usage and shows significant superiority over the corrective and preventive maintenance. However, conventional methods of predictive maintenance have noteworthy limitations in maintenance optimization and reliability improvement. In the last two decades, machine learning has flourished and overcome many inherent flaws of conventional maintenance prediction methods. Meanwhile, machine learning displays unprecedented predictive power in maintenance prediction and optimization. This paper compares the features of corrective, preventive, and predictive maintenance, examines the conventional approaches to predictive maintenance, and analyzes their drawbacks. Subsequently, this paper explores the driving forces, and advantages of machine learning over conventional solutions in predictive maintenance. Specifically, this paper reviews popular supervised learning and reinforcement learning algorithms and the associated typical applications in predictive maintenance. Furthermore, this paper summarizes the four critical steps of machine learning applications in maintenance prediction. Finally, the author proposes the future researches concerning how to utilize machine learning to optimize maintenance prediction and planning, improve equipment reliability, and achieve the best possible benefit.