Optimizing Predictive Maintenance With Machine Learning for Reliability Improvement
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 7, Issue 3
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. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4049525.
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Copyright © 2021 by ASME.
History
Received: May 16, 2019
Revision received: Dec 30, 2020
Published online: May 28, 2021
Published in print: Sep 1, 2021
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Cited by
- Xiaogang Guo, Ning Wang, Automated Identification of Pavement Structural Distress Using State-of-the-Art Object Detection Models and Nondestructive Testing, Journal of Computing in Civil Engineering, 10.1061/JCCEE5.CPENG-5864, 38, 4, (2024).