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Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective

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  • NSTL
  • Elsevier
In the Engineering discipline, prognostics play an essential role in improving system safety, reliability and enabling predictive maintenance decision-making. Due to the adoption of emerging sensing techniques and big data analytics tools, data-driven prognostic approaches are gaining popularity. This paper aims to deliver an extensive review of recent advances and trends of data-driven machine prognostics, with a focus on their applications in practice. The primary purpose of this review is to categorize existing literature and report the latest research progress and directions to support researchers and practitioners in acquiring a clear comprehension of the subject area. This paper first summarizes fundamental methodologies on data-driven approaches for predictive maintenance. Then, the article further conducts a comprehensive investigation on the different fields of applications of machine prognostics. Finally, a discussion on the challenges, opportunities, and future trends of predictive maintenance is presented to conclude this paper.

Machine prognosticsPredictive maintenanceCondition-based maintenanceMachine learningPrognostics and health managementRemaining useful lifeREMAINING USEFUL LIFEGAUSSIAN PROCESS REGRESSIONCONVOLUTIONAL NEURAL-NETWORKLITHIUM-ION BATTERYWIENER-PROCESSHEALTH PROGNOSTICSDEGRADATION MODELLEARNING-METHODSFAULT-DIAGNOSISFAILURE

Wen, Yuxin、Rahman, Md Fashiar、Xu, Honglun、Tseng, Tzu-Liang Bill

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Chapman Univ

Univ Texas El Paso

2022

10.1016/j.measurement.2021.110276

Measurement

Measurement

SCI
ISSN：0263-2241
年,卷(期)：2022.187
  • 31
  • 196