A Survey of Predictive Maintenance: Systems, Purposes and Approaches

TL;DR

This survey comprehensively reviews predictive maintenance systems, their architectures, optimization goals, and methods, emphasizing recent advances with machine learning and deep learning, and discusses future research directions.

Contribution

It provides a detailed overview of PdM system architectures, optimization objectives, and methods, including recent deep learning approaches, highlighting future research needs.

Findings

Overview of PdM system architectures including PdM 4.0 and cloud-based systems.

Analysis of optimization objectives like cost reduction and reliability enhancement.

Discussion of machine learning and deep learning methods in PdM applications.

Abstract

This paper highlights the importance of maintenance techniques in the coming industrial revolution, reviews the evolution of maintenance techniques, and presents a comprehensive literature review on the latest advancement of maintenance techniques, i.e., Predictive Maintenance (PdM), with emphasis on system architectures, optimization objectives, and optimization methods. In industry, any outages and unplanned downtime of machines or systems would degrade or interrupt a company's core business, potentially resulting in significant penalties and immeasurable reputation and economic loss. Existing traditional maintenance approaches, such as Reactive Maintenance (RM) and Preventive Maintenance (PM), suffer from high prevent and repair costs, inadequate or inaccurate mathematical degradation processes, and manual feature extraction. The incoming fourth industrial revolution is also demanding for a new maintenance paradigm to reduce the maintenance cost and downtime, and increase system availability and reliability. Predictive Maintenance (PdM) is envisioned the solution. In this survey, we first provide a high-level view of the PdM system architectures including PdM 4.0, Open System Architecture for Condition Based Monitoring (OSA-CBM), and cloud-enhanced PdM system. Then, we review the specific optimization objectives, which mainly comprise cost minimization, availability/reliability maximization, and multi-objective optimization. Furthermore, we present the optimization methods to achieve the aforementioned objectives, which include traditional Machine Learning (ML) based and Deep Learning (DL) based approaches. Finally, we highlight the future research directions that are critical to promote the application of DL techniques in the context of PdM.