A Survey of Predictive Maintenance: Systems, Purposes and Approaches (1912.07383v2)

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.

• The paper presents the evolution from reactive and preventive methods to predictive maintenance, emphasizing its cost-effectiveness and enhanced reliability.
• The paper details the architecture of PdM systems, including cloud-enhanced frameworks and layered models like OSA-CBM for real-time analytics.
• The paper evaluates machine and deep learning approaches, highlighting the superior fault detection capabilities of models such as CNNs, RNNs, and GANs.

An Analytical Review of Predictive Maintenance Systems and Approaches

The paper entitled "A Survey of Predictive Maintenance: Systems, Purposes and Approaches" provides a detailed examination of Predictive Maintenance (PdM) as a critical strategy in the burgeoning landscape of Industry 4.0. It discusses the evolution, architecture, optimization aims, and algorithmic techniques that underpin PdM systems. The authors position PdM as a pivotal solution to reduce equipment downtime and improve system reliability, highlighting it as an essential instrumental component in modern industrial settings.

This paper meticulously details the transition from traditional maintenance methods—Reactive Maintenance (RM) and Preventive Maintenance (PM)—to the more cost-effective and efficient PdM. It scrutinizes the inherent limitations of RM and PM, such as the high costs and inadequacies associated with these models, which necessitate the shift towards PdM to meet the demands of the fourth industrial revolution.

Systems Architecture and Model Considerations

Critical among the topics covered is the architectural framework for PdM systems. The authors elucidate several models, including PdM 4.0, which integrates Internet of Things (IoT), cyber-physical systems, and advanced analytics into maintenance frameworks. It elaborates on OSA-CBM, a standardized framework dividing PdM into functional layers—ranging from data acquisition to advisory generation—allowing for scalable and compatible implementation across various industrial setups.

Moreover, the paper introduces cloud-enhanced PdM systems, emphasizing the advantages of utilizing cloud computing for data processing and collaborative problem-solving. The authors argue that such integration significantly enhances the robustness and adaptability of maintenance systems while providing real-time insights and comprehensive decision-making frameworks.

Optimization Avenues and Strategy Formulation

The paper categorically divides the optimization objectives within PdM into three main sectors: cost minimization, reliability or availability maximization, and multi-objective optimization. Through quantitative formulations and case studies, the authors drive a robust conversation on how these objectives align with wider operational goals. They emphasize the trade-offs between different objectives and the incorporation of multi-objective optimization strategies, which consider cost, reliability, and other critical parameters simultaneously, bolstering PdM's applicability to complex and multifaceted industrial environments.

Machine Learning and Deep Learning Contributions

In advancing PdM, the paper explores various algorithmic strategies, delineating in-depth the machine learning and deep learning approaches utilized within this domain. A comprehensive review of traditional machine learning techniques like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Decision Trees (DTs) is presented, alongside discussions on their inadequacies in complex PdM tasks.

Significantly, the paper explores the role of deep learning models—Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs)—highlighting their efficacy in feature extraction, fault detection, and predictive accuracy. The authors assert how advanced learning models outperform traditional methods, especially in processing high-dimensional data and capturing intricate patterns in equipment behavior.

Implications and Future Directions

The implications of this survey reach beyond technical enhancements, suggesting profound shifts in maintenance strategy and industry practices. It underscores the need for developing standardized frameworks within PdM to harmonize the application of emergent technologies. The authors propose future research directions centered on digital twin integration, hybrid model development, and the addressing of class imbalance issues through advanced deep learning methods like GANs.

This paper is a critical resource for researchers and practitioners seeking to understand the breadth and depth of PdM strategies and technologies. It offers a detailed repository of knowledge that could inform future advancements and innovations tailored towards enhancing maintenance operations across diverse industrial sectors.