# A Survey of Predictive Maintenance: Systems, Purposes and Approaches

arXiv: https://arxiv.org/abs/1912.07383 · HTML: https://mlsanity.com/paper/1912.07383/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 21 of 21)

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- [State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies](https://mlsanity.com/paper/2311.06993/) (2023)
- [Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities](https://mlsanity.com/paper/2202.05610/) (2022)
- [Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry](https://mlsanity.com/paper/2203.03259/) (2022)
- [Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges](https://mlsanity.com/paper/2508.09561/) (2025)
- [State-of-the-Art Review: The Use of Digital Twins to Support Artificial Intelligence-Guided Predictive Maintenance](https://mlsanity.com/paper/2406.13117/) (2024)
- [Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data](https://mlsanity.com/paper/2411.01360/) (2024)
- [Revolutionizing System Reliability: The Role of AI in Predictive Maintenance Strategies](https://mlsanity.com/paper/2404.13454/) (2024)
- [Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey](https://mlsanity.com/paper/2408.00516/) (2024)
- [A domain adaptation neural network for digital twin-supported fault diagnosis](https://mlsanity.com/paper/2505.21046/) (2025)
- [On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems](https://mlsanity.com/paper/2605.07860/) (2026)
- [Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection](https://mlsanity.com/paper/2110.01447/) (2021)
- [RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks](https://mlsanity.com/paper/2301.10822/) (2023)
- [Cycling into the workshop: predictive maintenance for Barcelona's bike-sharing system](https://mlsanity.com/paper/2404.17217/) (2024)
- [Predictive maintenance solution for industrial systems -- an unsupervised approach based on log periodic power law](https://mlsanity.com/paper/2408.05231/) (2024)
- [The Role of XAI in Transforming Aeronautics and Aerospace Systems](https://mlsanity.com/paper/2412.17440/) (2024)
- [A Benchmark of Causal vs. Correlation AI for Predictive Maintenance](https://mlsanity.com/paper/2512.01149/) (2025)
- [Empirical Investigation of the Impact of Phase Information on Fault Diagnosis of Rotating Machinery](https://mlsanity.com/paper/2512.15344/) (2025)

## Abstract (arXiv)

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.