Review Paper on - Learning for Predictive Maintenance in Industry

Abstract

Predictive maintenance (PdM) has emerged as a transformative approach in modern industries to minimize unplanned downtime, optimize asset utilization, and reduce maintenance costs. With the increasing availability of industrial sensor data and advancements in computing power, machine learning (ML) has become a key enabler for effective PdM strategies. This paper presents a comprehensive review of recent literature on the application of ML techniques in predictive maintenance across various industrial domains. The study explores supervised, unsupervised, and reinforcement learning approaches used for anomaly detection, fault diagnosis, and remaining useful life (RUL) prediction. Key challenges such as data quality and availability, real-time processing, scalability, and model interpretability are critically discussed. Furthermore, the review highlights current trends, industrial case studies, and future research directions, emphasizing the role of ML in advancing Industry 4.0 initiatives. This work aims to provide researchers and practitioners with a consolidated understanding of state-of-the-art ML methodologies for predictive maintenance and their potential to enhance reliability and efficiency in industrial systems.9

Introduction

Industrial machinery is vital to sectors like manufacturing, energy, and transportation. Ensuring its reliability through effective maintenance strategies is key to minimizing downtime and costs.

Maintenance Strategies

1. Reactive Maintenance: Fix after failure

2. Preventive Maintenance: Regular, scheduled servicing

3. Predictive Maintenance (PdM): Uses data and machine learning (ML) to forecast failures and schedule interventions efficiently

PdM, empowered by Industry 4.0 and IIoT technologies, is the most promising approach, offering a balance between reliability and cost-efficiency.

Role of Machine Learning in PdM

• Data Sources: Sensors, SCADA, digital twins

• Signals Analyzed: Vibration, temperature, pressure, acoustics

• ML Tasks:

  • Fault Detection

  • Diagnostics

  • Remaining Useful Life (RUL) Prediction

ML models used include:

• Supervised learning: SVMs, Random Forests, Neural Networks

• Unsupervised learning: Clustering, anomaly detection

• Deep learning: RNNs, LSTMs, Autoencoders

Challenges

• Imbalanced and noisy industrial data

• Lack of labeled fault data

• Model interpretability and trust

• Integration into existing workflows

• Scalability and real-time requirements

Literature Insights

• Evolution from traditional condition-based maintenance to deep learning and hybrid models

• Increased focus on:

  • Explainable AI (XAI)

  • Edge computing and real-time analytics

  • Deployment and scalability

  • Human-in-the-loop systems

Surveys and empirical studies (2021–2025) stress the importance of generalization, transferability, and cost-aware evaluations.

Methodology

• Continuous sensor-based monitoring (e.g., temperature, voltage)

• Centralized processing with real-time ML analysis

• Fault prediction triggers automated safety protocols (e.g., shutdown, alerts)

• Uses supervised learning and reinforcement learning

• Combines edge computing for real-time actions with cloud analytics for long-term insights

Applications Across Industries

• Manufacturing: CNC tools, robotics

• Aerospace: Engine RUL estimation

• Energy: Wind turbines, transformers

• Automotive: Engine and battery diagnostics

• Oil & Gas: Pumps, compressors

• Railway: Tracks, wheelsets

• Chemical/Process: Valves, pipelines

• Healthcare: MRI/CT machine uptime

Results & Benefits

• High Accuracy in fault detection and RUL prediction (especially with LSTMs and CNNs)

• Reduced downtime (20–50%) and maintenance costs

• Shift toward automation, reducing dependency on manual feature engineering

• Enhanced safety and equipment longevity

Conclusion

This review confirms that machine learning has become a powerful and indispensable tool for predictive maintenance in modern industrial systems. It has evolved from a theoretical concept to a practical, value- adding application that fundamentally changes how industries approach asset management. The ability of ML models to process vast amounts of sensor data to detect subtle anomalies and predict future failures has enabled a transition from a time-based or reactive maintenance approach to a highly efficient, condition-based strategy. The literature shows significant progress, particularly with deep learning models, which have improved the accuracy of anomaly detection and Remaining Useful Life (RUL) prediction. However, it is also clear that a performance-first mindset has led to a focus on algorithms that may not be practical for real-world deployment due to their \"black box\" nature and high data requirements. The core challenge is bridging the gap between impressive research results and the realities of industrial environments, which are characterized by noisy data, a lack of labeled failure events, and the need for clear, trustworthy insights.

References

[1] T. Zhu, Y. Ran, X. Zhou, and Y. Wen, “A Survey of Predictive Maintenance: Systems, Purposes and Approaches,” Dec. 2019. [2] Y. Li, et al., “Remaining Useful Life Prediction Based on Deep Learning: A Survey,” Sensors, vol. 24, no. 11, p. 3454, May 2024. [3] J. Carrasco, I. Markova, D. López, I. Aguilera, D. García, M. García-Barzana, M. Arias-Rodil, and J. Luengo, “Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms,” Neurocomputing, vol. 462, pp. 440-452, Oct. 2021. [4] “A systematic literature review of machine learning methods applied to predictive maintenance,” Computers & Industrial Engineering, vol. 137, Nov. 2019, Article 106024. [5] Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities, L. Cummins, A. Sommers, S. BakhtiariRamezani, S. Mittal, J. Jabour, M. Seale, S. Rahimi. [6] G. A. Susto, A. Schirru, S. Pampuri, S. McLoone and A. Beghi, “Machine Learning for Predictive Maintenance: A Multiple Classifier Approach,” IEEE Trans. Ind. Informatics, available as preprint/PDF. [7] A. K. S. Jardine , D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process., 2006. [8] S. Susto, A. Schirru et al., “(survey) A systematic literature review of machine learning methods applied to predictive maintenance,” Computers & Industrial Engineering, 2019. [9] K. He, et al., “Review—Deep Learning Methods for Sensor Based Predictive Maintenance,” (survey PDF), 2021–2022. [10] “Prognostics and Health Management of Industrial Assets: Current Status and Perspectives,” MDPI/PMC review, 2021. [11] “A survey of deep learning-driven architecture for predictive maintenance,” Engineering Applications of Artificial Intelligence, 2024. [12] “Predictive maintenance in Industry 4.0: a survey of planning models and applications,” Sensors / MDPI. [13] “Comparison of deep learning models for predictive maintenance in industrial manufacturing systems,” Scientific Reports, 2025. [14] “Challenges in predictive maintenance – A review,” Journal article, 2022. [15] “A systematic literature review and open research challenges for predictive maintenance in Industry 4.0,” 2025.

Copyright

Copyright © 2025 Dr. D. A. Shahakar, Aniket Khadse, Anjali Paikrao, Payal Mahure, Vaibhavi Kadam, Shrushti Khawale, Deep Bhuyar, Dimpal Panekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.