Predictive Maintenance and Fault Diagnosis in Industrial Systems

Predictive maintenance and fault diagnosis constitute integral components of modern industrial asset management. By continuously monitoring sensor signals and operational data, predictive maintenance seeks to forecast component degradation and schedule interventions before failures occur, thereby maximising equipment availability and reducing unplanned downtime. Fault diagnosis complements this by identifying the root causes of anomalies, enabling targeted corrective actions. Together, these approaches underpin the broader discipline of prognostics and health management (PHM), which links failure mechanisms to lifecycle strategies and economic decision-making. Advances in sensor networks, the Internet of Things (IoT) and edge computing have dramatically increased data availability, while developments in machine learning and deep learning have delivered sophisticated pattern-recognition capabilities. Nevertheless, challenges remain in ensuring model interpretability, handling scarce failure-labelled data and integrating domain expertise with data-driven algorithms. Digital twins and cloud-based analytics further extend the reach of PHM systems, offering virtual replicas of physical assets for simulation and scenario testing. Globally, industries ranging from energy production to heavy manufacturing have begun to adopt these techniques at scale, realising significant cost savings, safety improvements and carbon-emission reductions. As research continues to address robustness, explainability and standardisation, predictive maintenance and fault diagnosis are poised to become universal enablers of resilient and sustainable industrial operations.

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Predictive Maintenance and Fault Diagnosis in Industrial Systems publication trend

The graph below shows the total number of articles in predictive maintenance and fault diagnosis in industrial systems across all publications each year (not limited to Nature Index journals).

Technical terms

Predictive Maintenance: A strategy that uses data analysis to forecast when equipment failure is likely and to schedule maintenance proactively.

Fault Diagnosis: The process of detecting, isolating and identifying the underlying causes of equipment malfunctions.

Anomaly Detection: Techniques for recognising patterns in data that deviate from expected normal behaviour, indicative of potential faults.

Remaining Useful Life (RUL): An estimate of the time span during which an asset will continue to perform its intended function before failure.

Prognostics and Health Management (PHM): A discipline combining failure-mechanism understanding with data analytics to guide maintenance and lifecycle planning.

Explainable AI (XAI): Methods that render machine-learning models’ decisions transparent and interpretable to human stakeholders.

References

1. Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence (2020).
2. FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning. Future Generation Computer Systems (2021).
3. Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Sensors (2021).
4. An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis. Sensors (2022).