Prognostics and Health Management in Technical Systems

Prognostics and Health Management (PHM) encompasses the strategies and technologies applied to anticipate failures, monitor system health and optimise maintenance in complex engineering systems. By integrating real-time sensor data, signal processing and advanced modelling, PHM transforms traditional reactive maintenance into proactive decision-making. Central to PHM is the estimation of Remaining Useful Life (RUL), which guides maintenance schedules to prevent unplanned downtime. Approaches range from purely physics-based models that describe fundamental degradation mechanisms to data-driven methods leveraging statistical learning and deep neural networks. Hybrid strategies combine the interpretability of physical laws with the adaptability of machine learning to achieve robust and accurate predictions even under varying operational conditions. Applications span aerospace, energy generation, manufacturing and transportation sectors, where enhanced reliability and safety are paramount. Digital-twin frameworks and cloud-based analytics further extend PHM capabilities by enabling continuous model updates and scalable deployment. Despite rapid progress, challenges remain in data quality, model transferability, uncertainty quantification and human–machine collaboration. Addressing these issues is essential to realise the full potential of PHM in Industry 4.0 and to support decision-making across global technical infrastructures.

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Prognostics and Health Management in Technical Systems publication trend

The graph below shows the total number of articles in prognostics and health management in technical systems across all publications each year (not limited to Nature Index journals).

Technical terms

Prognostics and Health Management (PHM): Framework for monitoring system health, predicting failures and planning maintenance.

Remaining Useful Life (RUL): Estimated duration a component or system can operate before failure.

Data-driven approach: Prognostic methodology using statistical and machine learning algorithms on sensor measurements.

Physics-based model: Prognostic technique employing mathematical descriptions of underlying degradation processes.

Hybrid modelling: Integration of data-driven and physics-based methods to combine adaptability with interpretability.

Condition-based maintenance (CBM): Maintenance strategy driven by real-time assessment of equipment condition rather than fixed schedules.

References

1. Intelligent fatigue damage tracking and prognostics of composite structures utilizing raw images via interpretable deep learning. Composites Part B Engineering (2024).
2. Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics. Reliability Engineering & System Safety (2023).
3. Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of Manufacturing Systems (2022).