Predictive maintenance uses data and analytics to anticipate equipment failures before they occur, allowing interventions to be scheduled at the most effective time. It moves beyond fixed-interval maintenance by exploiting sensor data, historical records and models to estimate the actual condition and remaining useful life of components.

Recent work emphasizes building digital representations of assets that combine physics based models with data driven techniques. Physics based models capture how systems should behave under known conditions, while machine learning models learn patterns and anomalies directly from data streams. Combining these approaches improves accuracy and robustness, especially when data is limited or operating conditions change.

A major focus is on how to handle and process large volumes of heterogeneous data. Research examines methods for cleaning and fusing sensor signals, extracting informative features and updating models online as new data arrives. Health indicators are derived to track degradation, and prognostic algorithms estimate failure probabilities and time to failure with quantified uncertainty.

Another key topic is deployment in real industrial environments. Studies highlight challenges such as sensor placement, data quality, integration with existing control systems and the need for scalable architectures from the edge to the cloud. There is growing interest in autonomous maintenance functions, where edge devices perform local diagnostics and refinement of models, reducing latency and dependence on constant connectivity.

Overall, predictive maintenance research is converging on hybrid modeling, uncertainty aware prognostics and hierarchical systems that distribute intelligence between embedded devices and central platforms to improve reliability, safety and lifecycle cost.