Intelligent preventive maintenance powered by health data analytics is essential to ensure
operation safety and performance of diverse industrial equipment. Despite the rapid …

This paper presents a novel data-driven predictive maintenance scheduling framework for
aircraft engines based on remaining useful life (RUL) prediction. First, a deep learning …

Current system reliability methods (typically based on fault trees [FTs] or reliability block
diagrams) can effectively propagate reliability data from the asset level to the system level in …

In many settings, fleets of assets must perform series of missions with in-between finite
breaks. For such fleets, a widely used maintenance strategy is the fleet selective …

The economic loss caused by shutdown and fault of coal mill is huge. By effective
prognostics and health management (PHM) for health monitoring and fault detection of coal …

Mechanical systems are crucial for the safety of complex equipment. As they often operate in
harsh environments, the patterns of their failures are becoming increasingly difficult to grasp …

In the context of advancing industrial automation, gearboxes, as pivotal components in
power transmission systems, have a direct bearing on the operational efficiency and safety …

Components that are vulnerable to the external environment and harsh working conditions
easily fail during their life cycle under variable loads of offshore oil and gas equipment. The …

Purpose This study aims to introduce an innovative approach to predictive maintenance by
integrating time-series sensor data with event logs, leveraging the synergistic potential of …

Remaining useful life (RUL) prediction plays a crucial role in maintaining the operational
efficiency, reliability, and performance of complex engineering systems. Recent efforts have …