The Impact of Prognostic Uncertainty on Condition-Based Maintenance Scheduling: an Integrated Approach

One of the challenges of Condition-Based Maintenance (CBM) is to combine health monitoring and predictions with efficient scheduling tools. However, the majority of literature is focusing on the assessment of prognostics algorithms performance. In fact, the added value of these algorithms can only be assessed when considering their impact on...

The Impact of Metrics on the Choice of Prognostic Methodologies

Metrics play an important part in the development and application of prognostic methodologies as they provide the capability to characterize and assess the quality of remaining useful life predictions. Although there is a wide range of both, prognostic metrics and prognostic methodologies available, the choice of those often is a demanding and...

An Adaptive Framework For Remaining Useful Life Predictions Of Aircraft Systems

Prognostics for condition-based maintenance does not only consist of prognostic algorithms but also involves steps such as data pre-processing, feature extraction, and feature selection, all of which contribute to the quality of the remaining useful life estimation. This process requires a lot of expertise and technical knowledge, which for many...

Data-Driven Prognostics Incorporating Environmental Factors for Aircraft Maintenance

During flights aircraft continuously collect data regarding operations, health status and system condition. Data-driven approaches typically applied to system specific sensor data provide a way to predict failures of aircraft systems. However, it is believed that some systems deteriorate faster when subjected to particular environmental...