Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder

Most Remaining Useful Life (RUL) prognostics are obtained using supervised learning models trained with many labelled data samples (i.e., the true RUL is known). In aviation, however, aircraft systems are often preventively replaced before failure. There are thus very few labelled data samples available. We therefore propose a Long Short-Term...

Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines

The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the...

Predictive Aircraft Maintenance: Integrating Remaining-Useful-Life Prognostics into Maintenance Optimization

Current aircraft maintenance ensures safe and reliable flight operations based on inspections repeated at fixed time intervals. The time interval between inspections is often much shorter than the average life of aircraft components, in an effort to timely detect potential failures. While this approach successfully prevents most potential...

Aircraft engine maintenance planning using model-based remaining useful life prognostics: A Master of Science Thesis

Aircraft maintenance methods are shifting from conservative maintenance approaches such as a periodic maintenance approach towards predictive maintenance approaches, leading to a reduction of costs, less unexpected aircraft-on-ground events and less wasted useful life of components. In this paper, we propose a new remaining useful life...

Online model-based remaining-useful-life prognostics for aircraft cooling units using time-warping degradation clustering

Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are...