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...

A novel strain-based health indicator for the remaining useful life estimation of degrading composite structures

We present a generic methodology for developing a Health Indicator out of strain-based Structural Health Monitoring data suitable for implementation in prognostic tasks. For this purpose, an in-house test campaign is launched. Single-stringered composite panels are subjected to compression-compression fatigue with the strains being monitored...

Health monitoring of aerospace structures utilizing novel health indicators extracted from complex strain and acoustic emission data

The development of health indicators (HI) of diagnostic and prognostic potential from generally uninformative raw sensor data is both a challenge and an essential feature for data‐driven diagnostics and prognostics of composite structures. In this study, new damage‐sensitive features, developed from strains acquired with Fiber Bragg Grating ...