A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

The prognostics and health management disciplines provide an efficient solution to improve a system's durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem's remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.

Automated summary

Prognostics and health management disciplines offer an efficient solution to enhance system durability by estimating the remaining useful life (RUL) before failure. This study focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to evaluate the performance quality in RUL estimation. The approach is applied to non-monotonic degradation signals and tested using data from new generation telescopes in real-world applications.