Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Abstract

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process anddown stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Comparison of Residual-based Methods on Fault Detection

“…In addition to the autoencoders, we also evaluate an inputoutput method that maps the operating conditions to the sensor readings (Lövberg, 2021;Darrah et al, 2022). We refer to this model as the operating-conditions-based model (OC Model).…”

“…While several residual-based approaches have been applied to different case studies (Arias Chao et al, 2019;Lövberg, 2021;Darrah, Lövberg, Frank, Biswas, & Quinones-Gruiero, 2022), to the best of our knowledge, their performances have not been compared. In this study, we compare two residualbased methods: autoencoders and input-output models.…”