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. 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. Hybrid and pure data driven approaches have shown to be much more promising, and in this work, we propose an end-to-end data-driven framework for developing deep learning models to predict remaining useful life of turbofan jet engines operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. 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.