Remaining Useful Life Estimation for Turbofan Engine with Transformer-based Deep Architecture

With the development of information technology and sensors, the large industrial system has become a data-rich environment, which leads to the rapid development and application of deep learning for the remaining useful life prediction, especially for the turbofan engine. Currently, the deep architecture of CNN, LSTM have been used to address the RUL estimation of a turbofan engine. However, they are mainly focused on simulation degradation data. The new realistic run-to-failure turbofan engine degradation dataset has been published in 2021, which presents a significant difference from the simulation one. The main challenge is that the flight duration of each cycle is different, which will result in the current deep method hardly used for predicting the RUL for the practical degradation data. To tackle this challenge, we propose a novel Transformer-based model using guiding features to deal with the unfixed-length data. Besides, our G-Transformer model makes use of multi-head attention to access the global features from various representation subspaces. We conduct experiments on turbofan engine degradation data with variable-length input under practical flight conditions. Empirical results and feature visualization via t-SNE indicate the effectiveness of the G-Transformer model for RUL estimation of turbofan engines.

I. Introduction

As the critical technique of prognostic and health management(PHM), remaining useful life(RUL) estimation plays an important role in the predictive maintenance of large equipment, which aims at estimating the performance of machinery across its lifetime period, and providing a suitable maintenance plan to avoid serious accidents occurring. With the development of the Internet of Things(IoT) and industrial digitalization, the current industrial systems have been gradually turned to become a data-rich environment. These changes have created an unprecedented opportunity to research and develop the advanced RUL method and application by the powerful deep learning technique [1].