Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines
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ORIGINAL PAPER
Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines Mengni Wang1
· Yuanxiang Li1 · Yuxuan Zhang1 · Lei Jia1
Received: 26 March 2020 / Revised: 27 July 2020 / Accepted: 17 October 2020 © Shanghai Jiao Tong University 2020
Abstract Accurate remaining useful life (RUL) estimation is crucial for the maintenance of complex systems, e.g. aircraft engines. Thanks to the popularity of sensors, data-driven methods are widely used to evaluate RULs of systems especially deep learning approaches. Though remarkably capable at non-linear modeling, deep learning-based prognostics techniques lack powerful spatio-temporal learning ability. For instance, convolutional neural networks are restricted to only process grid structures rather than general domains, recurrent neural networks neglect spatial relations between sensors and suffer from long-term dependency learning. To solve these problems, we construct a graph structure on sensor network with Pearson Correlation Coefficients among sensors and propose a method for combining the power of graph convolutional network on spatial learning and sequence learning success of temporal convolutional networks. We conduct the proposed method on aircraft engine dataset provided by NASA. The experimental results demonstrate that the established graph structure is appropriate and the proposed approach can model spatio-temporal dependency accurately as well as improve the performance of RUL estimation. Keywords Aircraft engine · Remaining useful life estimation · Graph structure · Spatio-temporal modeling
1 Introduction In view of the major consequences and huge expense are usually relevant to system failures, prognostics and health management (PHM) has received increasing attention in academia and industry. As a challenging task in PHM, prognostics aims to estimate how much time remains before a likely failure or in other words remaining useful life (RUL) to promote reliability and safety. Methods to evaluate RUL can be mainly grouped into three major categories: model-based methods, experience-based methods and datadriven methods [1]. The former requires extensive expert knowledge about systems to build a physical model [2–4],
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Yuanxiang Li [email protected] Mengni Wang [email protected] Yuxuan Zhang [email protected] Lei Jia [email protected]
1
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
which is usually unavailable in practice, e.g. aircraft engines. Experience-based methods leverage historical failure cases to build hidden relationship among current system states, current lives and recorded failure instances. However, methods included in this category, e.g. instance-based methods [5, 6], require more calculations as reference cases increase and depend too much on run-to-failure cases. Data-driven methods, especially deep learning techniques, have emerged as a research hotspot with the availability of sensor data from numerous systems. The clipped
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