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Multi-scale memory-enhanced method for predicting the remaining useful life of aircraft engines

  • S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021)
  • Published:
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Abstract

To guarantee the safe operation of machinery and reduce its maintenance costs, estimating its remaining useful life (RUL) is a crucial task. Hence, in this study, a multi-scale memory-enhanced prediction method is proposed to describe fully characteristics of the data. This method is based on a deep learning algorithm and is designed to estimate the RUL of aircraft engines. To handle the complex and multi-fault operating conditions with uncertain properties in RUL estimation, a hybrid model that combines a multi-scale deep convolutional neural network and long short-term memory is presented. Experimental verification was carried out with the Commercial Modular Aero-Propulsion System Simulation dataset from NASA. Compared with multi-scale deep convolutional and long short-term memory networks, the hybrid model performed more efficiently. Furthermore, compared with other state-of-the-art methods, the multi-scale memory-enhanced prediction method can achieve better prognostics, especially for equipment with multiple operating conditions and failure modes.

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Acknowledgements

This research was funded by The Major Project of Scientific and Technological Innovation 2030 (2021ZD0113603), National Natural Science Foundation of China (62103056, U20A20167), Natural Science Foundation of Beijing Municipal (4202026), Natural Science Foundation of Hebei Province (F202103079), The Qin Xin Talents Cultivation Program, Beijing Information Science and Technology University (QXTCP A202102) and Science and Technology Research and Development Program of Hebei Province (18210336).

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Authors and Affiliations

  1. School of Automation, Beijing Information Science & Technology University, Beijing, 100192, China

    Wenbai Chen, Chang Liu & Qili Chen

  2. Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing, China

    Wenbai Chen & Qili Chen

  3. School of Information Science and Engineering, Yanshan University, Hebei, 066004, China

    Peiliang Wu

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  1. Wenbai Chen
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  2. Chang Liu
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Correspondence to Wenbai Chen or Chang Liu.

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Chen, W., Liu, C., Chen, Q. et al. Multi-scale memory-enhanced method for predicting the remaining useful life of aircraft engines. Neural Comput & Applic 35, 2225–2241 (2023). https://doi.org/10.1007/s00521-022-07378-z

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  • DOI: https://doi.org/10.1007/s00521-022-07378-z

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