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A novel spatio-temporal hybrid neural network for remaining useful life prediction

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Abstract

Remaining useful life (RUL) prediction is a crucial mission for the prognostic and health management (PHM) of machinery equipment in modern industry. Due to ignorance of the spatial relationship among multiple sensors, the current RUL prediction methods do not perform adequately in terms of accuracy and comprehensive performance. In order to improve the performance, this paper proposes a novel spatio-temporal hybrid neural network (STHNN) method to capture spatio-temporal features of the time sequence signals from multi-sensors at different locations on mechanical equipment. In this method, the spatial relationship among sensors is constructed by a sensor graph based on the cosine similarity among them. Spatial features of the sensor graph are then extracted by the improved residual graph attention module, which is the first part of the spatio-temporal hybrid neural network. As the second part of the STHNN, the Transformer encoder module is employed to obtain the temporal features. At the same time, this study proposes a new evaluation metric named as comprehensive evaluation function (CEF) to assess overall performance of the STHNN on the entire dataset. To confirm the accuracy and validity of the method, experiments are conducted on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The comparison with existing state-of-the-art methods on the same dataset demonstrates performance of the provided STHNN is significantly greater than the current methods.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This study was supported by The Guizhou Science and Technology Support Project (QKHZC〔2022〕YB074) and Science and Technology Foundation of Guizhou Province (QKHJC-ZK〔2021〕YB271).

Funding

The Guizhou Science and Technology Support Project (QKHZC〔2022〕YB074) and Science and Technology Foundation of Guizhou Province (QKHJC-ZK〔2021〕YB271).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, 550025, China

    Tao Wang

  2. State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

    Xianghong Tang & Jianguang Lu

  3. Chongqing Industrial Big Data Innovation Center Co., Ltd., Chongqing, 400707, China

    Jianguang Lu

  4. Guizhou Xin Si Wei Technology Co. Ltd., Guiyang, 550001, China

    Fangjie Liu

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  1. Tao Wang
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  2. Xianghong Tang
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  3. Jianguang Lu
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  4. Fangjie Liu
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Contributions

Data analysis and writing contributed by Tao Wang; Supervision contributed by Xianghong Tang, Jianguang Lu, Fangjie Liu.

Corresponding author

Correspondence to Xianghong Tang.

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Wang, T., Tang, X., Lu, J. et al. A novel spatio-temporal hybrid neural network for remaining useful life prediction. J Supercomput 79, 19095–19117 (2023). https://doi.org/10.1007/s11227-023-05404-y

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