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A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm

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  • Jianwei Wu

    (School of Intelligent Manufacturing, Lishui Vocational & Technical College, Lishui 323000, China
    School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Jiaqi Wang

    (School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Huanguo Chen

    (School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective feature reduction, facilitated by the uniform information coefficient and Mann–Kendall trend tests. The RUL predictions are subsequently refined through a BiLSTM network, with the MSANGO algorithm optimizing the network parameters. Comparative evaluations with BiLSTM, BiGRU, and NGO-BiLSTM models, as well as tests on real-world datasets, demonstrate this method’s superior accuracy and generalizability in RUL prediction, enhancing the efficacy of tool management systems.

Suggested Citation

  • Jianwei Wu & Jiaqi Wang & Huanguo Chen, 2024. "A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm," Mathematics, MDPI, vol. 12(15), pages 1-22, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2404-:d:1448433
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    References listed on IDEAS

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    1. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
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