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

Jianwei Wu, Jiaqi Wang and Huanguo Chen ()
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Jianwei Wu: School of Intelligent Manufacturing, Lishui Vocational & Technical College, Lishui 323000, 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

Mathematics, 2024, vol. 12, issue 15, 1-22

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.

Keywords: bidirectional long short-term memory; enhanced northern goshawk optimization; remaining useful life prediction; tool wear (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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