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

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 t...

Full description

Bibliographic Details
Published in:Mathematics
Main Authors: Jianwei Wu, Jiaqi Wang, Huanguo Chen
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2024
Subjects:
bidirectional long short-term memory
enhanced northern goshawk optimization
remaining useful life prediction
tool wear
Mathematics
QA1-939
Northern Goshawk
Online Access:https://doi.org/10.3390/math12152404
https://doaj.org/article/519265c0fdc3424297d2682ef13fde46
Description
Summary: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.

Similar Items