Machine fault detection model based on MWOA-BiLSTM algorithm

This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. MWOA tackles common optimization problems like local optima and premature convergence using two key methods: shrink...

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Vydáno v:	PloS one Ročník 19; číslo 11; s. e0310133
Hlavní autoři:	Xia, Yi-Qiang, Yang, Yang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 11.11.2024 Public Library of Science (PLoS)
Témata:	Algorithms Analysis Animals Biology and Life Sciences Bionics Cetacea Computer and Information Sciences Convergence Cost reduction Earth Sciences Ecology and Environmental Sciences Environmental aspects Failure Fault detection Genetic aspects Health aspects Heuristic Heuristic methods Machine learning Mathematical optimization Medicine and Health Sciences Methods Models, Theoretical Neural networks Observations Optimization Optimization algorithms Optimization techniques Physical Sciences Preventive maintenance Process controls Repair & maintenance Research and Analysis Methods Solution space Support vector machines Trouble shooting China
ISSN:	1932-6203, 1932-6203
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Popis
Shrnutí:	This paper proposes the Modulated Whale Optimization Algorithm(MWOA), an innovative metaheuristic algorithm derived from the classic WOA and tailored for bionics-inspired optimization. MWOA tackles common optimization problems like local optima and premature convergence using two key methods: shrinking encircling and spiral position updates. In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. After that, comparisons were conducted between MWOA and seven recently proposed metaheuristics, utilizing the CEC2005 benchmark functions to assess MWOA’s optimization performance. Moreover, the Wilcoxon rank sum test is used to verify the effectiveness of the proposed algorithm. Eventually, MWOA was juxtaposed with the BiLSTM classifier and six other meta-heuristics combined with the BiLSTM classifier. The aim was to affirm that MWOA-BiLSTM outperforms its counterparts, showcasing superior performance across crucial metrics such as accuracy, precision, recall, and F1-Score. The study results unequivocally demonstrate that MWOA showcases exceptional optimization capabilities, adeptly striking a harmonious balance between exploration and exploitation.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0310133