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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Bibliographic Details
Published in:PloS one Vol. 19; no. 11; p. e0310133
Main Authors: Xia, Yi-Qiang, Yang, Yang
Format: Journal Article
Language:English
Published: United States Public Library of Science 11.11.2024
Public Library of Science (PLoS)
Subjects:
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
Online Access:Get full text
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Summary: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0310133