A lightweight progressive joint transfer ensemble network inspired by the Markov process for imbalanced mechanical fault diagnosis

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 224, P. 111994 - 111994

Published: Oct. 1, 2024

Language: Английский

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IMWMOTE: A novel oversampling technique for fault diagnosis in heterogeneous imbalanced data

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 251, P. 123987 - 123987

Published: April 25, 2024

Language: Английский

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Like draws to like: A Multi-granularity Ball-Intra Fusion approach for fault diagnosis models to resists misleading by noisy labels

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102425 - 102425

Published: Feb. 23, 2024

Language: Английский

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Novel imbalanced multi-class fault diagnosis method using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs)

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 167, P. 112324 - 112324

Published: Oct. 5, 2024

Language: Английский

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Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 492 - 492

Published: Oct. 18, 2023

The sand cat is a creature suitable for living in the desert. Sand swarm optimization (SCSO) biomimetic intelligence algorithm, which inspired by lifestyle of cat. Although SCSO has achieved good results, it still drawbacks, such as being prone to falling into local optima, low search efficiency, and limited accuracy due limitations some innate biological conditions. To address corresponding shortcomings, this paper proposes three improved strategies: novel opposition-based learning strategy, exploration mechanism, elimination update mechanism. Based on original SCSO, multi-strategy (MSCSO) proposed. verify effectiveness proposed MSCSO algorithm applied two types problems: global feature selection. includes twenty non-fixed dimensional functions (Dim = 30, 100, 500) ten fixed functions, while selection comprises 24 datasets. By analyzing comparing mathematical statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, show that ability can adapt wide range problems.

Language: Английский

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Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112785 - 112785

Published: Jan. 1, 2025

Language: Английский

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MWMOTE-FRIS-INFFC: An Improved Majority Weighted Minority Oversampling Technique for Solving Noisy and Imbalanced Classification Datasets

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4670 - 4670

Published: April 23, 2025

In view of the data fault diagnosis and good product testing in industrial field, high-noise unbalanced samples exist widely, such are very difficult to analyze field analysis. The oversampling technique has proved be a simple solution past, but it no significant resistance noise. order solve binary classification problem data, an enhanced majority-weighted minority technique, MWMOTE-FRIS-INFFC, is introduced this study, which specially used for processing noise-unbalanced classified sets. method uses Euclidean distance assign sample weights, synthesizes combines new into with larger weights belonging few classes, thus solves scarcity smaller class clusters. Then, fuzzy rough instance selection (FRIS) eliminate subsets synthetic low clustering membership, effectively reduces overfitting tendency caused by oversampling. addition, integration fusion iterative filters (INFFC) helps mitigate noise issues, both raw On basis, series experiments designed improve performance 6 algorithms on 8 sets using MWMOTE-FRIS-INFFC algorithm proposed paper.

Language: Английский

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A position oversampling based on ensemble for imbalanced multi-class classification

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Language: Английский

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A Two-Stage Graph Spatiotemporal Model with Domain-Class Alignment for Fault Diagnosis Under Multi-Source Long-Tailed Distributions

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113698 - 113698

Published: May 1, 2025

Language: Английский

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Semi-supervised dual-constraint centroid contrastive prototypical network for flip chip defect detection under limited labeled data

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 156, P. 111163 - 111163

Published: May 18, 2025

Language: Английский

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