Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism
This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/14/5/218 |
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| Summary: | This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanism (BiTCN-Attention). To address the limitations of empirical parameter selection in VMD, OCSSA adaptively optimizes the decomposition parameters (penalty factor <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and mode number <i>K</i>) through a hybrid strategy that combines chaotic initialization, Osprey-inspired global search, and Cauchy mutation. Subsequently, the BiTCN captures bidirectional temporal dependencies from vibration signals, while the attention mechanism dynamically filters critical fault features, constructing an end-to-end diagnostic model. Experiments on the CWRU dataset demonstrate that the proposed method achieves an average accuracy of 99.44% across 10 fault categories, outperforming state-of-the-art models (e.g., VMD-TCN: 97.5%, CNN-BiLSTM: 84.72%). |
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| ISSN: | 2076-0825 |