EverAdapt: Continuous adaptation for dynamic machine fault diagnosis environments

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 226, P. 112317 - 112317

Published: Jan. 16, 2025

Language: Английский

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Scraper conveyor gearbox fault diagnosis based on multi-source heterogeneous data fusion

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116797 - 116797

Published: Jan. 1, 2025

Language: Английский

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A new bearing fault diagnosis method based on digital twin-assisted domain adaptation transfer learning

Structural Health Monitoring, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

The demand for advanced monitoring and fault diagnosis technologies critical mechanical components is growing rapidly. Early detection of rolling bearing faults essential preventing performance degradation, unplanned downtime, safety risks. This article presents a novel method that leverages digital twin technology transfer learning to address the limitations existing approaches in terms data dependency cross-domain effectiveness. Initially, precise model developed using finite element analysis accurately simulate dynamics under various operating conditions, generating extensive simulation data. These compensate scarcity are valuable training diagnostic models. To reduce noise level real-world data, snow ablation optimizer algorithm employed optimize variational mode decomposition reduction. Subsequently, techniques utilized treat as source domain actual vibration signals target domain, enabling domain-adaptive learning. approach facilitates feature alignment knowledge transfer, further optimized through adversarial loss maximum kernel mean discrepancy metric. Moreover, deep combines residual convolutional neural networks with Transformer developed, significantly enhancing extraction classification accuracy. Experimental validation conducted on XJTU-SY dataset demonstrates proposed exhibits superior small sample outperforming methods.

Language: Английский

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Review of imbalanced fault diagnosis technology based on generative adversarial networks

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(5), P. 99 - 124

Published: Aug. 31, 2024

Abstract In the field of industrial production, machine failures not only negatively affect productivity and product quality, but also lead to safety accidents, so it is crucial accurately diagnose in time take appropriate measures. However, machines cannot operate with faults for extended periods, diversity fault modes results limited data collection, posing challenges building accurate prediction models. Despite recent advancements, intelligent diagnosis methods based on traditional sampling learning have shown notable progress. Nonetheless, these heavily rely human expertise, making challenging extract comprehensive feature information. To address challenges, numerous imbalance generative adversarial networks (GANs) emerged, GANs can generate realistic samples that conform distribution original data, showing promising diagnosing imbalances critical components such as bearings gears, despite their great potential, GAN face including difficulties training generating abnormal samples. whether GAN-based resampling technology or technology, there are fewer reviews noise-containing imbalance, intra- inter-class dual multi-class series other problems small samples, a lack more summary solutions above problems. Therefore, purpose this paper deeply explore under various failure modes, review analyze research basis. By suggesting future directions, aims provide guidance reference production maintenance.

Language: Английский

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In-depth research on fault diagnosis of turbine rotor utilizing NGSABO-Optimized VMD and CNN-BiLSTM

Engineering Research Express, Journal Year: 2024, Volume and Issue: 6(4), P. 045205 - 045205

Published: Sept. 24, 2024

Abstract To solve the problem of difficulty in extracting and identifying fault types during turbine rotor operation, a diagnosis method based on improved subtraction mean optimizer (NGSABO) algorithm to optimize variational mode decomposition (VMD) CNN-BiLSTM neural network is proposed. Firstly, three improvements are made average algorithm. Secondly, optimal VMD parameter combination NGSABO adaptive selection number K penalty factor α used decompose signal, minimum sample entropy as fitness function for feature extraction. Combining convolutional bidirectional long short-term memory identify classify features. Compared with other methods, this has outstanding performance single coupled faults. The accuracy reaches 98.5714%, which good practical application value.

Language: Английский

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Federated Transfer Learning-Based Paper Breakage Fault Diagnosis

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(2), P. 10009 - 10009

Published: Jan. 1, 2024

Language: Английский

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Local damage identification and nowcasting of mooring system using a noise-robust ConvMamba architecture

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 224, P. 112092 - 112092

Published: Nov. 12, 2024

Language: Английский

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Characteristic Energy Ratio Ramanujan-gram: A novel optimal multi-bands demodulation method

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2024, Volume and Issue: 74, P. 1 - 9

Published: Nov. 13, 2024

Language: Английский

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