Rolling bearing fault diagnosis method based on MTF-MFACNN

Lei, Chunli; Miao, Chengxiang; Wan, Huiyuan; Zhou, Jiyang; Hao, Dongfeng; Feng, Ruicheng

doi:10.1088/1361-6501/ad11c7

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Additionally, all models were implemented using Python programming language within the PyTorch framework. Based on previous parameter design experience mentioned in [41,42], a batch size of 64 is chosen to achieve better diagnostic results along with a fixed learning rate of 1 × 10 −4 and a maximum iteration limit of 300.…”

Section: Comparative Methods and Application Detailsmentioning

confidence: 99%

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

Yu,

Liu,

Wang

et al. 2024

Meas. Sci. Technol.

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High-quality labeled data are crucial prerequisites for ensuring the effectiveness of fault diagnosis methods based on deep learning technology. However, in practical scenarios, providing abundant training data with accurate labels for these approaches is unfeasible owing to the constraints imposed by the operating and working conditions. To tackle this realistic challenge, we propose an innovative feature separation simulation-assisted transfer framework (FSSATF) for the fault diagnosis of rotating machinery. The primary concept of FSSATF is to leverage dynamic simulation-assisted data as a surrogate for the labeled data of actual equipment and integrate the feature separation network to explicitly extract domain-independent and fault-discriminative features from the simulated and actual domains, facilitating knowledge transfer and enhancing fault diagnosis capabilities. Specifically, we design a feature separation network consisting of two feature extractors. The special feature extractor is trained with the proposed target domain classification loss to explicitly separate the noisy features from the actual data. Moreover, our proposed domain adaptive loss function effectively narrows the distribution discrepancy between the simulated and actual data, promoting the shared feature extractor to capture domain-invariant and fault-discriminative features. Additionally, clustering learning is embedded into FSSATF to minimize the distance between samples of the same category, strengthening the model's capabilities for feature extraction, and improving its performance in real machinery fault diagnosis. Artificially damaged and run-to-failure datasets were employed to validate the effectiveness and superiority of FSSATF. The comparative analysis results demonstrate that the fault diagnosis performance surpasses those of other advanced transfer learning fault diagnosis methods.

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Section: Comparative Methods and Application Detailsmentioning

confidence: 99%

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

Yu,

Liu,

Wang

et al. 2024

Meas. Sci. Technol.

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“…Markov Transition Field (MTF) can encode one-dimensional vibration signals into two-dimensional images [18]. It mainly uses Markov matrices to preserve time domain information and enables the encoding of dynamically transferred information.…”

Section: Markov Transition Field (Mtf)mentioning

confidence: 99%

Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

Tong,

Zhang,

Wang

et al. 2024

Applied Sciences

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To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively.

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Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

Zhao,

Yang,

Zhu

et al. 2025

International Journal of Refrigeration

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Rolling bearing fault diagnosis method based on MTF-MFACNN

Cited by 5 publications

References 26 publications

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

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