Multi source heterogeneous data diagnosis method of rotating machinery based on parameter collaborative optimization of multi-scale convolutional autoencoder
In order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencod...
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| Veröffentlicht in: | Journal of Vibroengineering |
|---|---|
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
28.10.2025
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| ISSN: | 1392-8716, 2538-8460 |
| Online-Zugang: | Volltext |
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| Abstract | In order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed. Firstly, multi-scale information learning is integrated into the convolutional autoencoder (CAE) to consider the temporal and spatial feature information of the diagnostic object simultaneously. To improve the training and diagnostic efficiency of MSCAE, a quantum particle swarm optimization (QPSO) module is used to perform hyperparameter optimization on it using chaos initialization and dynamic weight strategy (DWS). Besides, the sparse attention mechanism is introduced into the MSCAE model to improve the recognition rate of key fault features hidden in the original heterogeneous signals. Finally, the confusion matrix and visualization techniques are used to achieve fault classification. The experimental results demonstrate that after 100 experiments, the proposed method has an average diagnostic accuracy of 98.5 % and strong robustness to noise, providing a new method for rotating machinery fault diagnosis based on multi-source heterogeneous data. |
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| AbstractList | In order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed. Firstly, multi-scale information learning is integrated into the convolutional autoencoder (CAE) to consider the temporal and spatial feature information of the diagnostic object simultaneously. To improve the training and diagnostic efficiency of MSCAE, a quantum particle swarm optimization (QPSO) module is used to perform hyperparameter optimization on it using chaos initialization and dynamic weight strategy (DWS). Besides, the sparse attention mechanism is introduced into the MSCAE model to improve the recognition rate of key fault features hidden in the original heterogeneous signals. Finally, the confusion matrix and visualization techniques are used to achieve fault classification. The experimental results demonstrate that after 100 experiments, the proposed method has an average diagnostic accuracy of 98.5 % and strong robustness to noise, providing a new method for rotating machinery fault diagnosis based on multi-source heterogeneous data. |
| Author | Yang, Xiaoli |
| Author_xml | – sequence: 1 givenname: Xiaoli surname: Yang fullname: Yang, Xiaoli |
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| Cites_doi | 10.1080/00224065.2021.1960934 10.1088/1361-6501/ac41a5 10.55730/1300-0632.3909 10.1088/1361-6501/aadfb3 10.1016/j.isatra.2022.06.035 10.1088/1361-6501/ac543a 10.1016/j.compind.2019.02.004 10.1016/j.dib.2023.109049 10.1016/j.measurement.2020.108518 10.1007/s40430-023-04567-2 10.1109/TII.2018.2793246 10.3390/en12060995 10.1016/j.ress.2022.108528 10.1016/j.promfg.2019.06.075 10.1007/978-981-19-0572-8_92 10.3390/s20010176 10.1016/j.measurement.2021.109685 10.1063/5.0095530 10.1109/TIM.2022.3212542 10.1109/ACCESS.2020.3011980 |
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