Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

“…e existing fault feature extraction methods, including [20,21], will inevitably produce errors in the process of broadband signal processing. It is difficult to extract the broadband fault feature information of low-speed hub bearing signal from nonstationary strong noise.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

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“…Due to the powerful performance of feature learning and extracting, intelligent diagnosis methods based on deep learning have been applied to various engineering areas [ 22 ]. In particular, models based on convolutional neural network (CNN) have been widely researched to solve the problems of bearing fault diagnosis [ 23 , 24 ]. Wang et al [ 25 ] combined the squeeze-and-excitation (SE) network and CNN to propose SE-CNN, while using symmetrized dot pattern (SDP) images of vibration signals as input.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on an Enhanced Image Representation Method of Vibration Signal and Conditional Super Token Transformer

Liu

Liang

et al. 2022

Entropy

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Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is an advanced deconvolution method, which can effectively inhibit the interference of background noise and distinguish the fault period by calculating the multipoint kurtosis values. However, multipoint kurtosis (MKurt) could lead to misjudgment since it is sensitive to spurious noise spikes. Considering that L-kurtosis has good robustness with noise, this paper proposes a multipoint envelope L-kurtosis (MELkurt) method for establishing the temporal features. Then, an enhanced image representation method of vibration signals is proposed by employing the Gramian Angular Difference Field (GADF) method to convert the MELkurt series into images. Furthermore, to effectively learn and extract the features of GADF images, this paper develops a deep learning method named Conditional Super Token Transformer (CSTT) by incorporating the Super Token Transformer block, Super Token Mixer module, and Conditional Positional Encoding mechanism into Vision Transformer appropriately. Transfer learning is introduced to enhance the diagnostic accuracy and generalization capability of the designed CSTT. Consequently, a novel bearing fault diagnosis framework is established based on the presented enhanced image representation and CSTT. The proposed method is compared with Vision Transformer and some CNN-based models to verify the recognition effect by two experimental datasets. The results show that MELkurt significantly improves the fault feature enhancement ability with superior noise robustness to kurtosis, and the proposed CSTT achieves the highest diagnostic accuracy and stability.

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Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

Cited by 200 publications

References 34 publications

Clustering-Guided Novel Unsupervised Domain Adversarial Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Clustering-Guided Novel Unsupervised Domain Adversarial Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Bearing Fault Diagnosis Based on an Enhanced Image Representation Method of Vibration Signal and Conditional Super Token Transformer

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