Cross-Level fusion for rotating machinery fault diagnosis under compound variable working conditions. (August 2022)

• Record Type: Journal Article
• Authors: Wang, Sihan; Wang, Dazhi; Kong, Deshan; Li, Wenhui; Wang, Huanjie; Pecht, Michael
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Intelligent fault diagnosis -- Few-shot learning -- Anti-noise -- Compound faults -- Working condition variation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111455
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A new cross-level fusion neural network (CLFNN) is proposed by introducing a cross-level fusion method for FSL, which compares the similarity of feature embedding from different feature space perspectives and calculates the distribution differences of different data sets based on second-order statistical information. It can also obtain the abstract information of macroscopic data distribution and convolutional kernel scale and adaptively fuses the obtained similarity information. Through these methods, the data within a class are more tightly distributed in a smaller feature space, and more discriminative task-specific features are generated. This also allows CLFNN to capture comprehensive similarities and refine features at different levels of vibration signals in parallel. Effectively alleviates the domain shift problem in the data sparsity scenario. A new CVWCF is proposed as an example of the domain shift problem in a data sparsity scenario. The CVWCF couples a variety of complex tasks that cannot be handled by traditional intelligent fault diagnosis methods at the same time, which is closer to the actual industrial needs. The proposed method will be compared in this more complex scenario to verify the superiority. Ablation experiments are implemented on the dataset to find the contribution of different levels and the effectiveness of cross-level fusion methods. The proposed CLFNN belongs to the FSL method, which alleviates the problem of relying on the target domain label information and improves the adaptability to complex coupling conditions. The results show that it has excellent fault diagnosis ability of CVWCF. Abstract: Rotating machinery fault diagnosis based on deep learning has been successfully applied in modern industrial equipment. However, many existing types of research suffer from two significant deficiencies. First, most deep neural networks are based on a single or same kind of similarity measurement method, which cannot fully exploit the data to extract different levels of feature information. Second, most intelligent fault diagnosis methods can only partially solve the data sparsity and domain shift problems caused by small samples, noise, variable working conditions or compound faults. The model's performance will degenerate rapidly when the above problems occur simultaneously. To address this problem, this paper develops a cross-level fusion neural network method that extracts abundant information on features by calculating spatial-level, channel-level, and second-order statistical information and adaptively fusing the three levels to obtain the final relationship score. First, the signal is input into the embedding module through a Fast Fourier Transform to obtain the feature embedding of the one-dimensional sequence signal. Then, the cross-level metrics learning module calculates the similarity of query sets and support sets at different levels. Finally, the similarities of different levels are fused through the adaptive fusion module to output the final relationship score. The bearing fault diagnosis experiments in the compound variable condition scenario show that the proposed method improves at least 78.53% compared to the traditional deep learning method, at least 3.22% and at most 35.52% compared to multiple few-shot learning methods. In addition, the ablation test analyzes the contribution of different level measurement methods to the model, and the maximum difference between them will reach 32.49%. In summary, the cross-level fusion method can effectively alleviate the data sparsity and domain shift problems.