Bearing Faulty Prognostic Approach Based on Multiscale Feature Extraction and Attention Learning Mechanism. (22nd November 2021)

• Record Type: Journal Article
• Authors: Zhou, Yiqing; Wang, Jian; Wang, Zeru
• Other Names: Wong Kelvin Academic Editor.
• Is Part Of: Journal of sensors. Volume 2021(2021)
• Publication Date: 2021-11-22
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2021/6221545
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Recently, researches on data-driven faulty identification have been achieving increasing attention due to the fast development of the modern conditional monitoring technology and the availability of the massive historical storage data. However, most industrial equipment is working under variable industrial operating conditions which can be a great challenge to the generalization ability of the normal data-driven model trained by the historical storage operating data whose distribution might be different from the current operating datasets. Moreover, the traditional data-driven faulty prognostic model trained on massive historical data can hardly meet the real-time requirement of the practical industry. Since the hierarchical feature extraction can enhance the model generalization ability and the attention learning mechanism can promote the prediction efficiency, this paper proposes a novel bearing faulty prognostic approach combining the U-net-based multiscale feature extraction network and the CBAM- (convolutional block attention module-) based attention learning network. First, time domain conditional monitoring signals are converted into the two-dimensional gray-scale image which can be applicable for the input of the CNN. Second, a CNN model based on the U-net structure is adopted as the feature extractor to hierarchically extract the multilevel features which can be very sensitive to the faulty information contained in the converted image. Finally, the extracted multilevel features containing different representations of the raw signals are sent to the designed CBAM-based attention learning network for high efficiency faulty classification with its unique emphasize discrimination characteristic. The effectiveness of the proposed approach is validated by two case studies offered by the CWRU (Case Western Reserved University) and the Paderborn University. The experimental result indicates that the proposed faulty prognostic approach outperforms other comparison models in terms of the generalization ability and the speed-up properties.