Rolling bearing fault diagnosis method based on multi-sensor two-stage fusion. (1st December 2022)

• Record Type: Journal Article
• Authors: Liu, Cang; Tong, Jinyu; Zheng, Jinde; Pan, Haiyang; Bao, Jiahan
• Is Part Of: Measurement science & technology. Volume 33:Number 12(2022)
• Publication Date: 2022-12-01
• Subjects: fault diagnosis -- multi-frequency -- fuzzy entropy -- multi-sensor fusion -- rolling bearing; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac8894
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Despite the great achievements of deep learning methods based on a single sensor in fault diagnosis, learning useful information from multi-sensor data is still a challenge. In order to make full use of multi-sensor information and improve the performance of rolling bearing fault diagnosis, a novel multi-sensor information fusion framework is proposed in this paper. First, a multi-sensor-based multi-frequency information fusion method is proposed. The multi-frequency information of each sensor is segmented first to enhance the datasets, and then a weighted fusion rule based on fuzzy entropy is constructed to fuse the information of different frequency components for multi-sensors. Second, a multi-kernel attention convolutional neural network is designed to realize multi-frequency feature capture, fusion, and fault classification of multi-sensors. Finally, two different rolling bearing datasets are used to implement fault diagnosis experiments. Experimental results show that the proposed method outperforms the comparative methods in terms of diagnostic performance and robustness.