Grouping sparse filtering: a novel down-sampling approach toward rotating machine intelligent diagnosis in 1D-convolutional neural networks. (1st June 2022)

• Record Type: Journal Article
• Authors: Wang, Pengxin; Song, Liuyang; Wang, Huaqing; Han, Changkun; Guo, Xudong; Cui, Lingli
• Is Part Of: Measurement science & technology. Volume 33:Number 6(2022)
• Publication Date: 2022-06-01
• Subjects: convolutional neural networks -- intelligent fault diagnosis -- grouping sparse filtering -- feature selector; 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/ac4ce6
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Convolutional neural networks (CNNs) have weight-sharing and feature-learning abilities, and can efficiently and effectively be used for the health monitoring of industrial equipment. However, the pooling operation in a typical CNN can cause the loss of valuable impulse features during data down-sampling. We propose grouping sparse filtering (GSF) to overcome this problem. Instead of using a pooling operation, the GSF splits the channels of features obtained after convolution into equal-length groups. A feature selector with a feature aggregation function based on the channel importance factors and a lasso constraint is used to filter the groups to perform down-sampling. The GSF method preserves the impulse features due to the block sparsity of the vibration signal. Theoretical analysis demonstrates that the GSF has a similar computational complexity to using a pooling layer in a CNN for the same number of layers. Two experimental studies were conducted using data from a laboratory test and industrial environments. The experimental results show that the 1D-CNN with GSF provides better performance for retaining the impulse features of the rotating machinery signals and higher fault identification accuracy than a CNN with a pooling layer.