Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Abstract

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality.

A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

“…There are two main methods for bearing fault diagnosis: signal-based processing and a data-driven approach. Data-driven methods have made significant strides in fault diagnosis due to their powerful nonlinear self-learning [2,3] and intelligent fault diagnosis capabilities [4][5][6][7][8][9][10][11][12][13][14][15][16]. For example, convolutional neural networks (CNNs) [4][5][6][7], generative adversarial networks (GANs) [8][9][10][11], long short-term memory networks [12][13][14], and deep residual shrinkage networks (DRSNs) [15,16] have been widely adopted.…”

“…Data-driven methods have made significant strides in fault diagnosis due to their powerful nonlinear self-learning [2,3] and intelligent fault diagnosis capabilities [4][5][6][7][8][9][10][11][12][13][14][15][16]. For example, convolutional neural networks (CNNs) [4][5][6][7], generative adversarial networks (GANs) [8][9][10][11], long short-term memory networks [12][13][14], and deep residual shrinkage networks (DRSNs) [15,16] have been widely adopted. Ren et al [8] proposed a dynamically balanced domain adversarial network embedded with a physically interpretable novel frequency band attention module for feature extraction to mitigate noise interference.…”