Rolling Bearing Fault Diagnosis Based on Time-Frequency Compression Fusion and Residual Time-Frequency Mixed Attention Network

Abstract

The traditional rolling bearing diagnosis algorithms have problems such as insufficient information on time-frequency images and poor feature extraction ability of the diagnosis model. These problems limit the improvement of diagnosis performance. In this article, the input of the time-frequency image and intelligent diagnosis algorithms are optimized. Firstly, the characteristics of two advanced time-frequency analysis algorithms are deeply analyzed, i.e., multisynchrosqueezing transform (MSST) and time-reassigned multisynchrosqueezing transform (TMSST). Then, we propose time-frequency compression fusion (TFCF) and a residual time-frequency mixed attention network (RTFANet). Among them, TFCF superposes and splices two time-frequency images to form dual-channel images, which can fully play the characteristics of multi-channel feature fusion of the convolutional kernel in the convolutional neural network. RTFANet assigns attention weight to the channels, time and frequency of time-frequency images, making the model pay attention to crucial time-frequency information. Meanwhile, the residual connection is introduced in the process of attention weight distribution to reduce the information loss of feature mapping. Experimental results show that the method converges after seven epochs, with a fast convergence rate and a recognition rate of 99.86%. Compared with other methods, the proposed method has better robustness and precision.

A Multi-Adversarial Joint Distribution Adaptation Method for Bearing Fault Diagnosis under Variable Working Conditions

“…Many researchers use various techniques to convert one-dimensional signals received by sensors into two-dimensional images for direct input into existing 1D CNNs for feature extraction. For example, convert one-dimensional sequence data into two-dimensional grayscale images [33,34]; use continuous wavelet transform or fast Fourier transform methods to convert one-dimensional sequence data into time-frequency images [35,36]. These conversion methods have achieved satisfactory results in fault diagnosis, but converting 1D sequence data to 2D images consumes additional computational cost, and the training cost of 2D CNNs is also higher than that of 1D CNNs.…”

Research on fault diagnosis of rolling bearings with variable working conditions based on improved residual neural network

“…In bearing fault diagnosis, many researchers have attempted to use different types of deep learning models. For example, improved stacked recurrent neural networks [13], fault recognition methods combining LSTM and transfer learning [14], using CNN and bidirectional gated units to simultaneously learn time-domain and frequency-domain features in the data [15], using autoencoders [16] to mine signal feature information, and adaptive onedimensional convolutional neural networks [17]. These methods have achieved good results, effectively diagnosing and classifying rolling bearing faults and providing reliable guarantees for industrial production.…”

Rolling Bearing Fault Monitoring for Sparse Time-Frequency Representation and Feature Detection Strategy

“…When analysing the operating conditions of rolling bearings in the time domain or frequency domain, the diagnosis can be affected by nonlinear factors, such as stiffness and clearance to vibration signals and load friction. Time-frequency analysis methods are more effective when handling nonlinear and nonstationary signals because these methods can accurately describe the local time-frequency characteristics of nonstationary signals by revealing the frequency components and their time-varying properties [ 11 , 12 , 13 , 14 ]. Hence, time-frequency analysis methods have been widely used in fault diagnosis in the past few years.…”