Abstract
Convolutional Neural Network (CNN) has been widely used in bearing fault diagnosis in recent years, and many satisfying results have been reported. However, when the training dataset provided is unbalanced, such as the samples in some fault labels are very limited, the CNN’s performance reduces inevitably. To solve the dataset imbalance problem, a Generative Adversarial Network (GAN) has been preferably adopted for the data generation. In published research studies, GAN only focuses on the overall similarity of generated data to the original measurement. The similarity in the fault characteristics is ignored, which carries more information for the fault diagnosis. To bridge this gap, this paper proposes two modifications for the general GAN. Firstly, a CNN, together with a GAN, and two networks are optimized collaboratively. The GAN provides a more balanced dataset for the CNN, and the CNN outputs the fault diagnosis result as a correction term in the GAN generator’s loss function to improve the GAN’s performance. Secondly, the similarity of the envelope spectrum between the generated data and the original measurement is considered. The envelope spectrum error from the 1st to 5th order of the Fault Characteristic Frequencies (FCF) is taken as another correction in the GAN generator’s loss function. Experimental results show that the bearing fault samples generated by the optimized GAN contain more fault information than the samples produced by the general GAN. Furthermore, after the data augmentation for the unbalanced training sets, the CNN’s accuracy in the fault classification has been significantly improved.
Highlights
As an indispensable component in rotating machines, bearing health status directly affects or even determines the equipment service life
Fault Data Generation Based on Optimized Generative Adversarial Network (GAN)
The envelope spectrum error from the 1st-5th order Fault Characteristic Frequencies (FCF) between the experimental data and the generated data is taken as a correction term to the general cross-entropy based loss function of the GAN’s generator
Summary
As an indispensable component in rotating machines, bearing health status directly affects or even determines the equipment service life. The data-driven fault diagnosis has been attracting more and more attention from both academia and industry. Among the various data-driven methods, Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) are the most widely used due to their powerful abilities in the complex feature extraction and nonlinear mapping. CNN was first employed in the bearing fault diagnosis by O. Russell Sabir adopted LSTM for the bearing fault diagnosis based on the motor current signal and obtained a classification accuracy of 96% [7]. Qiu proposed the stacked LSTM and the bidirectional LSTM, respectively, and both LSTMs obtained an accuracy of more than 99% on the bearing fault diagnosis [8,9]. Pan combined 1D-CNN and 1D-LSTM into a unified structure by using the CNN’s output into LSTM, achieving a satisfactory test accuracy up to 99.6% [10]
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