Fault diagnosis method for complex feeding and ramming mechanisms based on SAE-ACGANs with unbalanced limited training data

The problem of imbalance and limited training data is the key factor that restricts the application effect of deep learning technology in the field of fault diagnosis of complex feeding and ramming mechanisms. In order to overcome the shortcomings of traditional deep learning methods that are difficult to obtain the internal distribution of limited data and the defect that traditional unbalanced data processing methods do not consider the equalization of category information, a fault diagnosis method for complex feeding and ramming mechanisms based on the wavelet time-frequency diagram and sparse autoencoder auxiliary classifier generative adver sarial networks (SAE-ACGANs) was proposed. Firstly, the continuous wavelet transform (CWT) was performed on the vibration signal of the feeding and ramming mechanism to obtain a two-dimensional time-frequency diagram reflecting the time-frequency characteristics of the signal. Then, the sparse encoder in the model was used to extract image features which were afterwards merged with category information into hidden variables so as to strengthen the ability of latent variables to represent the characteristics related to the category of the image. By the generator, the fused latent variables were mapped to generated samples which had the sample distribution similar to the real one to expand the training data set. The discriminator mined effective depth features from the expanded data set and realized the judgment of the authenticity and category of the samples. Finally, through an adversarial learning and training mechanism, the optimized generator and discriminator alternately optimized each other to achieve the Nash balance. The method improves the sample generation quality and fault judgment ability with unbalanced limited training data. The research results of the bench tests for the complex feeding and ramming mechanism show that; the SAE-ACGANs framework can fully learn the internal distribution and depth characteristics of the input samples. Compared with the original ACGANs framework, the method improves the performance of the discriminator, and realizes the improvement of the model convergence speed, training accuracy and stability. Compared with traditional unbalanced data processing algorithms, the model' s ability to identify minority fault samples is greatly improved. © 2023 Chinese Vibration Engineering Society. All rights reserved.