A recombination generation method for missing fault data samples under unknown working conditions based on swap invariance-based multi-attribute disentangled representation network

Fault diagnosis under variable working conditions has become a prominent research focus in recent years. Numerous methods have been proposed to address it and data-driven methods have become popular. A data-driven diagnostic model with high accuracy is required to be trained on data that fully cover all working conditions. However, collecting such data is difficult, hindering the construction of diagnostic models under variable working conditions. Based on the theory that genetic traits are controlled by genes, this study proposes a recombination generation method for missing fault data samples under unknown working conditions based on a swap invariance-based multi-attribute disentangled representation network (SIMDRN). The proposed method can generate data samples under unknown working conditions based on the existing data under known working conditions. Firstly, three reconstruction losses and a generative adversarial loss are used to train the SIMDRN built using an improved variational autoencoder. Secondly, auxiliary samples are selected and encoded as disentangled factors using the trained SIMDRN. Finally, the missing samples under specific working conditions are generated by recombining and decoding the disentangled factors to improve the diagnostic performance of the models. The proposed method is verified using a superposed sine signal dataset and an experimental rolling-bearing fault dataset. The results show that the samples generated by SIMDRN have great diversity and consistency. Moreover, the diagnostic accuracy is significantly improved by combining the generated samples with existing samples.