MFVAE: A Multiscale Fuzzy Variational Autoencoder for Big Data-Based Fault Diagnosis in Gearbox

Gearboxes have become an essential part of connecting various components in the machine and transmitting power. A fault in the gearbox will cause the entire mechanical equipment to stop working, causing economic losses and safety hazards. Previous fault diagnosis models for gearbox used the vibration signals as input features. They designed a variety of feature extraction modules to learn the information contained in the vibration signals. However, the latent features learned by previous models have poor interpretability and robustness, so the results are often less robust. Moreover, previous models used for gearbox are usually end-to-end black box models, which cannot provide interpretive information, so it is difficult to handle the uncertain information in the vibration signals. Therefore, we propose a multi-scale fuzzy variational autoencoder (MFVAE) using a fuzzy neural network for big data-based fault diagnosis in gearbox. The big data technology can automatically collect vibration signals from sensors and provide high-quality training samples for fault diagnosis models. The MFVAE model first uses two variational autoencoders of different sizes to extract the latent features of the gearbox vibration signal. Subsequently, the MFVAE model fuses low-level latent features and high-level latent features in proportion to form the final head features, which are used to diagnose gearbox faults. Finally, the MFVAE model inputs the head features into a prediction layer to diagnose the fault. The prediction layer comprises a fully connected network and a fuzzy neural network. Experimental results on a real gearbox dataset verify the outstanding performance of the MFVAE model.