Intelligent fault diagnosis of automobile main reducer based onstacked convolutional auto-encoder and parallel attention-based convolutional blocks

Fault diagnosis is an important subfield of prognostic and health management (PHM). Intelligent fault diagnosis based on deep learning is the most popular data-driven method of the present. However, current researches are prone to ignoring the strong noisy backgroundin real working conditions and cannot achieve excellent performance in actual application. As we all know, noise reduction and feature extraction are two vital aspects in mechanicalfault diagnosis. In this article, an intelligent diagnostic model based onimproved stacked convolutional auto-encoder (ISCAE) and parallel attention-based convolutional blocks (PACB) is proposed. ISCAE-based module is constructed to reduce the noise of raw signals and then PACB-based module can synchronouslyextract local spatial feature and global feature automatically.To equalize the role of above-mentioned two modules which are serial in the proposed model, two modules are trained and optimized synchronously to simultaneously adjust the neural network weights. The capability and effectiveness of the model are evaluated using a dataset collected from real operating environment of main reducer. The comparative analysisresults show that the ISCAE-PACB-based model can reach the accuracy of 98.95% and is superior to existing models.