TFFNet: A robust approach with anti-noise and domain shift adaptation for intelligent fault diagnosis of rotating machinery

Abstract

Recently, deep learning has been a predominantly used technique for intelligent fault diagnosis of industrial machines. It has accomplished satisfactory performance as well. However, noise is present in a real-life industrial working environment, and the operational load also constantly changes. This work proposes a Time-Frequency Fusion Network (TFFNet) for intelligent fault diagnosis. It is robust convolutional neural network based deep-learning algorithm and eliminates the signal processing required for denoising. The success of the developed model is verified in the presence of real-time noisy conditions and under a load-varying environment. The proposed model attained 99.98% accuracy in a noisy environment and 98.6% average accuracy under six cases of domain shift. Finally, the results are compared with past studies using accuracy as a performance indicator.