Fault Diagnosis Method for Rotating Machinery Based on Threshold-Free Recurrence Distance Visualization Convolutional Neural Network

Recursive Plots (RPs) can fully utilize the information of signals on a time scale, but their application involves the issue of manual threshold selection, and different thresholds have a significant impact on the analysis results of recursive plots, which in turn affects the accuracy of subsequent fault diagnosis models. Some scholars have proposed the no-threshold recursive plot method to address the above issues, but this method is not comprehensive enough and has limitations. On the basis of RPs, this article proposes a Threshold-Free Recurrence Distance (TFRD), which is combined with a Convolutional Neural Network (CNN) to form a TFRD-CNN rotating machinery fault diagnosis model. The accuracy of the method is tested using bearing vibration data from Western Reserve University, and the effectiveness of the model is verified using a planetary gearbox gear fault dataset. At the same time, the TFRD-CNN method is compared with a Markov Transition Field (MTF), Gramian Angular Fields (GAF), and RP and URP combined with CNN methods. The results show that the TFRD-CNN method has significant advantages.