A novel rotating machinery fault diagnosis method based on multi-channel correlation strategy image information enhancement

Abstract

To address the problem of blurred feature information caused by color mapping during the conversion of one-dimensional signals of industrial rotating machinery to pseudo-color image matrices, an image enhancement method based on matrix information expansion via a multi-channel correlation strategy is proposed. Building on the traditional Markov transition field (MTF) pseudo-color image generation method, the study develops a multi-channel correlation Markov transition field (MCMTF) image coding method. Without introducing additional variables, the multi-channel correlation strategy enhances the information representation capability of images through correlation calculations based on the inherent matrix information, while alleviating feature blurring from pseudo-color conversion. In addition, a max pooling and average pooling parallel convolutional neural network (MAPCNN) is developed to strengthen the network model's global feature extraction capability. The proposed method attains 99.51% and 99.88% accuracy on the self-collected sliding bearing and gear datasets, respectively. Comparisons with state-of-the-art image generation methods and classical models prove its superior performance. The study also explains the mechanism behind the improved performance from the perspective of image information entropy. Scalability experiments of the multi-channel correlation strategy applied to various image generation methods demonstrate the universality of this concept. Experiments on the rolling bearing dataset further confirm the performance of the proposed method. For industrial scenarios involving wind turbines, machine tools, and gas turbines, the proposed method provides both an effective diagnostic approach and new insights for the diagnosis of rotating machinery.