Motor Fault Diagnosis Based on Time–Frequency Swinging Door Algorithm and Convolutional Kernel Receptive Field Matching Framework
Xiaofei Zhang, Weizhi Liang, Yinpeng Qu, Xiwei Wang, Sheng Huang
Abstract
In the realm of industrial production, the condition of motors tends to deteriorate gradually over time. Among the various types of motor faults, the selection of appropriate diagnostic techniques stands out as a pivotal step in the process of motor fault identification. In this article, we introduce a fault diagnosis framework aimed at enhancing the precision and efficiency of diagnostics by perceptually matching the time–frequency features of data with the sensory fields of convolutional neural networks (CNNs). First, the time–frequency swing door algorithm (TF-SDA) is proposed to determine the optimal signal compression ratio, guided by the time–frequency attributes of the original data. Subsequently, the data are transformed into a two-dimensional image using temporal image conversion methodologies, specifically the Gramian angular field and symmetrized dot pattern. Finally, the diagnostic framework is realized by matching the CNN receptive fields with TF-SDA data features. Comparative analysis with other state-of-the-art methods and compression algorithms reveals that the proposed approach effectively isolates the optimal time–frequency components of fault characteristics while eliminating extraneous elements, thus substantially enhancing the precision and efficiency of multifault diagnosis.