Knowledge Push Mechanism of Rolling Bearing Fault Diagnosis Employing Deep Residual Network Architecture

Rolling bearings play a critical role in rotating machinery, with their fatigue life directly impacting equipment’s operational reliability. This underscores the significant engineering application value of “fault diagnosis” (FD) technology for rolling bearings in mechanical, automation, and aerospace domains. Literature reviews highlight that a substantial portion of failures in machinery such as jet turbine engines, wind turbines, gear reducers, and induction machines are attributable to bearing issues. Early fault detection and preventive maintenance are therefore imperative for ensuring the smooth operation of rotating machinery. This paper focuses on rolling bearings, delving deep into FD technology using machine learning principles. It analyses the structure and common failure modes of rolling bearings, discussing an FD method based on machine learning. Specifically, the SE-DRN (“squeeze-exclusion deep residual network”) approach is employed, leveraging “variational modal decomposition” (VMD) to decompose bearing vibration signals and reorganize the resulting “intrinsic mode function” (IMF) components into an IMF component signal matrix. This matrix is then processed by a depth residual network with a channel attention mechanism for feature extraction and recognition, forming the SE-DRN-based FD model for rolling bearings. The research attains a remarkable average diagnostic accuracy of 98% across five different bearing state types, underscoring its superior accuracy compared to existing literature, thus showcasing the effectiveness of the SE-DRN approach in rolling bearing FD technology.