Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments

Compared to vibration sensors, microphones offer several advantages, including non-contact detection, high sensitivity, low cost, and ease of installation. To address the challenges posed by the complex components and significant interference in rolling bearing sound signals, we proposed a fault diagnosis method for rolling bearing acoustic signals based on Secretary Bird Optimization Algorithm (SBOA)-optimized Feature Mode Decomposition (FMD). Initially, a microphone is utilized to collect sound data while the bearing operates, followed by the application of S-FMD (Secretary Bird Optimization Algorithm-optimized Feature Mode Decomposition) to decompose the sound signal and extract components that may contain fault information related to the bearing. The SBOA is employed to adaptively optimize four influencing parameters of FMD: mode number n, filter length L, frequency band cutting number K, and cycle period m. By minimizing envelope entropy as the objective function, we achieve FMD of the bearing sound signal with the assistance of the SBOA. Additionally, this paper introduces an Integrated Signal Evaluation Index (ISEI) to extract potential bearing failure characteristics from the filtered components. Simulation experiments and test results indicate that, compared to Empirical Mode Decomposition, Complementary Ensemble Empirical Mode Decomposition, fixed-parameter FMD, and adaptive variational mode decomposition methods, the proposed approach more effectively extracts weak characteristic information related to early faults in bearing sound signals.