Optimization
A. Haitao Zhang; B. Li Guan; C. Long Chang
Abstract
Rolling bearings are critical components of rotating machinery, and their health status directly affects the operational reliability of equipment. This paper proposes an optimized wavelet-SVM fault diagnosis method based on multi-source vibration signal fusion: Three-channel inputs are constructed by ...
Read More
Rolling bearings are critical components of rotating machinery, and their health status directly affects the operational reliability of equipment. This paper proposes an optimized wavelet-SVM fault diagnosis method based on multi-source vibration signal fusion: Three-channel inputs are constructed by synchronously collecting vibration signals from the drive end and fan end, along with their differential signals; Wavelet packet decomposition is utilized to extract frequency-domain features such as unit node energy entropy and wavelet coefficient standard deviation, while dimensionless indicators independent of rotational speed (kurtosis factor/waveform factor/impulse factor) are introduced to enhance time-domain characterization; The fused features are input into an RBF-SVM classifier after dimensionality reduction via PCA (retaining 99% variance, reducing dimensions from 102 to 4). Experiments indicate that on the CWRU dataset, this method achieves 97.0% precision, 96.9% recall, and an F1-score of 96.9% (representing a 2.9% improvement over single-source input methods); Although there is a 2.4% absolute accuracy gap compared to deep learning solutions, it possesses significant edge advantages—memory usage is only 12KB and inference latency is 0.6ms—providing a high-precision, low-cost embedded solution for rotating machinery fault diagnosis
