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Zhang, Haitao, Guan, Li, Chang, Long. (1404). A Vibration-Based Fault Diagnosis Method for Rolling Bearings via Optimized Wavelet-SVM Fusion. فناوری آموزش, (), -. doi: 10.22061/jcarme.2025.10582.2401
Haitao Zhang; Li Guan; Long Chang. "A Vibration-Based Fault Diagnosis Method for Rolling Bearings via Optimized Wavelet-SVM Fusion". فناوری آموزش, , , 1404, -. doi: 10.22061/jcarme.2025.10582.2401
Zhang, Haitao, Guan, Li, Chang, Long. (1404). 'A Vibration-Based Fault Diagnosis Method for Rolling Bearings via Optimized Wavelet-SVM Fusion', فناوری آموزش, (), pp. -. doi: 10.22061/jcarme.2025.10582.2401
Zhang, Haitao, Guan, Li, Chang, Long. A Vibration-Based Fault Diagnosis Method for Rolling Bearings via Optimized Wavelet-SVM Fusion. فناوری آموزش, 1404; (): -. doi: 10.22061/jcarme.2025.10582.2401

A Vibration-Based Fault Diagnosis Method for Rolling Bearings via Optimized Wavelet-SVM Fusion

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 مهر 1404    اصل مقاله (1.05 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2025.10582.2401
نویسندگان
Haitao Zhang* 1؛ Li Guan1؛ Long Chang2
1College of Mechanical Engineering， Nanjing Vocational University of Industry Technology, Nanjing , China
2Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
تاریخ دریافت: 20 دی 1402،  تاریخ بازنگری: 27 شهریور 1404،  تاریخ پذیرش: 06 مهر 1404 
چکیده
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
کلیدواژه‌ها
Statistical features؛ wavelet packet decomposition؛ Gaussian kernel function؛ Principal component analysis؛ Prediction accuracy
مراجع
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