噪声与振动控制2026，Vol.46Issue(3)：124-130,7.DOI:10.3969/j.issn.1006-1355.2026.03.019
基于ASFSSA-SVM的电机滚动轴承故障诊断
Fault Diagnosis of Motor Rolling Bearings Based on ASFSSA-SVM
摘要
Abstract
Aiming at the problems of the difficulty to extract the fault features from the bearing vibration signals and insufficient diagnosis accuracy,a fault diagnosis model was proposed.In this method,the feature vector was extracted from the optimized variational modal decomposition by using the adaptive spiral flying sparrow search algorithm,and used as the input to optimizes the support vector machine by means of the adaptive spiral flight sparrow search algorithmas for diagnostic recognition.First of all,the number of modes K and the penalty parameter of the adaptive spiral flight sparrow search algorithm were optimized for the variational modal decomposition,and the multiple Intrinsic Mode Function components were obtained by using VMD signal processing.Then,the optimal IMF components were screened out by using the craggy value as the evaluation index,and the mean value,square difference,peak value,peak factor,impulse factor,and waveform factor of the optimal IMF components were computed as the feature quantities.Finally,the fault identification classification was performed in the ASFSSA-SVM model.The experimental results show that this method possesses excellent diagnostic efficiency across different experimental data.关键词
故障诊断/滚动轴承/优化支持向量机/自适应螺旋飞行麻雀搜索算法Key words
fault diagnosis/rolling bearing/optimized support vector machine/adaptive spiral flying sparrow search algorithm分类
机械制造引用本文复制引用
盛敬,孙涛,刘国满,吴树良,马欣..基于ASFSSA-SVM的电机滚动轴承故障诊断[J].噪声与振动控制,2026,46(3):124-130,7.基金项目
国家自然科学基金(51865031) （51865031）
