摘要
针对涡旋压缩机振动信号的不稳定性及难以获取大量故障样本的问题,提出了一种信息熵融合与粒子群优化(particle swarm optimization,简称PSO)的支持向量回归(support vector classification,简称SVC)涡旋压缩机故障诊断方法。通过奇异谱熵和功率谱熵分析,分别提取振动信号时域与频域特征,采用变分模态分解(variational modede composition,简称VMD)能量熵衡量故障振动信号时⁃频域特征,利用因子分析融合奇异谱熵、功率谱熵和能量熵值得到单一评价指标特征向量。将评价指标作为PSO⁃SVC模型的输入,通过训练建立PSO⁃SVC涡旋压缩机故障分类模型。实验结果表明,该方法在小样本情况下,仍能有效地对涡旋压缩机4种典型故障类型进行分类,准确率达到94.5%。
Aiming at the instability of vibration signal of scroll compressors and the difficulty in obtaining a large number of fault samples,a fault diagnosis method based on information entropy fusion and particle swarm opti⁃mization-support vector classification(PSO-SVC)for scroll compressors is proposed to determine its fault types.Firstly,the singular spectrum entropy of time-domain,the power spectrum entropy of frequency-domain and the variational mode decomposition(VMD)energy entropy of time-frequency domain are extracted to calcu⁃late the information entropy of vibration signals.Then the single evaluation index eigenvector is obtained by fus⁃ing the above three types of entropy using factor analysis method.Lastly,the scroll compressor PSO-SVC fault classification model is established through signal data training after the evaluation index is input into the PSOSVC model.The experimental results show that this method can effectively classify four typical fault types of scroll compressors with 94.5%accuracy in the case of small sample size.
作者
刘涛
梁成玉
LIU Tao;LIANG Chengyu(School of Mechanical and Electrical Engineering,Lanzhou University of Technology Lanzhou,730500,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2022年第1期141-147,200,共8页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51665035)。
关键词
信息熵融合
粒子群优化⁃支持向量回归
涡旋压缩机
故障诊断
information entropy fusion
particle swarm optimization-support vector classification(PSO-SVC)
scroll compressor
fault diagnosis
