机械科学与技术2018，Vol.37Issue(2)：293-299,7.DOI:10.13433/j.cnki.1003-8728.2018.0221
旋转机械振动信号频域随机压缩与故障诊断
Frequency Domain Random Compression of Vibration Signal and Fault Detection for Rotation Machinery
摘要
Abstract
The fault detection method based on random compression of frequency domain and sparse representation classification for rotation machinery is proposed.The random compression of frequency domain is a way to achieve feature extraction.Fourier Transform converts vibration signal to get the amplitude sequence.And then,the compressive measurement of amplitude sequence is implemented with random matrix as fault feature vector.In sparse representation classification,the fault feature library is composed of fault feature vectors of which fault pattern is known.The classification of test feature vector is converted to a sparse optimization problem.The sparse representation coefficient of test feature vector under fault feature library is obtained using Orthogonal Matching Pursuit.With the sparse representation coefficient,the reconstruction residual of test feature vector under each fault pattern is obtained and the fault detection is done.The effectiveness of the proposed fault detection method is verified through the experiment of gear and bearing vibration.关键词
旋转机械/故障诊断/随机矩阵/稀疏表示Key words
rotation machinery/feature extraction/random matrix/fault detection/optimization分类
机械制造引用本文复制引用
王江萍,段腾飞..旋转机械振动信号频域随机压缩与故障诊断[J].机械科学与技术,2018,37(2):293-299,7.基金项目
国家科技重大专项项目(2011ZX05046-04-07)与西安石油大学全日制硕士研究生优秀学位论文培育项目(2015YP140407)资助 （2011ZX05046-04-07）
