摘要
螺栓松动是一种常见且具有潜在危害的机械故障。考虑到螺栓松动会导致被联接件结合部动力参数发生变化,提出了一种基于两被联接件振动信号的松动识别方法。所提方法首先计算两信号的概率密度,并对概率密度曲线进行网格化处理生成概率矩阵,继而对概率矩阵进行主元分析(PCA),在合并两路信号经主元分析后所得投影矩阵之后,再次进行主元分析和投影。设计了两种识别方式,方式1首先按上述过程进行已知样本训练以得到各松动状态投影点,识别时根据所得投影点与各状态投影点间的欧式距离进行判断;方式2使用螺栓紧固状态时所得样本数据和现场实测数据直接按上述过程进行计算,并根据PCA特性设计了松动判别条件。试验验证表明所提方法能够准确区分不同松动状态,且识别方式2操作简便,无需故障样本,易于实际应用。
Considering that the bolt looseness induces the variation of dynamic parameters of joint of bolt connection,a detection method was proposed based on vibration signals of two connected parts.The probability densities of the two signals were calcuated,the probability density curves were processed by a mesh to obtain probability matrixes, and were the probability matrixes were transformed by using principal component analysis （PCA）method.After PCA,two projection matrixes of the probability matrixes were merged to one matrix.To this matrix,PCA and projection were implemented again.According to the peculiarity of bolt looseness,tow detection modes are designed based on the proposed method.The mode 1 carries out training work using foregone samples to generate projection points of each looseness condition employing the proposed method,then in detection,Euclidean distance between projection point and each foregone projection point was used as estimation criterion.The mode 2 directly detects looseness condition by using proposed method based on the signals of tight bolt connection condition and field measurement,and a criterion for judging bolt looseness was designed.The experimental verification shows that the proposed method can distinguish different looseness conditions,and the detection mode 2 is easy and simple to be handled without foregone fault samples.
出处
《振动与冲击》
EI
CSCD
北大核心
2015年第1期63-67,共5页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(51275080)
关键词
螺栓松动
概率密度
主元分析
故障诊断
bolt looseness
probability density
principal component analysis
faults diagnosis
