摘要
针对航空发动机中介轴承振动信号故障微弱,故障特征难提取的问题,提出了基于固有时间尺度分解(ITD)和近似熵(AE)结合随机森林(RF)的航空发动机中介轴承故障诊断方法。首先,利用航空发动机中介轴承试验台模拟并采集轴承在正常、外圈故障、滚动体故障三种状态下的振动信号;然后通过ITD方法将非平稳、非线性的中介轴承振动信号分解成一组固有旋转分量(PR),计算其近似熵;最后,将不同尺度的近似熵值作为特征向量,输入到随机森林分类器模型中进行分类识别与故障诊断。研究表明,该方法能有效提取出机匣表面振动信号中微弱的中介轴承振动故障信号特征,故障诊断准确率高,具有工程实用性。
For the lower accuracy and poor stability limitation of single classifier,a method of aeroengine intermediary bearing fault diagnosis based on intrinsic time scale decomposition(ITD)and approximate entropy combined with random forest(RF)was proposed in it.Firstly,vibration signals of normal bearing,outer ring defect and rolling element defect of an aeroengine intermediary bearing fault simulation rig were measured.Then,the nonlinear and nonstationary signals of intermediary bearing were decomposed to a set of inherent rotating components(PR)using ITD method,and the approximate entropy were calculated.Finally,values of different scale approximate entropy as feature vectors were input to the random forest(RF)classifier for fault classification and diagnosis.It’s shown that the proposed method can effectively extract weak fault signal characteristics of aeroengine mediation bearings from casing surface vibration signals.The method has a high accuracy for aeroengine intermediate bearing fault diagnosisis with a certain engineering practicability.
作者
艾延廷
董欢
田晶
孙志强
AI Yan-ting;DONG Huan;TIAN Jing;SUN Zhi-qiang(Liaoning Key Laboratory of Advanced Measurement and Test Technology for Aircraft Propulsion Systems,Shenyang Aerospace University,Liaoning Shenyang 110136,China)
出处
《机械设计与制造》
北大核心
2018年第10期157-160,164,共5页
Machinery Design & Manufacture
基金
中航工业产学研专项(cxy2012sh17)
关键词
中介轴承
固有时间尺度分解
随机森林
近似熵
故障诊断
Intermediate Bearing
Intrinsic Time Scale Decomposition
Random Forests
Approximate Entropy
Fault Diagnosis
