基于Wigner分布和分形维数的柴油机故障诊断

Study on Fault Diagnosis of Diesel Valve Trains Based on Wigner Distribution and Fractal Dimension

针对柴油机配气机构故障诊断问题,提出了一种基于Wigner分布和差分分形盒维数的故障诊断方法。首先,利用改进局部均值分解算法对柴油机缸盖振动信号进行分解,并采用相关性分析剔除噪声和伪分量;然后,分别对各相关分量进行Wigner时频分析,将结果线性叠加得到振动时频图,再提取图像的差分分形盒维数作为故障特征;最后,利用k-最近邻(k-NN)实现故障诊断。仿真结果表明,改进局部均值分解算法可以抑制Wigner分布交叉项的干扰。实验结果显示,差分分形盒维数优于其他6种典型故障特征,利用本研究提出的方法对配气机构进行故障诊断的正确率为97.2%,该方法可以用于柴油机配气机构故障诊断。

Aiming at the problem of fault diagnosis in diesel engine valve trains,a fault diagnosis method was proposed based on Wigner distribution and differential box-counting fractal dimension.First,the improved local mean decomposition(LMD)was used to decompose the vibration signals of the cylinder head into several product function(PF)components,and the correlation analysis was selected to eliminate noise and pseudo components.Second,for each relevant component,Wigner distribution was calculated sepa-rately and then accumulated to construct the time-frequency image of the vibration signals.Then,the differential box-counting fractal dimension was extracted as the fault feature.Finally,the k-newerest neighbor algorithm(k-NN)was used to fulfill the fault diagnosis task of diesel valve trains.The simulation results showed that the improved LMD method efficiently suppressed the cross-term of Wigner distribution.The experimental results showed that the differential box-counting fractal dimension was superior to the other six kinds of typical fault characteristics,and the fault diagnosis accuracy was 97.2%.Therefore,the proposed method can be used to diagnosis the fault of diesel engine valve trains.