基于离散广义S变换与双向二维主成分分析的内燃机故障诊断

I.C.Engine Fault Diagnosis Based on Discrete Generalized S-transformation and TD-2DPCA

针对内燃机气阀机构的故障诊断问题,提出一种将离散广义S变换和双向二维主成分分析(TD-2DPCA)相结合的诊断方法。该方法首先利用离散广义S变换将内燃机缸盖振动信号生成振动谱图像,然后利用TD-2DPCA对图像进行特征提取,有效减小特征系数矩阵的维数,最后,通过最近邻分类器进行分类识别。将该方法应用于内燃机气阀机构8种工况的诊断实例中,对比不同时频表征及特征提取方法的计算效率和识别精度,结果表明该方法可为内燃机故障诊断提供一条新途径。

For the problems of fault diagnosis of I.C.engines,a method was proposed,which is consisted of discrete generalized S-transformation and TD-2DPCA.First of all,vibration spectrum images of cylinder head vibration signals were generated by discrete generalized S transform.Secondly,image matrix was bidirectional compressed by TD-2DPCA to reduce sizes of feature coefficient matrix effectively.Lastly,these feature matrixes obtained from image projects were served as enters of nearest neighbor classifier,which was used to achieve fault types division.The method was applied to diagnosis example of the vibration signals of valve mechanism eight operating modes,comparisons of different time-frequency characterizations and feature extraction methods were carried out.The results show that the proposed method provides a new way for fault diagnosis of I.C.engines.