基于EMD和模糊聚类的柴油机故障诊断

Fault Diagnosis Based on EMD and Fuzzy Clustering for Diesel Engine

根据柴油机故障振动信号的特点,提出了一种经验模式分解和模糊c均值聚类相结合的柴油机振动信号故障诊断新方法.首先,采用经验模式分解方法对柴油机排气门间隙为0.4,0.6及0.75mm的3种工况下的振动信号时间序列进行分解,对分解求得的前6个固有模态函数分别求其能量比并将其作为反映故障状态的特征参数,再利用模糊c均值聚类方法对特征参数进行聚类分析.实验结果表明:所有样本的测试结果均与实际状况相一致,该方法可以有效地对气门间隙故障进行诊断.

According to the traits of fault vibration signals from a diesel engine, a new method was presented for the fault diagnosis of the signals based on EMD（empirical mode decomposition） and FCM（fuzzy c-means clustering）. The time series of vibration signals of three different air valve clearances （0.4, 0.6 and 0.75 mm） was decomposed via EMD, then the energy ratios in percentage of the first six intrinsic modal functions obtained by EMD were calculated separately, and the relevant energy ratios were taken as the characteristic parameters reflecting what state the fault is in. Those parameters were analyzed by FCM algorithm. Experiments indicated that the diagnosed results of all samples conform to actualities, i.e. the method can effectively diagnose the faults of air valve clearance of a diesel engine.