基于多尺度核独立成分分析的柴油机故障诊断

为提高利用缸盖振动信号进行柴油机故障诊断的精度和速度,提出了一种基于多尺度核独立成分分析提取故障敏感频带的柴油机故障诊断方法。首先,提出奇异值能量标准谱对缸盖振动信号中的微弱冲击特征进行增强;然后,对信号进行固有时间尺度分解,并基于相关性准则选择有效频带分量;最后,利用核独立成分分析消除有效频带之间的频带混叠,得到故障敏感信息集中的独立频带,并计算其自回归模型(auto regression model,简称AR)参数、模糊熵和标准化能量矩作为特征向量输入核极限学习机(kernel extreme learning machine,简称KELM)进行柴油机故障诊断。试验分析结果表明,该方法可以快速准确地提取缸盖振动信号中的柴油机故障敏感频带,增强故障敏感特征,故障诊断准确率达到99.65%。

Diesel Engine Fault Diagnosis Based on Multi-scale Kernel Independent Component Analysis

In order to improve the speed and accuracy of diesel engine fault diagnosis by using the cylinder head vibration signal, a diesel engine fault diagnosis method of extracting the fault sensitive frequency bands is presented based on multi-scale kernel independent component analysis. Firstly, the singular value energy standard spectrum is proposed to enhance the weak shock characteristics of the vibration signal. Then the signal is decomposed into several different frequency bands by intrinsic time-scale decomposition, and the effective frequency components are selected according to the correlation criterion. Finally, the frequency aliasing between different effective components is eliminated by using kernel independent component analysis in order to obtain independent components, which contain the fault sensitive information, and AR model parameters, fuzzy entropy and standardized energy moment of each independent component are extracted to be feature vectors. They are input into the kernel extreme learning machine (KELM) in order to diagnose different running faults of the diesel engine. The test result indicates that the proposed method can effectively extract the fault sensitive frequency bands in cylinder head vibration signal, enhance the fault features and improve the fault diagnosis accuracy to 99.65%.