基于KPCA SVM的柴油机状态识别方法的研究

为了有效地对柴油机的运行状态进行状态识别,根据柴油机的特征信息和识别的特点,研究了基于核主元分析(KPCA)和支持向量机(SVM)进行柴油机状态识别的故障诊断方法.首先,对柴油机进行特征提取,构成一个特征向量.然后对其进行核主元分析,计算得到能反映设备状态的特征向量,有效去除信息的冗余.最后,将得到的特征向量进行支持向量机的训练学习,识别柴油机的状态.通过实验室柴油机燃烧系统不同运行状态下的识别分析,验证了此方法的可行性和实用性.

Pattern Recognition of Diesel Engine by Using Kernel Principle Component Analysis and Support Vector Machine

To effectively recognize the operating condition of a diesel engine, this paper presented a new method by com bining kernel principle component analysis (KPCA) and support vector machine (SVM) for the pattern recognition. Firstly, feature extraction was used to the diesel engin e vibration signal, and a multiple parameter vector was obtained. Secondly, a new fe ature vector resulted from the feature vector by using KPCA whi ch can demonstrate the operating condition. Redundancy information was removed from the vector during KPCA. Lastly, the condition of the diesel engine was classified by inputting the new feature vector to SVM for train ing and recognizing. The combustion system pattern recognition of a diesel engine in a lab was used as an example to testify the method. The results show that the method is effective for pattern recogniti on of a diesel engine.