复杂机电系统核熵判别分析的异常分类方法

A Classification Method for Abnormal Patterns of Complex Electromechanical System for Discriminant Analysis Nuclear Entropy

为了解决复杂机电系统的海量数据的复杂性和动态性,以及对故障类型快速而有效地进行分类,提出一种基于信息熵的核熵判别分析-KEDA方法。首先,引入了信息熵的观点以便排除信息冗余后剩余的平均信息量能够保证异常模式的有效分类。其次,利用核熵成分分析对数据进行非线性映射和降维,为此确定基于熵的参数选取方法计算和KEDA算法步骤。从而在降维后的空间进行分类。最后,结合TE过程数据集对算法效果进行验证。通过仿真实验得知,提出的KEDA方法的识别率85%以上,表明KEDA方法比其他方法的有效性和优越性,具有一定的应用价值。

In order to solve the complexity and dynamic of massive data of complex electromechanical system in process industry, and to put up with fault data precisely and classify the fault types accurately and efficiently, this paper proposes a kernel entropy discriminant analysis method based on information entropy. Firstly, the concept of information entropy is introduced. Because of the redundancy of information, the redundancy degree is related to the uncertainty of information. The average information amount after excluding redundancy can eliminate the effective classification of abnormal patterns. Secondly, the kernel entropy component analysis is used to nonlinearly map and reduce the dimension of the data. The entropy-based parameter selection method is made to calculate steps and KEDA algorithm steps, so as to classify the space after dimension reduction. Finally, the effectiveness of the algorithm is verified by combining with the TE process dataset, and the effectiveness, superiority and rationality of the algorithm are verified by simulation experiments. The results show that KEDA method proposed in this paper has certain application value compared with other methods.