基于MMD的故障可诊断性定量评价方法

提出一种基于最大均值差异(maximum mean discrepancy,MMD)的故障可诊断性定量评价方法.该方法无需构建任何系统模型,通过度量不同故障模式下测量数据之间的距离定量评价故障可诊断性,适用于结构复杂、不易于建模且能够获取测量数据的复杂系统.首先,将测量数据通过特征核映射到可再生核希尔伯特空间(reproducing kernel Hilbert space,RKHS)中,以MMD作为多元分布距离度量指标,将故障可诊断性定量评价问题转换为多元分布在RKHS中的距离度量问题;然后,利用数学推导分析测量噪声强度对故障可诊断性评价结果的影响;最后,通过仿真实例验证所提出方法的有效性.

Quantitative evaluation approach of fault diagnosability based on maximum mean discrepancy

This paper proposes a method of quantitative evaluation of fault diagnosability based on maximum mean discrepancy(MMD). The method evaluates the fault diagnosability quantitatively by measuring the distance between measurement data under different fault conditions without building any system model. It is suitable for the system with complex structures that are difficult to build models and obtain measurement data. Firstly, the measurement data is mapped to the reproducing kernel Hilbert space(RKHS) through the characteristic kernel. The MMD is taken as the distance measure of multivariate distributions, and the fault diagnosability quantitative evaluation is transformed into the distance measurement of multivariate distributions in the RKHS. Then, the influence of measurement noise intensity on the result of fault diagnosability evaluation is analyzed by mathematical derivation. Finally, the validity of the proposed method is verified by simulation analysis.