基于模糊贝叶斯风险和T-S模糊模型的故障诊断

为解决复杂装备故障诊断中的知识获取和决策制定问题,提出一种数据驱动的故障诊断方法。利用模糊贝叶斯风险模型以风险最小化原则挖掘数据中有价值知识,得到相对最优属性子集,其中生成的概率分布用于T-S(Takagi-Sugeno)模糊规则提取,以分段线性化思想逼近复杂的数据知识。在数值实验中,以C-MAPSS(Commercial Modular Aero-Propulsion System Simulation)发动机数据为研究对象,验证本文方法的有效性,结果表明本文方法适用于复杂装备的故障诊断。与其他知识获取方法对比表明,本文方法可得到更高的诊断准确率。

Fault Diagnosis Based on Fuzzy Bayes Risk and T-S Fuzzy Model

A kind of data-driven fault diagnosis approach is proposed to solve the problems of knowledge acquisition and decision making in the diagnosis of complex equipment.Using the fuzzy Bayes risk model to mine the valuable knowledge under the raw data with the principle of risk minimization to obtain the relative optimal attribute subset.The generated probability distribution is used for T-S fuzzy rule extraction,and the complex data knowledge is approximated according to the idea of piecewise linearization.In the numerical experiments,two diagnostic cases were carried out to illustrate the efficiency of the proposed method by utilizing the data of C-MAPSS,and the results show that the proposed method is suitable for fault diagnosis of complex equipment.Compared with other knowledge acquisition methods,this method can achieve higher diagnostic accuracy.