基于模糊故障特征信息的随机集度量信息融合诊断方法

Information Fusion Algorithm of Fault Diagnosis Based on Random Set Metrics of Fuzzy Fault Features

该文给出一种基于模糊故障特征信息随机集度量的信息融合诊断方法。针对信号采集与故障特征提取中的模糊性,首先用模糊隶属度函数分别表示故障档案库中的多种故障样板模式和从不同传感器观测中提取的多类故障特征亦即待检模式,进而基于模糊集的随机集模型,得到样板模式与待检模式的匹配度,即基本概率指派函数(BPA)。然后利用Dempster-Shafer证据组合规则对BPA进行融合,给出诊断结果。该文给出的待检模式是从多个连续观测中提取的,与原有的由单个观测确定待检模式的方式相比,文中提出的特征提取及匹配方法,同时考虑了样板模式和待检模式所具有的模糊性,能够显著降低融合决策中的不确定性,大大提高故障识别的能力。最后通过电机转子故障诊断实例验证方法的有效性。

In order to deal with the uncertainties in feature extraction and decision-making, an information fusion algorithm of fault diagnosis is presented based on random set metrics of fuzzy features and evidence reasoning. Firstly, membership functions are used to describe the fault templates in model database and features extracted from sensor observations. Secondly, a random sets model of fuzzy information is introduced to give a likelihood function, which can be transformed into a Basic Probability Assignment （BPA） function. A BPA numerically shows the support degree of the hypotheses that the machine has certain faults under the fuzzy features. The proposed fuzzy feature is not extracted from single observation but from continuous observations. The fusion diagnosis results based on this proposed feature are more accurate than that based on traditional single observation feature. Finally, the diagnosis results of machine rotor show that the proposed method can enhance diagnostic accuracy and reliability.