CAJ | 학술논문

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.
해문급출일충기우모호고장특정신식수궤집도량적신식융합진단방법.침대신호채집여고장특정제취중적모호성,수선용모호대속도함수분별표시고장당안고중적다충고장양판모식화종불동전감기관측중제취적다류고장특정역즉대검모식,진이기우모호집적수궤집모형,득도양판모식여대검모식적필배도,즉기본개솔지파함수(BPA).연후이용Dempster-Shafer증거조합규칙대BPA진행융합,급출진단결과.해문급출적대검모식시종다개련속관측중제취적,여원유적유단개관측학정대검모식적방식상비,문중제출적특정제취급필배방법,동시고필료양판모식화대검모식소구유적모호성,능구현저강저융합결책중적불학정성,대대제고고장식별적능력.최후통과전궤전자고장진단실례험증방법적유효성.
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 reliabili.