针对齿轮传统健康评估方法中特征描述单一,齿轮箱中多种参数信息未能有效利用的问题,为更准确地监测齿轮运行的健康状况,提出一种基于异类特征信息融合的齿轮健康评估方法。对前期正常状态运行的齿轮采集振动、油液、铁谱等多种信号并提取特征指标,建立各类特征的模糊C均值聚类中心;利用模糊理论输出待测信号对于正常状态信号的隶属度作为各类特征的健康评估指标;以隶属度构造基本概率赋值函数,应用D-S证据理论组合规则,在决策层进行异类信息融合,从而完成齿轮的健康评估。通过对齿轮全寿命试验的数据处理与对比分析,证明该方法能够有效地评估齿轮健康状况。
Abstract
Aiming at problems in gear traditional health assessment of feature description being unitary and information of many parameters in gear box being not used effectively, a gear health assessment method based on heterogeneous information fusion was proposed to more correctly monitor gear health during operation.Firstly, various signals including vibration, oil fluid and ferrography, etc.were collected and their feature indexes were extracted for a gear in early normal operation state, and the fuzzy C-means (FCM) clustering center was established for each kind of features.Secondly, the fuzzy theory was used to output the membership degree of the signal to be measured to the normal state signal as the health assessment index of various kinds of features.Finally, the membership degree was used to construct the basic probability assignment function, and the combined rules of Dempster-shafer (DS) evidence theory was adopted to perform heterogeneous information fusion at the decision-making level to complete a gear’s health assessment.The effectiveness of the proposed method was verified through data processing and contrastive analysis for gear whole life tests.
关键词
异类信息融合 /
模糊C均值 /
D-S证据理论 /
健康评估 /
齿轮
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Key words
heterogeneous information fusion /
fuzzy C-mean (FCM) /
DS evidence theory /
health assessment /
gear
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