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.
Key words
heterogeneous information fusion /
fuzzy C-mean (FCM) /
DS evidence theory /
health assessment /
gear
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