CAJ | 학술논문

针对新研制的多功能油液磨粒智能检测系统MIDCS中的磨粒图像识别问题，引入数据挖掘方法获取了磨粒图像识别的知识规则，实现对磨粒类别的智能识别。利用MIDCS系统获取实际航空发动机运行过程中由于滚动轴承磨损而产生的大量典型磨粒，基于图像分析方法提取16个磨粒特征参数，形成标准案例库；利用Weka软件的决策树算法自动提取知识规则，并对知识规则进行优化和简化；对所提取得到的知识规则进行验证和分析。结果表明，所提取的磨粒识别规则符合磨粒识别的统计规律，识别规则不仅简洁，而且具有很高的精度。基于Weka软件的规则提取方法大大提高了MIDCS系统的磨粒识别自动化和智能化水平，对于利用MIDCS进行航空发动机滚动轴承疲劳磨损故障诊断，具有重要的工程实用价值。
침대신연제적다공능유액마립지능검측계통MIDCS중적마립도상식별문제，인입수거알굴방법획취료마립도상식별적지식규칙，실현대마립유별적지능식별。이용MIDCS계통획취실제항공발동궤운행과정중유우곤동축승마손이산생적대량전형마립，기우도상분석방법제취16개마립특정삼수，형성표준안례고；이용Weka연건적결책수산법자동제취지식규칙，병대지식규칙진행우화화간화；대소제취득도적지식규칙진행험증화분석。결과표명，소제취적마립식별규칙부합마립식별적통계규률，식별규칙불부간길，이차구유흔고적정도。기우Weka연건적규칙제취방법대대제고료MIDCS계통적마립식별자동화화지능화수평，대우이용MIDCS진행항공발동궤곤동축승피로마손고장진단，구유중요적공정실용개치。
Aimed at the wear particle recognition problem of the new Multiple Intelligent Debris Classifying System ( MIDCS) , data mining method was introduced in order to obtain the knowledge rules of wear particle recognition, and the expert system theory was used to realize the intelligent recognition of debris classes. A large number of typical debris caused by rolling bearing wear in the actual aero?engine operational process was obtained by MIDCS, 16 debris character?istic parameters were extracted based on the image analysis method, and the standard case library was formed. The deci?sion tree algorithm of the Weka software was used for automatic extraction of the knowledge rules, and the knowledge rules were opti?mized and simplified. The extracted knowledge rules were verified and analyzed. The results show that the rules agree well with the wear particles recognition statistical laws, the extracted rules is very brief and correct, the extraction method based on Weka software can be used in the debris class recognition of MIDCS well,and the automation and intelli?gent level of MIDCS debris class recognition are greatly improved. It is of significant engineering value for the aero?engine rolling bearing fatigue wear fault diagnosis by using MIDCS.