机床与液压
機床與液壓
궤상여액압
MACHINE TOOL & HYDRAULICS
2012年
6期
54-66
,共13页
陈林%邵毅敏%Chris K.Mechefske%朱新才
陳林%邵毅敏%Chris K.Mechefske%硃新纔
진림%소의민%Chris K.Mechefske%주신재
间隙故障%发动机%诊断%角度域
間隙故障%髮動機%診斷%角度域
간극고장%발동궤%진단%각도역
Clearance fault%Engine%Diagnosis%Angle domain
曲柄连杆机构是内燃机的关键部件,它将往复运动转换成旋转运动.由于内燃机经常运行在变工况环境,存在复杂的非线性激励因素,所以识别连杆轴承和曲轴轴颈间隙异常故障是一个具有挑战性的难题.为了解决这一难题,提出了基于角域信号预处理的角域信号二阶累计值( AS-SOAI)算法,即:利用角域采样消除内燃机信号的非平稳性,再运用离散小波降噪消除原始信号中的噪声成分,通过新提出的参数指标(RMSSOC:二阶累积均方根,KSOC:二阶累积峭度)可识别内燃机的运行状态.不同异常间隙程度和偏差情况的试验和分析结果表明,角域信号-二阶累计值(AS-SOAI)算法,可应用于内燃机不同状态下的间隙异常故障识别,且可靠性和准确性高.
麯柄連桿機構是內燃機的關鍵部件,它將往複運動轉換成鏇轉運動.由于內燃機經常運行在變工況環境,存在複雜的非線性激勵因素,所以識彆連桿軸承和麯軸軸頸間隙異常故障是一箇具有挑戰性的難題.為瞭解決這一難題,提齣瞭基于角域信號預處理的角域信號二階纍計值( AS-SOAI)算法,即:利用角域採樣消除內燃機信號的非平穩性,再運用離散小波降譟消除原始信號中的譟聲成分,通過新提齣的參數指標(RMSSOC:二階纍積均方根,KSOC:二階纍積峭度)可識彆內燃機的運行狀態.不同異常間隙程度和偏差情況的試驗和分析結果錶明,角域信號-二階纍計值(AS-SOAI)算法,可應用于內燃機不同狀態下的間隙異常故障識彆,且可靠性和準確性高.
곡병련간궤구시내연궤적관건부건,타장왕복운동전환성선전운동.유우내연궤경상운행재변공황배경,존재복잡적비선성격려인소,소이식별련간축승화곡축축경간극이상고장시일개구유도전성적난제.위료해결저일난제,제출료기우각역신호예처리적각역신호이계루계치( AS-SOAI)산법,즉:이용각역채양소제내연궤신호적비평은성,재운용리산소파강조소제원시신호중적조성성분,통과신제출적삼수지표(RMSSOC:이계루적균방근,KSOC:이계루적초도)가식별내연궤적운행상태.불동이상간극정도화편차정황적시험화분석결과표명,각역신호-이계루계치(AS-SOAI)산법,가응용우내연궤불동상태하적간극이상고장식별,차가고성화준학성고.
Crankshaft and connecting rod mechanism is an important component which transforms the reciprocating motion to rotating motion in a internal combustion engine(ICE).It is a difficult and challenging task to identify abnormal clearance between the connecting rod bearing(CRB) and the crankshaft rod journal(CRJ) because of variable working conditions and complicated nonlinear excitation.In order to solve this problem,the use of second order accumulation indicators from per-processed angle domain signals(AS-SOAI) is proposed.First,an angle domain sampling technique is employed in order to eliminate the non-stationary property of the signals.Then the discrete wavelet transform is used to remove any significant noise from the raw signal.Finally the internal combustion engine condition can be easily diagnosed through the proposed indicators ( RMS of second order cumulant,RMSSOC,and kurtosis of second order cumulant,KSOC).The AS-SOAI is applied to the data sets,which are collected from engines working with different levels of abnormal clearance and deviation.The results show the proposed method can reliably and accurately detect the different states of the internal combustion engine.