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中文摘要：
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，角域信号一二阶累计值（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。
英文摘要：
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 in- dicators 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.
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