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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。
Crank linkages are a key part of internal combustion engines that convert reciprocating motion into rotational motion. Due to the complicated non-linear excitation factors that the internal combustion engine often runs in a variable working environment, it is a challenging problem to identify the abnormality of the connecting rod bearing and crankshaft journal gap. In order to solve this problem, the second-order integrated angular-domain signal (AS-SOAI) algorithm based on the preprocessing of the angular domain signal is proposed, which uses the angular domain sampling to eliminate the nonstationarity of the internal combustion engine signal and then uses the discrete wavelet denoising The noise components of the original signal can be identified by the newly proposed parameter indicators (RMSSOC: second-order cumulative root-mean-square, KSOC: second-order cumulative kurtosis). The experimental and analysis results of different abnormal clearances and deviations show that the angular-signal-second-order accumulated value (AS-SOAI) algorithm can be applied to the identification of abnormal clearance in different states of internal combustion engine with high reliability and accuracy.