Individual instances of the knock resonant response are easy to acquire but these are subject to noise and vary considerably from cycle to cycle due to random variations in the knock process. This work provides a new way to quantify and model the stochastic properties of knock signals, capturing both the time domain resonant characteristics within a cycle as well as the random variations from cycle to cycle. A new phase alignment method enables the ensemble mean knock waveform to be identified from the data which also removes noise components without the need for narrowband filtering. This ensemble waveform shows the empirical characteristics of knock onset, decay, and frequency slurring within the cycle as the gas expands and cools. The phase-aligned cyclic variations of the knock waveform are also shown to approximate a (time-varying) dual-Gaussian distribution, and fitting such a model to the data enables the statistical properties of the dataset as a whole to be decomposed into separate knocking and non-knocking populations providing further insight into the knock process. The technique is applied both to filtered cylinder pressure signals and to accelerometer-based knock signals, and the results are compared and contrasted.

爆震共振响应的个别实例很容易获得，但由于爆震过程中的随机变化，它们会受到噪声的影响，并且在每个周期之间变化很大。这项工作提供了一种新的方法来量化和建模爆震信号的随机特性，捕获周期内的时域共振特性以及周期之间的随机变化。一种新的相位对齐方法可以从数据中识别出整体平均爆震波形，该方法还可以去除噪声分量，而无需进行窄带滤波。该整体波形显示了随着气体膨胀和冷却，循环内爆震开始、衰减和频率模糊的经验特征。爆震波形的相位对齐循环变化也显示为近似（随时间变化的）双高斯分布，并且将这种模型与数据拟合使数据集作为一个整体的统计特性能够分解为单独的爆震和非敲击种群提供了对敲击过程的进一步了解。该技术应用于过滤后的气缸压力信号和基于加速度计的爆震信号，并对结果进行比较和对比。