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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Gaussian Mixture Model Clustering-Based Knock Threshold Learning in Automotive Engines
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Gaussian Mixture Model Clustering-Based Knock Threshold Learning in Automotive Engines

机译：高斯混合模型基于集群的汽车发动机爆震阈值学习

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摘要

In this article, a Gaussian mixture model (GMM) clustering-based method is proposed to learn the optimal threshold of the knock intensity metric, which minimizes the probability of the judgment error of the knock event. First, statistical analysis of knock intensity on an experimental database measured from a gasoline engine test bench is conducted and the results show that GMM has better fitting performance than the single Gaussian model. Then, the learning problem which consists of two subproblems is formulated based on the assumption that the probability distribution model of knock intensity is a two-component GMM. The first subproblem is a clustering problem in which parameters of the two-component GMM are optimized to maximize the likelihood of obtaining the measurements. The second subproblem is also an optimization problem in which the probability of the judgment error is formulated as a function of the threshold. The solution to the problem is the required threshold decision. The convergence analysis of the proposed method is implemented. Furthermore, the proposed method is also validated in the experimental database. The proposed method is expected to be applied in the real-world application to save time, labor effort on the knock threshold calibration.
机译：在本文中，提出了一种高斯混合模型（GMM）基于聚类的方法，以了解爆震强度度量的最佳阈值，这最小化了爆震事件的判断误差的概率。首先，进行从汽油发动机测试台测量的实验数据库上的爆震强度的统计分析，结果表明，GMM具有比单个高斯模型更好的拟合性能。然后，基于爆震强度的概率分布模型是双组分GMM的假设来制定由两个子问题组成的学习问题。第一个子问题是一个聚类问题，其中两组组件GMM的参数被优化，以最大化获得测量的可能性。第二个子问题也是一种优化问题，其中判断误差的概率被配制为阈值的函数。解决问题的解决方案是所需的阈值决定。实施了该方法的收敛分析。此外，该方法还在实验数据库中验证。预计该方法将应用于现实世界应用程序以节省时间，劳动力努力爆震阈值校准。

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