首页> 外文会议>SAE World Congress and Exhibition >Controlling Low-Speed Pre-Ignition in Modern Automotive Equipment: Defining Approaches to and Methods for Analyzing Data in New Studies of Lubricant and Fuel-Related Effects (Part 2)
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Controlling Low-Speed Pre-Ignition in Modern Automotive Equipment: Defining Approaches to and Methods for Analyzing Data in New Studies of Lubricant and Fuel-Related Effects (Part 2)

机译:控制现代汽车设备中的低速预点火:定义分析润滑油和燃料相关效果新研究中数据的方法和方法(第2部分)

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In recent years, an abnormal combustion phenomenon called low-speed pre-ignition (LSPI) has arisen from the downsizing of gasoline engines in order to improve fuel economy and comply with global CO_2 legislation. The type and quality of the fuel and lubricant has been found to influence LSPI occurrence rates. A methodology for studying LSPI has been implemented, and a rigorous statistical approach for studying the data from a stationary engine test can provide consistent results as shown in Part 1 of the series. LSPI events can be determined by an iterative statistical procedure based on calculating the mean and standard deviation of peak pressure (PP) and crank angle location of 2% mass fraction burned (MFB02) data, determining cycles with parameters which exceeded n standard deviations from the mean and identifying outliers. Outliers for the PP and MFB02 metrics are identified as possible LSPI events.Further investigation was conducted to refine the approaches and methods used for analyzing data. This paper expands on some of the methodology described in Part 1 and explores the assumption of normal distribution that is used to determine the number of standard deviations beyond which correspond to outliers. The application of a method for adjusting departure from normality in terms of skewness and kurtosis is explored to reduce the frequency of false positives and negatives.
机译:近年来,从汽油发动机的缩小尺寸下出现了一种称为低速点火(LSPI)的异常燃烧现象,以改善燃料经济性并遵守全球CO_2立法。已发现燃料和润滑剂的类型和质量影响LSPI发生率。已经实施了用于研究LSPI的方法,并且对于从静止发动机测试中研究数据的严格统计方法可以提供一致的结果,如系列的第1部分所示。 LSPI事件可以通过基于计算峰值压力(PP)和曲柄角位置的迭代统计程序来确定2%质量分数燃烧(MFB02)数据的曲柄角位置,确定具有超过N标准偏差的参数的循环均值和识别异常值。 PP和MFB02度量的异常值被确定为可能的LSPI事件。进行了调查,以优化用于分析数据的方法和方法。本文扩展了第1部分中描述的一些方法,并探讨了用于确定与异常值相对应的标准偏差数量的正常分布的假设。探讨了在偏离和峰氏症方面调整偏离正常性的方法,以减少误报和底片的频率。

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