配气机构动力学试验方法与模型规划研究
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摘要
配气机构控制发动机进排气过程，对发动机性能具有重要影响。随着发动机技术的发展，配气机构动力学问题逐渐受到重视并得到深入研究。配气机构动力学特性试验和动力学模型分析已经广泛应用于配气机构设计和研究中。但是，目前配气机构动力学特性试验方法缺乏统一的规范，没有对其数据处理方法进行有针对性的研究，动力学模型的工况适应性研究尚不充分，缺少配气机构动力学模型计算结果与试验结果一致性评价依据。
针对目前配气机构动力学研究中的问题，本课题分析了现有试验和数据处理方法，将多传感器数据融合技术应用到配气机构动力学试验数据处理中，并在试验基础上，对现有配气机构动力学模型进行规划研究，分析各种动力学模型的适用范围，并提出动力学模型计算气门加速度与试验结果一致性的评价依据。
首先，本课题建立了配气机构综合试验系统，将配气机构动力学特性试验和配气机构可靠性试验在一个试验系统上完成。该系统对目前所采用的各种配气机构结构形式具有通用性，避免了过去专用试验装置的局限性。气门动态特性试验采用压电式加速度传感器和电涡流位移传感器测量气门加速度和位移。
然后，本课题对气门动态特性试验获得的气门加速度和气门升程信号进行数据处理方法研究。在过去的研究中，配气机构动力学特性试验数据处理多借用通用的数据处理方法，如多循环平均、数字滤波等，缺乏针对性。本课题对气门动态特性试验数据处理方法研究包括：
■ 采用最小二乘拟合方法消除气门加速度测量结果中包含的传感器漂移信号，其效果优于数字滤波方法；
■ 将线性平均和指数平均方法用于气门加速度和气门升程信号处理，比较表明，气门加速度适用指数平均方法处理，气门升程信号可由线性平均方法处理；
■ 由气门加速度积分和气门升程微分得到两路气门速度信号。应用离散小波分解提取气门速度信号特征，依据传感器频响特性不同，确定气门速度信号特征的权值；应用加权平均方法将两路速度信号特征融合，离散小波重构得到融合的气门速度。结果表明，在配气机构动力学特性试验数据处理中应用多传感器
Air exchange process of I.C. engine is controled by valve train, which has great influnce on engine performance. Valve train dynamic experiment and models are widely used in valve train study. By now, there are some limitations in valve train dynamic study, such as dynamic experiment standard, data processing methods, dynamic models' computed result coherence with experimental result, and adaptability of dynamic models to revolution.
Aim of this paper is to study valve train dynamic experiment and data processing methods, compare the computed results with experimental result, analyze the adaptability of dynamic models to revolution and the coherence of results.
Firstly, valve train test system (VTTS) was built in this project, which can take both valve train dynamic experiment and reliability experiment. Valve train dynamic characteristic can be tested by this system, with engine speed, temperature, lubricating condition in control. Valve acceleration is taken by piezoelectricity acceleration sensor, and valve displacement by eddy current displacement sensor.
Secondly, data processing methods used in valve train dynamic experiment are analyzed in details. Least square fit method is used to eliminate drifting sign from piezoelectricity acceleration sensor. Data average, digital filter and wavelet metods were used to processing valve dynamic data. It shows that:
■ Least square fit method is better than digital filter used in eliminate acceleration sensor excursion.
■ Valve acceleration data suit to be exponential average, and linear average method can be used to processing valve displacement data.
■ Multi sensor data fusion method was used to get valve velocity data. Two kinds of valve velocity data were got from valve acceleration integral and displacement differential. Signal character in different time-frequency dormain is got by discreted wavelet transform. Based on the difference of frequency response of two sensors, the valve velocity signals were weighted added and reconstructed by wavelet. As the result, valve velocity without abnormal alteration is got.
针对目前配气机构动力学研究中的问题，本课题分析了现有试验和数据处理方法，将多传感器数据融合技术应用到配气机构动力学试验数据处理中，并在试验基础上，对现有配气机构动力学模型进行规划研究，分析各种动力学模型的适用范围，并提出动力学模型计算气门加速度与试验结果一致性的评价依据。
首先，本课题建立了配气机构综合试验系统，将配气机构动力学特性试验和配气机构可靠性试验在一个试验系统上完成。该系统对目前所采用的各种配气机构结构形式具有通用性，避免了过去专用试验装置的局限性。气门动态特性试验采用压电式加速度传感器和电涡流位移传感器测量气门加速度和位移。
然后，本课题对气门动态特性试验获得的气门加速度和气门升程信号进行数据处理方法研究。在过去的研究中，配气机构动力学特性试验数据处理多借用通用的数据处理方法，如多循环平均、数字滤波等，缺乏针对性。本课题对气门动态特性试验数据处理方法研究包括：
■ 采用最小二乘拟合方法消除气门加速度测量结果中包含的传感器漂移信号，其效果优于数字滤波方法；
■ 将线性平均和指数平均方法用于气门加速度和气门升程信号处理，比较表明，气门加速度适用指数平均方法处理，气门升程信号可由线性平均方法处理；
■ 由气门加速度积分和气门升程微分得到两路气门速度信号。应用离散小波分解提取气门速度信号特征，依据传感器频响特性不同，确定气门速度信号特征的权值；应用加权平均方法将两路速度信号特征融合，离散小波重构得到融合的气门速度。结果表明，在配气机构动力学特性试验数据处理中应用多传感器
Air exchange process of I.C. engine is controled by valve train, which has great influnce on engine performance. Valve train dynamic experiment and models are widely used in valve train study. By now, there are some limitations in valve train dynamic study, such as dynamic experiment standard, data processing methods, dynamic models' computed result coherence with experimental result, and adaptability of dynamic models to revolution.
Aim of this paper is to study valve train dynamic experiment and data processing methods, compare the computed results with experimental result, analyze the adaptability of dynamic models to revolution and the coherence of results.
Firstly, valve train test system (VTTS) was built in this project, which can take both valve train dynamic experiment and reliability experiment. Valve train dynamic characteristic can be tested by this system, with engine speed, temperature, lubricating condition in control. Valve acceleration is taken by piezoelectricity acceleration sensor, and valve displacement by eddy current displacement sensor.
Secondly, data processing methods used in valve train dynamic experiment are analyzed in details. Least square fit method is used to eliminate drifting sign from piezoelectricity acceleration sensor. Data average, digital filter and wavelet metods were used to processing valve dynamic data. It shows that:
■ Least square fit method is better than digital filter used in eliminate acceleration sensor excursion.
■ Valve acceleration data suit to be exponential average, and linear average method can be used to processing valve displacement data.
■ Multi sensor data fusion method was used to get valve velocity data. Two kinds of valve velocity data were got from valve acceleration integral and displacement differential. Signal character in different time-frequency dormain is got by discreted wavelet transform. Based on the difference of frequency response of two sensors, the valve velocity signals were weighted added and reconstructed by wavelet. As the result, valve velocity without abnormal alteration is got.
引文
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