重型载货汽车气压制动系统危险状态识别
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摘要
针对重型载货汽车因气压制动系统发生管路破裂、机械故障或热衰退导致制动效能下降且不易察觉从而引发严重交通事故的问题,提出基于主成分分析降维(PCA降维)和马尔可夫模型的气压制动系统危险状态识别方法。考虑到三轴载货汽车双回路制动系统的结构复杂性以及制动过程制动踏板动作、系统压力建立和实现车辆减速具有明显的时序性特点,首先采用PCA降维的方法对系统状态进行辨识;然后运用驾驶人制动意图与制动系统响应的双层隐形马尔可夫模型对系统状态进行识别。受驾驶人习惯影响制动踏板作用瞬间辨识度低,采用混合高斯聚类法提取不同制动意图时制动保持阶段数据建立制动意图识别模型和系统响应识别模型,通过二者匹配程度判定系统状态。最后,分别依据实车试验数据对模型进行离线训练和在线辨识验证。试验结果表明:系统正常状态下,基于PCA降维和马尔可夫模型相结合的识别方法能够准确、有效地识别制动系统状态;制动管路断开压力降低状态下,PCA降维方法能够及时有效识别其危险状态。
To resolve the problem that potential factors easily to ignore for pneumatic braking system cause serious traffic accidents, including pipeline breakdown, mechanical faults, or thermal recession, a method was developed for identifying the risks of pneumatic braking systems based on principal component analysis(PCA) dimensionality reduction and a Markov model. For the dual-circuit pneumatic brake system of a three-axle heavy duty truck, in order to consider the structural complexity and obvious time-series characteristics during the braking process, brake pedal operation, establish system pressure, and achieve vehicle deceleration, the system state was first identified by characteristic value using PCA dimension reduction. Then, a double-layer hidden Markov model with driver braking intention and braking system response was used to recognize the system state. Affected by driver's habits,the recognizability of braking pedal action moment was low. In this paper, Mixture Gauss Clustering Method was used to extract the data of braking holding stage to build the braking intention recognition model and system response recognition model. The system state was represented by matching degree between the two models. Finally, braking tests were performed to train the identification of the mode and validate the accuracy of the model online. The test results show that the identification method based on PCA dimensionality reduction and Markov model can identify the status of the braking system accurately and effectively in the normal state. The PCA dimensionality reduction method can also identify the dangerous state promptly and effectively when brake line breakdown and pressure reduction occur.
To resolve the problem that potential factors easily to ignore for pneumatic braking system cause serious traffic accidents, including pipeline breakdown, mechanical faults, or thermal recession, a method was developed for identifying the risks of pneumatic braking systems based on principal component analysis(PCA) dimensionality reduction and a Markov model. For the dual-circuit pneumatic brake system of a three-axle heavy duty truck, in order to consider the structural complexity and obvious time-series characteristics during the braking process, brake pedal operation, establish system pressure, and achieve vehicle deceleration, the system state was first identified by characteristic value using PCA dimension reduction. Then, a double-layer hidden Markov model with driver braking intention and braking system response was used to recognize the system state. Affected by driver's habits,the recognizability of braking pedal action moment was low. In this paper, Mixture Gauss Clustering Method was used to extract the data of braking holding stage to build the braking intention recognition model and system response recognition model. The system state was represented by matching degree between the two models. Finally, braking tests were performed to train the identification of the mode and validate the accuracy of the model online. The test results show that the identification method based on PCA dimensionality reduction and Markov model can identify the status of the braking system accurately and effectively in the normal state. The PCA dimensionality reduction method can also identify the dangerous state promptly and effectively when brake line breakdown and pressure reduction occur.
引文
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[16] 阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真[J].上海交通大学学报,2014,38(4):524-533.YAN Wei-wu,Chang Jun-Lin,Shao Hui-Hao.Least Square SVM Regression Method Based on Sliding Time Window and Its Simulation [J].Journal of Shanghai Jiaotong University,2004,38 (4):524-523.
[2] LEE S J,LIN K F,TAI C F.The Development of a Temperature Alarm Device for Failure Prevention of the Automobile Hydraulic Brake System [J].SAE Paper 1999-01-0474.
[3] JOHN S,RICHARD F,NORRIS H,et al.Brake Performance Testing and Truck Runaway Analysis [J].SAE Paper 2003-01-3396.
[4] DELAIGUE P,ESKANDARIAN A.A Comprehensive Vehicle Braking Model for Predictions of Stopping Distances [J].Journal of Automobile Engineering,2004 (218):1409-1417.
[5] VIKAS G,VIGNESH R,SHANKAR S.Model-based Braking Control of a Heavy Commercial Road Vehicle Equipped with an Electropneumatic Brake System [J].Journal of Automobile Engineering,2017,231 (12):095440701668473.
[6] PINTO R L M,GUTIéRRE Z,JUAN C H,et al.Performance Evaluation Tribological and Thermal Friction Materials During the Braking Process [J].Matéria,2017,22 (3):1-10.
[7] 姚雪萍.载货汽车危险状态辨识及监测预警研究[D].长春:吉林大学,2014.YAO Xue-ping.Research on Identification and Monitoring and Early-warning for Truck Status [D].Changchun:Jilin University,2014.
[8] 国俭.载货汽车制动器温度监测及预警系统研究[D].长春:吉林大学,2014.GUO Jian.Research on Monitoring and Early-warning System of Truck Brake System Temperature [D].Changchun:Jilin University,2014.
[9] 孙维园.基于制动距离的载货汽车制动系统危险状态辨识研究[D].长春:吉林大学,2011.SUN Wei-yuan.Identification Research of Risk State on Tuck Braking System Based on Braking Distance [D].Changchun:Jilin University,2011.
[10] 李世武,田晶晶,沙学锋,等.基于模糊综合评价和BP神经网络的车辆危险状态辨识[J].吉林大学学报;自然科学版,2011,41(6):1609-1613.LI Shi-wu,TIAN Jing-jing,SHA Xue-feng,et al.Identification of Vehicle Risk Status Based on Fuzzy Comprehensive Evaluation and BP Neural Network [J].Journal of Jilin University:Engineering and Technology Edition.2011,41 (6):1609-1613.
[11] 游忍,李绪龙,张嘉明,等.基于双重扩展卡尔曼滤波汽车防碰撞模型研究[J].合肥工业大学学报:自然科学版,2018(6):757-760,851.YOU Ren,LI Xu-long,ZHANG Jia-ming,et al.Research on Vehicle Anti-collision Model [J].Journal of Hefei University of Technology:Natural Science,2018 (6):757-760,851.
[12] 李文亮.大型营运客车运行安全性能监测方法研究[D].北京:北京理工大学,2015.LI Wen-liang.Study on Operational Safety Performance Monitoring Method for Large Commercial Motor-vehicles of Passenger Transport [D].Beijing:Beijing Institute Technology,2015.
[13] 李乃斌.汽车鼓式制动器制动温度监测与制动效能测试研究[D].昆明:昆明理工大学,2017.LI Nai-bin.Research on Braking Temperature Monitoring and Braking Efficiency Testing of Drum Brake [D].Kunming:Kunming University of Science and Technology,2017.
[14] 赵轩,马建,汪贵平.基于制动驾驶意图辨识的纯电动客车复合制动控制策略[J].交通运输工程学报,2014,14(4):64-75.ZHAO Xuan,MA Jian,WANG Gui-ping.Composite Braking Control Strategy of Pure Electric Bus Based on Brake Driving Intention Recognition [J].Journal of Traffic & Transportation Engineering,2014,14 (4):64-75.
[15] 臧卓,林辉,杨敏华.利用PCA算法进行乔木树种高光谱数据降维与分类[J].测绘科学,2014,39(2):146-149.ZANG Zhuo,LIN Hui,YANG Min-hua.Dimensional Reduction and Classification of Hyperspectral Data for Tree Species Using PCA Algorithm [J].Science of Surveying and Mapping,2014,39 (2):146-149.
[16] 阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真[J].上海交通大学学报,2014,38(4):524-533.YAN Wei-wu,Chang Jun-Lin,Shao Hui-Hao.Least Square SVM Regression Method Based on Sliding Time Window and Its Simulation [J].Journal of Shanghai Jiaotong University,2004,38 (4):524-523.