本文關(guān)鍵詞： 大型營運(yùn)客車 制動(dòng)系統(tǒng) 轉(zhuǎn)向系統(tǒng) 行駛系統(tǒng) 安全性能監(jiān)測(cè)　出處：《北京理工大學(xué)》2015年博士論文　論文類型：學(xué)位論文

【摘要】：遏制營運(yùn)車輛導(dǎo)致的群死群傷事故一直是我國道路交通安全管理的重點(diǎn)工作。營運(yùn)車輛本身因素是造成道路交通事故的主要原因之一。在車輛運(yùn)行過程中，其技術(shù)性能變差、關(guān)鍵零部件損壞失靈會(huì)造成事故的意外發(fā)生。本論文以造成群死群傷交通事故比重較大的大型營運(yùn)客車為研究對(duì)象，研究提出制動(dòng)系統(tǒng)與轉(zhuǎn)向系統(tǒng)安全性能以及行駛系統(tǒng)疲勞可靠性監(jiān)測(cè)方法，研究成果的應(yīng)用對(duì)于遏制群死群傷事故的發(fā)生具有重要意義。具體研究內(nèi)容和結(jié)論如下： （1）構(gòu)建了大型營運(yùn)客車運(yùn)行安全性能監(jiān)測(cè)方案與實(shí)車試驗(yàn)平臺(tái)。結(jié)合目前營運(yùn)客車安全性能監(jiān)測(cè)存在的關(guān)鍵問題，通過分析大型客車氣壓制動(dòng)系統(tǒng)、液壓助力轉(zhuǎn)向系統(tǒng)工作過程以及行駛系統(tǒng)疲勞可靠性的主要影響因素，研究提出了制動(dòng)系統(tǒng)與轉(zhuǎn)向系統(tǒng)運(yùn)行安全性能以及行駛系統(tǒng)疲勞可靠性監(jiān)測(cè)方案。按照監(jiān)測(cè)方案，確定了需要采集的信號(hào)和需要加裝的傳感器，搭建了基于VBOX的制動(dòng)系統(tǒng)、轉(zhuǎn)向系統(tǒng)安全性能監(jiān)測(cè)試驗(yàn)平臺(tái)（簡(jiǎn)稱VBOX試驗(yàn)平臺(tái)）和基于eDAQ的行駛系統(tǒng)疲勞可靠性監(jiān)測(cè)試驗(yàn)平臺(tái)（簡(jiǎn)稱eDAQ試驗(yàn)平臺(tái)）。VBOX試驗(yàn)平臺(tái)和eDAQ試驗(yàn)平臺(tái)能夠?qū)崿F(xiàn)監(jiān)測(cè)方案所需信號(hào)的同步采集、實(shí)時(shí)監(jiān)測(cè)與數(shù)據(jù)處理，能夠滿足本論文研究工作的需要。 （2）建立了大型客車整車動(dòng)力學(xué)仿真模型，并進(jìn)行了實(shí)車試驗(yàn)驗(yàn)證。參照福田某大型客車，利用Trucksim動(dòng)力學(xué)仿真軟件，建立了大型客車動(dòng)力學(xué)仿真模型；進(jìn)行了穩(wěn)態(tài)轉(zhuǎn)向?qū)嵻囋囼?yàn)與仿真試驗(yàn)，對(duì)比分析了車輛側(cè)向加速度、側(cè)傾角、橫擺角速度等參數(shù)的穩(wěn)態(tài)值，各項(xiàng)參數(shù)最大相對(duì)誤差在10%以內(nèi)，驗(yàn)證了所建車輛動(dòng)力學(xué)仿真模型的有效性。 （3）提出了典型制動(dòng)工況下的制動(dòng)系統(tǒng)狀態(tài)監(jiān)測(cè)方法。基于平穩(wěn)制動(dòng)、下坡恒速制動(dòng)和直線緊急制動(dòng)典型工況對(duì)客車制動(dòng)系統(tǒng)進(jìn)行綜合監(jiān)測(cè)。對(duì)平穩(wěn)制動(dòng)時(shí)的制動(dòng)器制動(dòng)力矩估計(jì)、下坡恒速制動(dòng)時(shí)的制動(dòng)效能監(jiān)測(cè)以及直線緊急制動(dòng)時(shí)充分發(fā)出的平均制動(dòng)減速度(MFDD)估算進(jìn)行了理論分析。在理論分析的基礎(chǔ)上，利用VBOX試驗(yàn)平臺(tái)進(jìn)行了平穩(wěn)制動(dòng)實(shí)車試驗(yàn)，回歸建立了制動(dòng)踏板力、制動(dòng)踏板位移與制動(dòng)器制動(dòng)力矩的關(guān)系模型。進(jìn)行了直線緊急制動(dòng)實(shí)車試驗(yàn)、平穩(wěn)制動(dòng)和下坡恒速制動(dòng)仿真實(shí)驗(yàn)與故障模擬試驗(yàn)，驗(yàn)證了理論分析的正確性。 （4）提出了基于卡爾曼濾波殘差移動(dòng)平均值的轉(zhuǎn)向系統(tǒng)狀態(tài)監(jiān)測(cè)方法。建立了三自由度大型客車模型，構(gòu)建了卡爾曼濾波狀態(tài)估計(jì)器；卡爾曼濾波狀態(tài)估計(jì)器以轉(zhuǎn)向盤轉(zhuǎn)角和車速為輸入信號(hào)，以側(cè)向加速度為測(cè)量信號(hào)，實(shí)現(xiàn)車輛橫擺角速度的實(shí)時(shí)估計(jì)；將橫擺角速度估計(jì)值與測(cè)量值對(duì)比產(chǎn)生殘差，計(jì)算殘差移動(dòng)平均值；分析轉(zhuǎn)向系統(tǒng)性能良好時(shí)殘差的移動(dòng)平均值，設(shè)定故障判定閾值，超出閾值判定系統(tǒng)出現(xiàn)故障。進(jìn)行穩(wěn)態(tài)轉(zhuǎn)向?qū)嵻囋囼?yàn)和故障模擬試驗(yàn)驗(yàn)證了該方法的有效性。 （5）提出了考慮車輛行駛速度分布的行駛系統(tǒng)疲勞可靠性監(jiān)測(cè)方法�；诿x損傷疲勞壽命預(yù)測(cè)理論和Miner線性累積損傷法則，，主要分析了車輛行駛速度對(duì)疲勞壽命的影響，提出了不同速度段的當(dāng)量系數(shù)，建立了行駛系統(tǒng)疲勞可靠性監(jiān)測(cè)模型。利用eDAQ試驗(yàn)平臺(tái)采集了大型客車軸頭載荷數(shù)據(jù)，計(jì)算了不同速度段的當(dāng)量系數(shù)。根據(jù)設(shè)計(jì)目標(biāo)可靠性、車輛行駛速度分布以及當(dāng)量系數(shù)實(shí)現(xiàn)了四條不同運(yùn)營線路車輛行駛系統(tǒng)剩余疲勞壽命的預(yù)測(cè)，驗(yàn)證了監(jiān)測(cè)模型的有效性。
[Abstract]:Qunsiqunshang accident caused containment operation of vehicles has been the focus of China's road traffic safety management. The operation of the vehicle itself factors is one of the main reasons causing traffic accidents. In the running process of the vehicle, its technical performance becomes poor, the key parts of damage failure can cause accidents accidents. In this paper, the proportion of traffic accidents Qunsiqunshang the large passenger vehicles as the research object, the research put forward the braking system and steering system and driving safety monitoring system of fatigue reliability method, the research results are important to curb the Qunsiqunshang accident. The specific research contents and conclusions are as follows:
(1) the construction of large passenger operation operation safety monitoring scheme and real vehicle test platform. Combined with the key problem in the operation of passenger safety monitoring exists, through the analysis of large passenger car air braking system, hydraulic steering system working process and main factors affecting the fatigue reliability of driving system, research and put forward the braking system and steering safety the performance of system operation and reliability monitoring scheme of system fatigue. According to the monitoring plan, identified the need for signal acquisition and sensor installation, build a braking system based on VBOX, steering test platform monitoring system safety performance (VBOX test platform) and driving system fatigue reliability monitoring test platform based on eDAQ (eDAQ test platform).VBOX test platform and eDAQ test platform can achieve synchronous acquisition monitoring scheme of the desired signal, real-time monitoring And data processing can meet the needs of the research work in this paper.
(2) a large passenger vehicle dynamics simulation model, and the real vehicle test. According to a large car Futian, using the dynamic simulation software Trucksim established a large passenger dynamics simulation model; the test and Simulation of vehicle test comparison and analysis of the vehicle steady steering, lateral acceleration, roll angle, yaw steady angular velocity values of the parameters, the parameters of the maximum relative error is less than 10%, to verify the effectiveness of the vehicle dynamics simulation model.
(3) the braking system state monitoring method of typical braking conditions. Based on the stationary brake, constant speed brake and emergency brake line typical conditions of comprehensive monitoring of bus braking system downhill. Estimation of brake torque smooth braking, the average braking constant speed braking braking performance monitoring and emergency brake line fully developed deceleration (MFDD) estimation is analyzed. On the basis of theoretical analysis, the smooth braking vehicle test using the VBOX test platform, regression to establish a brake pedal force, brake pedal displacement and the relation model of brake torque. The linear emergency braking vehicle test, braking and downhill constant speed test and braking fault simulation test, verify the correctness of the theoretical analysis.
(4) proposed steering condition monitoring method system of Calman filter residual moving average value. Based on the three degree of freedom was established a large bus model, constructed the Calman filter state estimator; Calman filter state estimator with steering wheel angle and vehicle speed as the input signal to the lateral acceleration measurement signal, the real-time estimation of vehicle yaw rate the yaw rate estimation; the value of residual contrast produced and measured values, computing the residual moving average; analysis of the steering system has good performance when the residual moving average, setting threshold exceeds a threshold value to determine fault, system failure. The steady state to verify the effectiveness of the method of real vehicle test and fault simulation test of steering.
(5) considering the vehicle driving fatigue reliability monitoring system. The method of velocity distribution of nominal damage fatigue life prediction theory and Miner linear cumulative damage rule based on the influence of vehicle speed on fatigue life, the equivalent coefficient of different speeds, established the traveling system fatigue reliability monitoring model using eDAQ. The test platform for collecting large bus axle load data, equivalent coefficient of different speeds were calculated. According to the design goals of reliability prediction, the vehicle speed distribution and equivalent coefficient to achieve four different operation line vehicle residual fatigue life of the system, to verify the effectiveness of the monitoring model.

【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：U492.8

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