闭环电喷系统中的氧传感器波形分析(1)

在电喷摩托车发动机进入闭环控制工况时,ECM将根据氧传感器的电压信号来修正喷油脉宽,使混合气浓度保持在理论空燃比附近。借助发动机数据分析系统对各工况下的氧传感器波形进行分析判断,有助于快速检测出电喷系统中是否存在影响尾气排放的空燃比反馈故障。同时对于完成故障维修的车辆,通过氧传感器波形分析可以验证出维修是否有效,故障是否彻底排除。使车辆在日常使用中的尾气排放也能达到一个良好的状态。     

电动汽车制动真空助力系统真空度值可信度故障检测方法研究

针对真空度传感器电压偏移后对于电动汽车制动安全和电动真空泵工作耐久性的重大影响,本文在分析真空助力器结构和工作原理基础上,提出可以通过制动前后真空助力器里真空度变化量来判断真空度传感器电压是否存在偏移,并且从原理上推导了当真空度传感器电压发生偏移后,与传感器电压未发生偏移时对比,制动前后真空度变化量偏差与制动踏板角度无关,只与制动前初始真空度有关,最后进行了实车数据采集和建模验证,结果表明本方法可有效的检测出传感器电压偏移导致的可信度故障。     

基于局部均值分解与局部离群因子动力电池故障诊断

动力电池故障诊断是保证电动汽车正常运行的关键。提出一种基于局部均值分解和局部离群因子的动力电池故 障诊断方法,用于电池组故障识别与定位。通过局部均值分解对电压信号预处理,并根据相关系数高低重构电压信号。 进一步提取重构信号的峭度因子作为故障特征输入到局部离群因子算法中,根据局部离群因子算法自适应阈值输出故障 电池。采用实车数据验证了所提方法能有效、准确地检测出故障,具有较好的可靠性与鲁棒性。     

汽油发动机的故障监测及其原理

故障监测方法发动机电子控制单元不断地监测着传感器、执行器、相关的电路、故障指示器和蓄电池电压等等,乃至电子控制单元(ECU),并对传感器输出信号、执行器驱动信号和信号处理过程(如λ闭环控制、爆震控制、怠速转速控制和蓄电池电压控制等)中发生的各种中间信息进行可信度检测,借此确定是否存在某一种故障。重要的是,这种监测不是基于对所要求监测的物理量的直接测量,例如不直接测量污染物的排放量,而是依据间接的算法来推算排放量。这种间接的算法需要数值条件和相关量之间相互的依存性作为先决条件,以保证与所要     

汽油车前氧传感器响应性能主动诊断策略研究

本文提出一种基于频率和振幅的汽油车前氧传感器电压信号响应性能主动诊断策略。通过在特定工况下主动快速调整闭环燃油调节因子的波动幅度,以快速改变排气系统废气成分,并在合理的采样区间内计算氧传感器电压跳变频率与跳变振幅,测试氧传感器电压信号响应性能。以氧电压跳变频率对主动燃油调节过程进行监控,以氧电压跳变振幅作为故障诊断指标。实车试验验证的结果表明,所提出的诊断方法能有效诊断氧传感器从浓到稀迟滞、从稀到浓迟滞和双向迟滞等典型响应故障,具有良好的实用性。     

防抱制动系统基于模型的最佳滑移率计算方法

提出了一种基于μ-λ曲线的近似数学模型来实现最佳滑移率的递推最小二乘算法(RLS)。应用累积求和统计控制法(CUSUM)，解决了RLS算法在检测跃变路面时响应滞后的问题。通过计算机仿真，验证了算法在车辆防抱制动系统中的可行性和有效性。     

东风天龙D760常见故障诊断流程汇编(十一)

<正>（接上期）故障33燃油含水传感器或线路故障（0428）1.故障现象发动机ECM显示“燃油含水指示灯传感器电路-电压高于正常值或对高压电源短路，燃油含水电路检测到高压电”，或车辆显示“燃油含水传感器或线路故障”，且连接INSITE软件，在发动机控制系统中存有故障码0428-燃油含水传感器电路电压高于正常值（图78），则说明ECM检测到燃油含水传感器信号电压高于4.95V，且持续1s以上。     

基于模型的汽车电动助力转向系统故障诊断

为实现电动助力转向系统(EPS)的状态监测和关键故障诊断,本文中引入基于模型的结构分析法(SA)故障诊断理论,结合EPS数学模型和系统关键故障,建立系统故障数学模型;利用SA方法中的系统结构表征、DM分解和故障隔离矩阵分析其故障的可检测和可隔离性,获得EPS故障诊断系统最优传感器配置,并得到5组结构最小型超定方程集(MSO sets);设计5组MSO残差,并搭建MATLAB/Simulink故障诊断系统模型,检验5组残差的信号特征和故障状态,验证EPS故障诊断设计系统的有效性,为后续的试验验证奠定基础。     

奇偶空间法用于电动车锂离子电池传感器故障诊断

在电池1阶等效电路模型的基础上耦合电池产热与传热方程,建立了可反映电池动态响应及热特性的电热模型,在此基础上应用奇偶空间法对电池输出传感器进行故障检测,并提出了一种基于系统矩阵运算的故障隔离方法。以随机电流激励为输入信号,对电池输出传感器故障进行实例分析,结果表明,奇偶空间法所设计的残差生成器可准确检测电池输出传感器故障,其针对故障信号的响应远大于源于建模精确度导致的振荡;经改良后的残差生成器在检测输出传感器故障的基础上,可实现对故障源的准确隔离。     

故障代码分析及在汽车故障检测诊断中的应用（六）

3．9．4 P0405——EGR位置传感器电路电压过低的诊断方法 动力系统控制模块（PCM）监视EGR阀位置输入信号以确保该阀门正确响应动力系统控制模块的指令，并检测EGR阀位置传感器电路断路或短路故障。当动力系统控制模块检测到废气再循环反馈信号电压过低时，将设置故障代码P0405。P0405——EGR位置传感器电路电压过低的诊断方法如下。     

基于电压频域特征和异常系数的动力电池故障诊断方法

动力电池系统是电动汽车(EV)的关键部件和主要故障源,因而提高动力电池故障诊断的效率和准确率显得尤为重要。基于此提出一种基于快速傅里叶变换(FFT)和异常系数评估(ACE)的动力电池电压不一致性故障诊断方法。针对6辆发生故障或热失控事故的电动汽车和1辆电压一致性良好的电动汽车,基于其在新能源汽车国家监管平台的全生命周期运行数据,经过电压数据的数据清洗、数据变换等大数据预处理后,利用FFT技术时频变换,提取频域中的幅值作为故障诊断的特征参数;然后,引进基于Z分数理论的异常系数对故障程度进行定量评估,以实现故障单体的检测和定位;此外,针对存在多个故障单体的情况,基于单体异常率的计算,实现单体故障程度的判定和排序;在此基础上,详细分析电压数据长度及采样间隔、FFT采样点数对模型的影响;最后,与基于熵和Z分数的电压故障诊断方法进行比较。研究结果表明:在上述研究条件下,该诊断方法对于电压一致性良好的车辆未产生误报警,且可以有效地检测出事故车辆动力电池系统存在的电压不一致性故障;相比之下,模型平均计算准确率提高了3.25%,模型平均耗时仅为熵值模型的0.55%;验证了该方法故障单体定位更精准、数据适用性更好及计算速度更快的优点。该研究成果能有效实现动力电池电压不一致性故障诊断,具有较高的工程应用价值。     

A signal-based fault detection and classification method for heavy haul wagons

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.     

传感器故障检测与隔离算法研究

传感器数据采集和分析是桥梁健康系统对桥梁状态评估的基础。由于传感器采集的数据格式复杂、信息量大,如不能有效的对传感器故障进行自动检测和隔离,将影响评估的准确性,产生错误预警信息。本文提出一种基于几何后非线性 ICA(Geometric Post Nonlinear ICA,gp ICA)的传感器故障检测与隔离算法。该算法通过引入几何后非线性(PNL)混合模型,将非线性数据线性化,再利用快速独立元分析(FastICA)对故障进行检测。通过计算监测数据对监控统计量的贡献度,基于贡献度分析法实现对故障传感器的隔离。利用MATLAB软件进行数值模拟,实现了模拟故障传感器的检测和隔离。该算法相比传统的线性ICA故障检测具有更高的故障检测率,更适用于桥梁健康监测系统的故障检测与隔离。。     

基于Simulink的小型汽油机温度传感器故障自诊断软件开发

运用MATLAB/Simulink模型开发平台,采用飞思卡尔MC9S12XEP100单片机作为主芯片,选取典型的模拟量传感器温度传感器为研究对象,采用模块化的设计思想,开发了传感器故障诊断软件,通过特定的诊断算法以及容错策略判断传感器是否存在故障。在168F小型汽油机上,通过人为设置故障,验证了故障诊断软件的可行性。试验结果表明,该软件运行稳定可靠,诊断效果明显,对普通模拟量传感器的诊断软件开发具有指导意义。     

基于车用柴油机不平衡数据集的故障识别组合模型

基于车用柴油机的不平衡数据集,根据对应故障发生频次高与低,将模型建立对象分为样本丰富的大数据量故障与样本集不完备的小数据量故障两种。面向前者,基于XGBoost （Extreme Gradient Boosting） 分类算法构建故障识别模型,面向后者,基于模糊神经网络构建故障识别模型,然后针对两类模型进行参数调节以获得最优效果,并分别建立评估机制。模型评估结果表明,该故障识别组合模型能够较为精确、全面地识别大多数故障种类,是一种对数据量要求不高且总识别率超过80%的多适应性识别模型算法,可作为汽车维保工作中的重要工具使用。     

Parallel model based fault detection algorithm for electronic parking brake system

This paper describes a parallel model-based fault detection algorithm for an electronic parking brake (EPB) system, which consists of an electronic control unit with built-in current sensor and braking force sensor. For the EPB system to supply sufficient parking force to a vehicle, the parking force sensor is of utmost importance. If a fault occurs in this sensor, sufficient parking force may not be supplied, thereby seriously threatening the safety of the vehicle. Thus, a fault detection method is required for the parking force sensor of the EPB system to improve the safety of vehicles. For this purpose, a highly reliable fault detection method is needed to detect abnormal fault signals, which cannot be detected by the existing on-line sensor monitoring fault detection methods. This paper proposes a novel parallel model-based fault detection algorithm for the EPB system, which compares the physical sensor data with the mathematical model, the fuzzy model, and the neural network model at the same time. In order to reduce false alarms, the magnitude of thresholds and the operation counts are changed adaptively. When the proposed parallel model-based fault detection algorithm detects severe failures of the force sensor, it warns the driver in advance to prevent accidents due to the failures. The proposed algorithm is verified by hardware-in-theloop simulations in various situations.     

Fault detection and diagnosis of the electromechanical brake based on observer and parity space

In the future, the conventional hydraulic brake system in automobiles will be removed and replaced by an electrically operated brake system called brake-by-wire. The brake-by-wire units, such as the EMB (Electro-Mechanical Brake), provide better braking performance by directly controlling the brake motor and are environmentally friendly because they do not use hydraulic fluid. For implementation of the EMB systems, reliable and robust fault detection and diagnosis methods become increasingly important. In this study, a sensor fault diagnosis method is proposed with parity space and observer approaches to detect faults in the motor current sensor, speed (or position) sensor and clamping force sensor. The proposed method is verified through a closed-loop simulation using Matlab/Simulink, and the simulation result is compared with the HILS bench test results.     

Fault-tolerant control of heavy-haul trains

The fault-tolerant control (FTC) of heavy-haul trains is discussed on the basis of the speed regulation proposed in previous works. The fault modes of trains are assumed and the corresponding fault detection and isolation (FDI) are studied. The FDI of sensor faults is based on a geometric approach for residual generators. The FDI of a braking system is based on the observation of the steady-state speed. From the difference of the steady-state speeds between the fault system and the faultless system, one can get fault information. Simulation tests were conducted on the suitability of the FDIs and the redesigned speed regulators. It is shown that the proposed FTC does not explicitly worsen the performance of the speed regulator in the case of a faultless system, while it obviously improves the performance of the speed regulator in the case of a faulty system.     

EEMD和SVM在发动机故障诊断中的应用

针对发动机缸盖振动信号的非线性非平稳特征,提出一种总体平均经验模态分解(EEMD)和支持向量机相结合的信号分析及故障诊断方法,该方法利用EEMD算法以及IMF序列和原始振动信号之间的相关系数,有效放大故障诊断特征向量的差异。对原始振动信号进行EEMD分解,得到各阶特征模态函数(IMF),求各阶IMF分量对应于原始信号的相关系数并组成故障分类特征向量。分别将IMF相关系数法和IMF能量分布法得到的特征向量作为输入,建立BP神经网络和支持向量机,判断发动机工作状态和故障类型。分析表明,对IMF求相关系数的方法简便易行,能有效放大不同工况下特征向量的差异,结合支持向量机能够对既定机型的配气机构和点火系常见故障进行准确识别。