汽车稳定性控制系统关键传感器故障诊断

设计了基于解析模型的故障诊断算法实现传感器信号的自诊断功能,首先建立关键传感器的故障诊断算法基本框架,然后利用不同的故障诊断模型分别设计了基于等价方程与基于观测器两种故障诊断算法.等价方程算法利用车辆线性单轨模型及车辆运动学方程,将车辆操纵稳定性控制需求的传感器联系起来,并用于彼此的相互诊断.观测器方法利用车辆非线性单轨模型,通过建立龙贝格观测器提取传感器故障信息.从灵敏度与误报率两方面出发,提出了将两种算法有效融合的算法改进措施,重新制定诊断规则.实车试验的结果表明融合算法能够准确快速地诊断出微小故障,并给出故障等级.

Fault Diagnosis of Vehicle Stability Control System Key Sensors

As the input of stability control system, the accuracy of sensors signals is crucial to system control and vehicle safety. The article designed a fault diagnosis algorithm based on analytic model to achieve the self-diagnosis function of sensors signals. Firstly established the fundamental frame, then two fault diagnosis algorithms were designed based on parity equations and observer. Parity equation algorithm utilized linear vehicle model and kinematic equations which show the relation between the different key sensors signals of vehicle stability control system, and let them diagnosed each other. Observer algorithm utilized nonlinear vehicle model to obtain fault information by founding Dragon Berger observer. Considering sensitivity and false warning rate, the fusion of the two algorithm was proposed to improve diagnosis effect, and the diagnosis rules were reset. Finally, the fault diagnosis algorithm was testified through several kinds of on-road tests with different operating conditions. The results showed that the algorithm can provide accurate different types fault information of different sensors, as well as the the fault degree.