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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems
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Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems

机译：使用支持向量机用于车辆悬架系统的传感器故障检测和隔离

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摘要

In this paper, a means of generating residuals based on a fault isolation observer (FIO) and evaluating them using a support vector machine (SVM) is proposed. The proposed FIO generates the isolated residual signals and they shows robust performance regardless of unknown road surface conditions. This FIO is designed using a linear time-invariant quarter-car model. While quarter-car models have the form of a bilinear system, in this study the authors convert this bilinear model to a linear model with model uncertainty based on the assumption that the control input is limited. Therefore, the proposed FIO can be used regardless of the type of damper or controller. Furthermore, the SVM based residual evaluator without empirically set thresholds is used to evaluate the generated residuals. The proposed fault diagnosis algorithm is expected to reduce the effort required in the design procedure and it can also detect a small amount of sensor fault that cannot be detected by traditional limit-checking method. The proposed fault diagnosis algorithm is verified using low cost production accelerometers and a quarter-car test rig. Consequently, the fault diagnosis algorithm proposed in this paper can detect the faults of a sprung mass accelerometer and an unsprung mass accelerometer independently, and this algorithm can reduce the effort required in designing the diagnosis algorithm greatly.
机译：在本文中，提出了一种基于故障隔离观察者（FIO）生成残差并使用支持向量机（SVM）进行评估的方法。所提出的FIO产生隔离的残余信号，无论不知名的路面条件如何，它们都显示出稳健的性能。该FIO使用线性时间不变的四分之一车模型设计。虽然四分之一车型具有双线性系统的形式，但在本研究中，作者将该双线性模型转换为基于控制输入限制的假设的模型不确定性的线性模型。因此，无论阻尼器或控制器的类型如何，都可以使用所提出的FIO。此外，没有经验设定阈值的基于SVM的残余评估器用于评估生成的残差。建议的故障诊断算法预计将减少设计过程中所需的工作，并且它还可以检测到无法通过传统的限制检查方法检测的少量传感器故障。使用低成本生产加速度计和四分之一车试验台来验证所提出的故障诊断算法。因此，本文提出的故障诊断算法可以独立地检测簧上质量加速度计和未填充质量加速度计的故障，并且该算法可以大大降低设计诊断算法所需的努力。

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