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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types
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Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types

机译：基于模型的多类型未知故障车辆转向系统的健康监测

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This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle.
机译：本文提出了一种基于模型的车辆转向系统故障诊断和预后方案。使用混合键图工具将转向系统建模为具有连续动力学和离散模式的混合系统。使用增强的全局分析冗余关系（AGARR）的概念，可以考虑不同类型的多个故障，即突然故障，初期故障和间歇性故障。故障鉴别器被构造为一旦检测到故障就区分出故障的类型。此后，提出了一种故障识别方案，以估计突发性故障的大小，间歇性故障的特征以及初期故障的退化行为。故障识别是通过使用控制参数较少的新型自适应混合差分进化算法（AHDE）实现的。基于已识别的早期故障的退化行为，进行预后以预测故障组件的剩余使用寿命。该算法在CyCab电动汽车的转向系统上进行了实验验证。

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