...
首页> 外文期刊>Engineering Applications of Artificial Intelligence >Signed Bond Graph for multiple faults diagnosis
【24h】

Signed Bond Graph for multiple faults diagnosis

机译:带符号的键图用于多故障诊断

获取原文
获取原文并翻译 | 示例
           

摘要

Different approaches have been developed to perform diagnosis and supervision on continuous systems. On the one hand, Consistency-Based Diagnosis (CBD) as a qualitative approach has proved its convenience to diagnose multiple faults. However, it faces some problems regarding robustness in decision step and difficulties to obtain an accurate qualitative model. On the other hand, the quantitative approaches based Fault Detection and Isolation (FDI) enable to generate a set of fault indicators called residuals in order to carry out on-line diagnosis. The performances of such methods depend mainly on the behavioural model accuracy and their implementation is sometimes difficult to realise, especially when the possibility of multiple faults is taken into account. To overcome the drawbacks of such methods and to fully exploit their strengths, we give a formal description of a graphical model called Signed Bond Graph (SBG). This formalism exploits its qualitative and quantitative structural properties enabling the generation of multiple behaviour predictions (i.e. possible conflicts). Furthermore, since the SBG is constructed from the Bond Graph (BG) model, the use of this latter as a quantitative method for residuals generation allows to compare the results emanating from the qualitative reasoning based SBG in order to eliminate the possible conflicts which are inconsistent or not physically possible even though they sound logical from a qualitative point of view. The proposed approach is illustrated by a real application to a traction system of an intelligent and autonomous vehicle performed within the European project InTraDE. The result shows its good applicability and efficiency.
机译:已经开发出不同的方法来对连续系统进行诊断和监督。一方面,基于一致性的诊断(CBD)作为定性方法已证明其可方便地诊断多个故障。但是,它在决策步骤的鲁棒性和获得准确的定性模型方面面临一些问题。另一方面,基于故障检测和隔离(FDI)的定量方法可以生成称为残差的一组故障指标,以便进行在线诊断。这种方法的性能主要取决于行为模型的准确性,有时难以实现,尤其是考虑到多个故障的可能性时。为了克服此类方法的缺点并充分利用其优势,我们对称为签名键图(SBG)的图形模型进行了正式描述。这种形式主义利用了其定性和定量的结构特性,从而能够生成多种行为预测(即可能的冲突)。此外,由于SBG是从Bond Graph(BG)模型构建的,因此使用后者作为定量生成残差的方法,可以比较基于定性推理的SBG产生的结果,从而消除可能不一致的冲突或从物理上讲不可能,即使从定性的角度来看它们听起来合乎逻辑。通过在欧洲InTraDE项目中执行的智能自动驾驶汽车的牵引系统的实际应用来说明所提出的方法。结果表明其良好的适用性和效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号