基于优化的动态多故障诊断方法

Satnam Singh, S. Ruan, Kihoon Choi, K. Pattipati, P. Willett, S. Namburu, S. Chigusa, D. Prokhorov, Liu Qiao
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引用次数: 11

摘要

由于不可靠的传感器、电磁干扰和环境条件等因素,不完美的测试结果表现为漏检和误报。我们研究车载诊断推理的主要目的是开发在存在不完美测试结果的情况下动态多故障诊断(DMFD)问题的近最优算法。我们的问题是确定最可能的故障状态演变,最好地解释观察到的测试结果。本文提出了一种将拉格朗日松弛和维特比译码算法结合迭代求解DMFD问题的原始对偶算法。我们方法的一个新颖特征是,近似对偶性间隙提供了DMFD解决方案的次优性度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimization-Based Method for Dynamic Multiple Fault Diagnosis Problem
Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. The main objective of our research on on-board diagnostic inference is to develop near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. Our problem is to determine the most likely evolution of fault states, the one that best explains the observed test outcomes. Here, we develop a primal-dual algorithm for solving the DMFD problem by combining Lagrangian relaxation and the Viterbi decoding algorithm in an iterative way. A novel feature of our approach is that the approximate duality gap provides a measure of suboptimality of the DMFD solution.
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