Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory
Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory
复制标题
DOI：
10.1016/j.inffus.2005.07.003
复制
发表时间：
2007-10-01
影响因子：
18.6
通讯作者：
Yuan, Xiaohong
中科院分区：
文献类型：
--
作者：
Basir, Otman;Yuan, Xiaohong
发动机故障诊断是一个典型的多传感器融合问题。它涉及使用多传感器信息，如振动，声音，压力和温度，以检测和识别发动机故障。从证据理论的观点来看，从每个传感器获得的信息可以被看作是一个证据，因此，基于多传感器的发动机诊断可以被看作是一个证据融合的问题。在本文中，我们研究使用Dempster-Shafer证据理论作为一种工具，用于建模和融合与发动机质量相关的多感官证据。我们提出了一个初步的审查证据理论和解释如何多传感器发动机诊断问题可以在这个理论的背景下，在故障识别框架，质量函数和规则相结合的证据。我们介绍了两种新的方法，以提高质量函数的建模和证据相结合的有效性。此外，我们提出了一个规则，作出合理的决定，就发动机质量，并提出了一个标准来评估所提出的信息融合系统的性能。最后，我们报告了一个案例研究，以证明该系统在处理传感器之间可能出现的不精确信息线索和冲突方面的有效性。(C)2005年由Elsevier B. V.出版
Engine diagnostics is a typical multi-sensor fusion problem. It involves the use of multi-sensor information such as vibration, sound, pressure and temperature, to detect and identify engine faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, multi-sensor based engine diagnosis can be viewed as a problem of evidence fusion. In this paper we investigate the use of Dempster-Shafer evidence theory as a tool for modeling and fusing multi-sensory pieces of evidence pertinent to engine quality. We present a preliminary review of Evidence Theory and explain how the multi-sensor engine diagnosis problem can be framed in the context of this theory, in terms of faults frame of discernment, mass functions and the rule for combining pieces of evidence. We introduce two new methods for enhancing the effectiveness of mass functions in modeling and combining pieces of evidence. Furthermore, we propose a rule for making rational decisions with respect to engine quality, and present a criterion to evaluate the performance of the proposed information fusion system. Finally, we report a case study to demonstrate the efficacy of this system in dealing with imprecise information cues and conflicts that may arise among the sensors. (C) 2005 Published by Elsevier B.V.