As part of the theory of belief functions, we address the problem of appraising the similarity between bodies of evidence in a relevant way using metrics. Such metrics are called evidential distances and must be computed from mathematical objects depicting the information inside bodies of evidence. Specialization matrices are such objects and, therefore, an evidential distance can be obtained by computing the norm of the difference of these matrices. Any matrix norm can be thus used to define a full metric. In this paper, we show that other matrices can be used to obtain new evidential distances. These are the α -specialization and α -generalization matrices and are closely related to the α -junctive combination rules. We prove that any L(1) norm-based distance thus defined is consistent with its corresponding α -junction. If α > 0 , these distances have in addition relevant variations induced by the poset structure of the belief function domain. Furthermore, α -junctions are meta-data dependent combination rules. The meta-data involved in α -junctions deals with the truthfulness of information sources. Consequently, the behavior of such evidential distances is analyzed in situations involving uncertain or partial meta-knowledge about information source truthfulness.
International audienceVibration-based monitoring is an approach for health analysis of helicopters. However, accelerometers and other sub-elements that convert and transmit vibrations to the recording system must not corrupt the signal. These elements are prone to defects because of external injuries during flights or maintenance. This paper will deal with a method to tackle problems of loosening and mechanical shocks. The objective is to perform a passive detection of accelerometer failures from the vibrations without knowledge of previous recordings. Experiments of mechanical failures have been carried out on a shaker to reproduce in flight vibrations, and it appears that the loosening and mechanical shocks introduce asymmetry and random peaks in the temporal vibrations. Loosening was successfully detected but mechanical shocks were much harder to detect as a result of strong dependences in the vibratory environment. Loosening data sets from flights confirm experimental observations and the proposed detection method allows for the detection of the fault with better performance than standard indicators
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