ArticlePloS one2015
A bio-inspired memory model embedded with a causality reasoning function for structural fault location.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 authors at 1 institution in 1 country.
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Abstract
Structural health monitoring (SHM) is challenged by massive data storage pressure and structural fault location. In response to these issues, a bio-inspired memory model that is embedded with a causality reasoning function is proposed for fault location. First, the SHM data for processing are divided into three temporal memory areas to control data volume reasonably. Second, the inherent potential of the causal relationships in structural state monitoring is mined. Causality and dependence indices are also proposed to establish the mechanism of quantitative description of the reason and result events. Third, a mechanism of causality reasoning is developed for the reason and result events to locate faults in a SHM system. Finally, a deformation experiment conducted on a steel spring plate demonstrates that the proposed model can be applied to real-time acquisition, compact data storage, and system fault location in a SHM system. Moreover, the model is compared with some typical methods based on an experimental benchmark dataset.
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