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A Shannon entropy approach for structural damage identification based on self-powered sensor data

Mousavi, M, Holloway, D ORCID: 0000-0001-9537-2744, Olivier, JC ORCID: 0000-0002-7703-6357, Alavi, AH and Gandomi, AH 2019 , 'A Shannon entropy approach for structural damage identification based on self-powered sensor data' , Engineering Structures, vol. 200 , pp. 1-11 , doi: 10.1016/j.engstruct.2019.109619.

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Abstract

Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducersand p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with aseries of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceedsone of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the appliedstrain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors forstructural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy isused to find the effective voltage thresholds that ensure the maximum detectability of the structural damagerelated changes. To this end, a baseline is constructed using the strain data obtained from the undamagedstructure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posteriorstate of the structure is monitored using thresholds set up on the baseline and a cumulative density function(CDF) of strain events. In order to determine the damage severity, a damage index is defined based on theEuclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The resultsconfirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provideadditional insights into the stress distributions.

Item Type: Article
Authors/Creators:Mousavi, M and Holloway, D and Olivier, JC and Alavi, AH and Gandomi, AH
Keywords: Shannon entropy, symbolic dynamic analysis, self-powered sensors, wireless damage detection, strain events, structural health monitoring
Journal or Publication Title: Engineering Structures
Publisher: Elsevier Sci Ltd
ISSN: 0141-0296
DOI / ID Number: 10.1016/j.engstruct.2019.109619
Copyright Information:

Copyright 2019 Elsevier Ltd.

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