Abstract
This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks (ANN) in Structural Health Monitoring (SHM). Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise (SN) ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.
Highlights
The cornerstone of Structural Health Monitoring (SHM) in engineering design is the comparison of data measured over a pre-defined damaged structure to the same type of information obtained from the healthy (un-dam-How to cite this paper: Teimouri, H., Milani, A.S., Seethaler, R. and Heidarzadeh, A. (2016) On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process
As a step forward to address the above need, the main aim of this article is to conduct an investigation into the development of a robust SHM via a weighted Artificial Neural Network (ANN), which can be immune against potential manufacturing errors in the structure
Multilayer Perceptron (MLP) Artificial Neural Networks have proven to be powerful tools for pattern recognition in the structural health monitoring applications, yet its efficiency can reduce when dealing with unseen uncertainty in the input layers of a system such as ply thickness variation in the composite laminate
Summary
The cornerstone of Structural Health Monitoring (SHM) in engineering design is the comparison of data measured over a pre-defined damaged structure to the same type of information obtained from the healthy (un-dam-How to cite this paper: Teimouri, H., Milani, A.S., Seethaler, R. and Heidarzadeh, A. (2016) On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process. A main goal in SHM is to seek for abnormalities in the structure’s behavior and try to classify or correlate them to the location and extent of damage during the actual service of the same or a similar structure. For this purpose, the machine learning techniques have been developed and widely used by researchers and industry experts [1], by means of simulating the learning ability of humans via computer algorithms to analyze the measured input data and gain the corresponding (output) knowledge and skills. Generalization and robustness of the learning algorithms are vital to SHM system designers and require the ability of a chosen algorithm to predict the structure’s response when confronted with input data outside the nominal training set (i.e., the problem of uncertainty) [5]
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