High-dimensional phase space reconstruction with a convolutional neural network for structural health monitoring
Journal
Sensors
Journal Volume
21
Journal Issue
10
Date Issued
2021
Author(s)
Abstract
In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens’ embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Classification (of information); Convolution; Convolutional neural networks; Degrees of freedom (mechanics); Embeddings; Object oriented programming; Phase space methods; Embedding theorems; High Degree of Freedom; High dimensional phase space; Higher-dimensional; Reconstructed attractor; Shear frame structures; Statically indeterminate structure; Structural health monitoring (SHM); Structural health monitoring
Type
journal article