Abstract
The paper presents a novel implementation of the genetic algorithm (GA) to improve the coverage of the sensor network for damage detection using guided waves. The implementation allows depiction of sensor locations with real values which is closer to the real-life situation. Also, additional features such as proximity checks and node insertions have been implemented in order to improve the convergence of the GA as well as the thoroughness of the search space. For the traditional integer-based implementation, the size of the problem is large but finite. For the real-valued implementation, the problem size can indeed be infinitely large. So added measures have been introduced such as a two-step optimization process for the reduction in size and improved convergence.
Highlights
Guided wave- (GW) based structural health monitoring (SHM) in one of the most widely used techniques for large plate or pipe-like structures
The paper outlines a two-step methodology for optimization of sensor placement for GW-based damage detection
The minimum number of sensors needed is calculated based on the quality of the signal processing algorithm
Summary
Guided wave- (GW) based structural health monitoring (SHM) in one of the most widely used techniques for large plate or pipe-like structures. The propagating wave may be used to cover a large area and through the processing of the time of flight (TOF) allows damage isolation. The first work in the area of sensor placement optimization was based on improving the probability of detection (POD). Staszewski et al [6] used it in conjunction with artificial neural networks for improving the probability of impact localization and detection. Markmiller and Chang [7] used a metric dependent on the POD which was computed based on the response reconstruction of the impact event. Haynes [9] built on the Bayes’ risk framework and included the cost of the SHM system in the decisionmaking process. Similar approaches based on the false alarms were proposed by Vanli et al [10] and Coelho et al [11]
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