A Study of Sensor Placement Optimization Problem for Guided Wave-Based Damage Detection

Abstract

Guided waves (GW) allow fast inspection of a large area and hence have attracted research interest from the structural health monitoring (SHM) community. Thus, GW-based SHM is ideal for thin structures such as plates, pipes, etc., and is finding applications in several fields like aerospace, automotive, wind energy, etc. The GW propagate along the surface of the sample and get reflected from discontinuities in the structure in the form of boundaries and damage. Through proper signal processing of the reflected waves based on their time of arrival, the damage can be detected and isolated. For complex structures, a higher number of sensors may be required, which increases the cost of the equipment, as well as the mass. Thus, there is an effort to reduce the number of sensors without compromising the quality of the monitoring achieved. It is of utmost importance that the entire structure can be investigated. Hence, it is necessary to optimize the locations of the sensors in order to maximize the coverage while limiting the number of sensors used. A genetic algorithm (GA)-based optimization strategy was proposed by the authors for use in a simple aluminum plate. This paper extends the optimization methodology for other shape plates and presents experimental, analytical, and numerical studies. The sensitivity studies have been carried out by changing the relative weights of the application demands and presented in the form of a Pareto front. The Pareto front allows comparison of the relative importance of the different application demands, and an appropriate choice can be made based on the information provided.

A Real-Valued Genetic Algorithm for Optimization of Sensor Placement for Guided Wave-Based Structural Health Monitoring

“…This minimization can be implemented in the cost function or as a constraint in the allowed placements. If it is incorporated in the cost function as by Soman et al [17], the number of possible sensor placements increases. The optimization problem becomes very large, and as a result, the time needed for convergence is very large.…”

“…Also, the time consumed for the computation of the cost function increases with the increase in number of sensors as shown in Figure 1. The computations are based on the implementation of the GA reported in [17].…”

“…Taking into 3 Journal of Sensors consideration the proposed redundancy in the system, the maximum number of sensors is calculated as 9. This allows comparison of the method with the older method as the sensor optimization carried out previously and reported in [17] was on a network of 9 sensors.…”

“…The criterion for the optimization is given by the cost function. As has been mentioned in [17], the three demands from the application are as follows:…”

“…The number of possible sensor locations was increased by Soman et al [17] through the use of an analytical approach which is computationally more efficient than the pixel-based approach. Soman et al [17] then extended the optimization cost function also to improve the quality of the damage isolation. The damage isolation in the GW-based SHM is carried out by the triangulation technique.…”