Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate the impact, extracting numerical signals of the structure, and the wavelet decomposition is used to extract the band energy. Meanwhile, principal component analysis (PCA) is used to reduce the dimensions of the vibration signal. Following this, the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the placement of sensors. Finally, the experimental system is established, and the Product-based Neural Network is used to recognize different impact categories. Three sets of experiments are carried out to verify the optimal results. When three sensors are applied, the average accuracy of the impact recognition is 59.14%; when the number of sensors is four, the average accuracy of impact recognition is 76.95%.

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The sensor optimization arrangement is a combinatorial optimization problem. It has great significance in structural health monitoring (SHM). The choice of optimization method is directly related to the efficiency and feasibility of optimizing the calculation. There are many methods for sensor optimization, and heuristic algorithms have been widely used in solving sensor layout because of their great advantages in solving large-scale data optimization problems. In this paper, several common heuristic algorithms using for sensor placement optimization are introduced. The advantages and disadvantages of different algorithms are analyzed. And its development trend is prospected.

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