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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Underwater pipelines are the channels for oil transportation in the sea. In the course of pipeline operation, leakage accidents occur from time to time for natural and man-made reasons which result in economic losses and environmental pollution. To avoid economic losses and environmental pollution, damage detection of underwater pipelines must be carried out. In this paper, based on the histogram of oriented gradient (HOG) and support vector machine (SVM), a non-contact ultrasonic imaging method is proposed to detect the shedding damage of the metal underwater pipeline external anti-corrosion layer. Firstly, the principle of acoustic scattering characteristics for detecting the metal underwater pipelines is introduced. Following this, a HOG+SVM image-extracting algorithm is used to extract the pipeline area from the underwater ultrasonic image. According to the difference of mean gray value in the horizontal direction of the pipeline project area, the shedding damage parts are identified. Subsequently, taking the metal underwater pipelines with three layers of polyethylene outer anti-corrosive coatings as the detection object, an Autonomous Surface Vehicle (ASV) for underwater pipelines defect detection is developed to verify the detection effect of the method. Finally, the underwater ultrasonic image which used to detect the metal underwater pipeline shedding damage is obtained by acoustic sensor. The results show that the shedding damage can be detected by the proposed method. With the increase of shedding damage width, the effect of pipeline defect location detection is better.

Due to good thermal conductivity and corrosion resistance, copper has become common material for transmission pipeline. It is necessary to detect the early damage of copper pipeline effectively and quickly. Laser ultrasound scanning is non-contact and non-destructive damage identification method, which can realize high-precision, non-contact detection. At the same time, with the progress of internet technology, the traditional damage testing began to use advanced technologies such as internet of things and cloud computing to promote the upgrading of the testing industry from offline to online. However, obtaining large number wavefield vibration data is time consuming. In this paper, a laser ultrasonic scanning cloud platform damage detection method for copper pipeline based on alternating learning Blind Compressive Sensing (BCS) and Adjacent Area Difference Coefficient (AADC) is presented, which can improve the real-time performance and the detection accuracy. Firstly, the damage detection method is introduced in detail. BCS is used to compress the laser scanning signal at the data acquisition terminal, and then transmitted to data processing cloud platform for reconstruction. Taking the AADC value of each measuring point as the pixel value, the copper tube damage imaging is realized. Then, the simulated detection data of copper pipeline are obtained through the finite element model, and the weighted vector of AADC are determined by genetic algorithm. Finally, experimental are used to verify the effectiveness of this method, and the experimental results are analyzed and discussed. The AADC and other distance damage imaging methods are compared. The results show that this method can compress the wavefield data to 13% of the original data, and realize the damage detection.

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