Authors: Han Bing Liu, Chun Li Wu, Jing Wang
Abstract: An investigation into sensor optimal placement for bridge structure based on Single Parents Genetic Algorithm with different fitness functions has been carried on. Five fitness functions are designed from several aspects on linear independence, orthogonality and energy of mode. The two-step method is proposed to determine the number of sensors firstly and then sensor position. An example of a large span arch bridge has proved the following facts: the Single Parents Genetic Algorithm is quite suitable to sensor optimal placement for bridge structure. Fitness functions designed by effective independence index and MAC and BHM combined index are more desirable than other evaluation indices. Two-step method used to determine the number of sensors and position is very effective.
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Authors: Zhong Qi Sheng, Liang Dong, Chang Ping Tang
Abstract: This paper discusses the structure of wireless sensor network (WSN) and the key technologies for the monitoring of machine tools. Multi-sensor is used to monitor the acoustic emission and vibration signal during the manufacturing process of machine tools. Vibration signals and acoustic characteristics are extracted by using wavelet analysis. Based on the fusion of BP artificial neural networks and multi-sensor information, the monitoring of machine tool is carried out in the environment of wireless sensor network.
616
Authors: Qing Hua Li, Yi Wang, Yang Pang
Abstract: An improved gain principle component analysis(PCA) algorithm is proposed for detecting the small deviation fault of the inertial sensor data. During calculating process of the Q and statistics, different gains are set to improve the small deviation fault detecting capability of some important variables. And the filtering technology is applied to reduce the noise of the sample data and emerge the misjudgment phenomenon. Numeric example result shows that the proposed algorithm can achieve fault diagnosis effectively compared with the conventional PCA algorithm.
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Authors: Bo Ying Qin, Xian Kun Lin
Abstract: In the dynamic testing, the sensor positions have a major influence on the quality of the experimental modal parameters of a tested structure. In order to dispose sensors to reasonable degrees of freedom (DOF), and reflect adequately the dynamic characteristics of tested structure, the sensor positions must be optimized. In this paper, taking the combination of MAC matrix and Fisher information matrix (FIM) as optimization criteria, the integer-coded genetic algorithm (IGA) was applied to optimal sensor position problem (OSPP). The effect of optimization criteria and optimal method to optimal sensor positions were discussed. According to the results, the following conclusion is obtained: using MAC and FIM as optimal criteria, introducing the IGA into the OSPP, the optimal sensor positions can ensure the better linear independence of the mode shape vectors and the better estimation of the experimental modal parameters. Comparing with three existing optimal sensor placement methods, which are Guyan, effective independence (EI), and cumulative method based on QR decomposition (CQRD), their results of the optimal sensor positions indicated that the IGA is better than them.
1114
Authors: Teng Li, Xiao Mei Yuan, Shi Liang Yang, Xin Hui Zhang
Abstract: A new approach is presented for analyzing gas mixtures by transforming the problem into a pattern classification one to reduce the effect of the poor repeatability of sensor response on the prediction of gas concentration. The aim of numerical simulation is to determine how successfully the approach using the combination of artificial neural networks with multi-sensor arrays can analyze multi-component gas mixtures. The results indicate that the new approach is realistic for gas mixture analysis, and numerical simulation is a powerful tool to determine the architecture of a network. By constructing improved BP neural network algorithm and basic BP neural network into sensor array signal processing and extracting 6 component as the input of neural network, Our investigation results indicated that recognition results obtained from improved BP neural network algorithm more accuracy than the results obtained from basic BP neural network.
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