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Abstract: The problem of optimal sensor placement plays a key role in the success of structural health monitoring (SHM) systems. In this study, a new method is presented to investigate the optimization problem of sensor placement on gantry crane structures. The method is a combination of an improved harmony search (HS) algorithm and the modal assurance criterion (MAC). Firstly, we review previous studies on setting reasonable values for HS parameters that have the most impact on the result, and highlight the lack of general rules governing this aspect. Based on more efficient HS algorithms resulting from those studies, we apply our proposed technique to the optimization problem of sensor placement on gantry crane structures. The purpose of the optimization method is to select the optimal sensor locations on gantry crane girders to establish a sensor network for an SHM system. Our results show that the HS algorithm is a powerful search and optimization technique that can lead to a better solution to the problem of engineering optimization. The mode of a crane structure could be identified more easily when different mode shape orientations are considered comprehensively.

This paper addresses the problem of sensor placement in SHM. Although this problem has been studied extensively in the last decades, the authors utilize a relatively new meta-heuristic method, harmony search, to tackle the problem. The paper is generally well written, with convincing analysis, particularly when choosing the parameters in the HS.

基于改进和声搜索算法识别门式起重机结构空间模态的传感器优化布置方法研究

[1]Alia, O.M., Mandava, R., 2011. The variants of the harmony search algorithm: an overview. Artificial Intelligence Review, 36(1):49-68.

[2]Allemang, R.J., Brown, D.L., 1982. Correlation coefficient for modal vector analysis. Proceedings of the 1st International Modal Analysis Conference, Orlando, USA, p.110-116.

[3]Brehm, M., Zabel, V., Bucher, C., 2013. Optimal reference sensor positions using output-only vibration test data. Mechanical Systems and Signal Processing, 41(1-2):196-225.

[4]Bruggi, M., Mariani, S., 2013. Optimization of sensor placement to detect damage in flexible plates. Engineering Optimization, 45(6):659-676.

[5]Carne, T.G., Dohrmann, C.R., 1995. A modal test design strategy for modal correlation. The 13th International Modal Analysis Conference, Nashville, USA, p.927-933.

[6]Chen, H.Y., Zhu, L.B., Huang, X., et al., 2012. Structural health monitoring system of gantry crane based on ZigBee technology. Proceedings of the 3nd International Conference on Digital Manufacturing & Automation, Guilin, China, p.801-804.

[7]Cobb, R.G., Liebst, B.S., 1997. Sensor placement and structural damage identification from minimal sensor information. AIAA Journal, 35(2):369-374.

[8]CRANESInspect, 2014. What is the CRANESInspect? Available from http://www.cranesinspect.eu/node/5 [Accessed on May 10, 2014].

[9]Ding, K.Q., Wang, Z.J., Lina, et al., 2012. Structural health monitoring system for the crane based on Bragg grating sensors. 18th World Conference on Nondestructive Testing, Durban, South Africa, p.16-20.

[10]Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., et al., 2008. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197(33-40):3080-3091.

[11]Garcia-Perez1, A., Amezquita-Sanchez, J.P., Dominguez-Gonzalez, A., et al., 2013. Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 14(9):615-630.

[12]Geem, Z.W., Kim, J.H., Loganathan, G.V., 2001. A new heuristic optimization algorithm: harmony search. Simulation, 76(2):60-68.

[13]Guyan, R.J., 1965. Reduction of stiffness and mass matrices. AIAA Journal, 3(2):380-380.

[14]Kammer, D.C., 1991. Sensor placements for on-orbit modal identification and correlation of large space structures. Journal of Guidance, Control, and Dynamics, 14(2):251-259.

[15]Kim, H.B., Park, Y.S., 1997. Sensor placement guide for structural joint stiffness model improvement. Mechanical Systems and Signal Processing, 11(5):651-672.

[16]Lee, K.S., Geem, Z.W., 2004. A new structural optimization method based on the harmony search algorithm. Computers and Structures, 82(9-10):781-798.

[17]Lee, K.S., Geem, Z.W., 2005. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194(36-38):3902-3933.

[18]Li, B.B., 2012. Information Theoretic Optimal Sensor Placement in Structure Health Monitoring. MS Thesis, Dalian University of Technology, China (in Chinese).

[19]Li, G., Qin, Q., Dong, C., 2000. Optimal placement of sensors for monitoring systems on suspension bridges by using genetic algorithms. Engineering Mechanics, 17(1):25-34 (in Chinese).

[20]Li, L., Chi, S.C., Lin, G., 2006. Improved harmonic search algorithm and its application to soil slope stability analysis. China Civil Engineering Journal, 39(5):107-111 (in Chinese).

[21]Lian, J.J., He, L.J., Ma, B., et al., 2013. Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm. Smart Materials and Structures, 22(9):095015.

[22]Ma, G., Huang, F.L., Wang, X.M., 2007. An optimal approach to the placement of sensors in structural health monitoring based on hybrid genetic algorithm. 2nd International Conference on Structural Condition Assessment, Monitoring and Improvement, Changsha, China, p.929-934.

[23]Manjarres, D., Landa-Torres, I., Gil-Lopez, S., et al., 2013. A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence, 26(8):1818-1831.

[24]Papadimitriou, C., Lombaert, G., 2012. The effect of prediction error correlation on optimal sensor placement in structural dynamics. Mechanical Systems and Signal Processing, 28:105-127.

[25]Pei, W., 2010. Research of Gantry Crane Health Monitoring Experiment System Based on Option Fiber Gratin. MS Thesis, North University of China, China (in Chinese).

[26]Salama, M., Rose, T., Garba, J., 1987. Optimal placement of excitations and sensors for verification of large dynamical systems. Proceedings of the 28th AIAA/ASME Structure, Structure Dynamics, and Materials Conference, Monterey, USA, p.1024-1031.

[27]Shi, Z.Y., Law, S.S., Zhang, L.M., 2000. Optimum sensor placement for structural damage detection. Journal of Engineering Mechanics, 126(11):1173-1179.

[28]van der Linden, G.W., Emami-Naeini, A., Kosut, R.L., et al., 2010. Near-optimal sensor placement for health monitoring of civil structures. Conference on Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security, San Diego, USA.

[29]Zhang, G.F., 2005. Optimal Sensor Localization Selection in a Diagnostic Prognostic Architecture. PhD Thesis, Georgia Institute of Technology, Atlanta, USA.