Significant technological advances in sensing and communication promote the use of large sensor networks to monitor structural systems, identify damages, and quantify damage levels. Prognostics and health management (PHM) technique has been developed and applied for a variety of safety-critical engineering structures, given the critical needs of the structure health state awareness. The PHM performance highly relies on real-time sensory signals which convey the structural health relevant information. Designing an optimal structural sensor network (SN) with high detectability is thus of great importance to the PHM performance. This paper proposes a generic SN design framework using a detectability measure while accounting for uncertainties in material properties and geometric tolerances. Detectability is defined to quantify the performance of a given SN. Then, detectability analysis will be developed based on structural simulations and health state classification. Finally, the generic SN design framework can be formulated as a mixed integer nonlinear programming (MINLP) using the detectability measure and genetic algorithms (GAs) will be employed to solve the SN design optimization problem. A power transformer study will be used to demonstrate the feasibility of the proposed generic SN design methodology.
How to Cite
sensor network, detectability, prognostics and health management
Allan, D., Brundell, M., Boyd, K., and Hinde, D., (1992): New techniques for monitoring the insulation quality of in-service HV apparatus, IEEE Trans. Elect. Insulation, vol. 27, pp. 578– 585.
Azarbayejani1,M.,, El-Osery, A.I., Choi, K.K., and Taha, M.M.R., (2008): A probabilistic approach for optimal sensor allocation in structural health monitoring,” Smart Mater. Struct. 17, 055019 (11).
Bocca, M., Cosar, E.I., Salminen, J., and Eriksson, L.M., (2009): A Reconfigurable Wireless Sensor Network for Structural Health Monitoring, 4th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-4), 22-24, Zurich, Switzerland.
Buczak, A.L, Wang, H., Darabi, H., and Jafari, M.A., (2001): Genetic algorithm convergences study for sensor network optimization, Inform. Sci. 133 267– 82.
Chakrabarty, K., and Chiu, P.K., (2002): Grid coverage for surveillance and target location in distributed sensor networks, IEEE Trans. Comput. 51 1448– 53.
Chang, F-K and Markmiller, J F C, (2006): A new look in design of intelligent structures with SHM, Proc. 3rd European Workshop: Structural Health Monitoring pp 5–20.
Field, R.V.J., and Grogoriu, M., (2006): Optimal design of sensor networks for vehicle detection, classification and monitoring, Probab. Eng. Mech. 21 305–16.
Flynn, E.B., and Todd, M.D., (2010): A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing, Mechanical System and Signal Processing, In Press, Corrected Proof.
Guratzsch R. F. and Mahadevan S., (2006): Sensor placement design for SHM under uncertainty Proc. 3rd European Workshop: Structural Health Monitoring pp 1168–75.
Heredia-Zavoni, E., and Esteva, E.L., (1998): Optimal instrumentation of uncertain structural systems subject to earthquake motion, Earthq. Eng. Struct. Dyn., 27, 343–62.
Kirkgaard, P.H., and Brincker, R., (1999): On the optimal location of sensors for parameter identification of linear structural systems,” Mech. Syst. Signal Process. 8 639–47.
Leibfield, T., (1998): Online monitors keep transformers in service, IEEE Computer Appl. Power, pp. 36–42.
Li D S, Li H N and Fritzen C P, (2006): On the physical significance of the norm based sensor placement method Proc. 3rd European Workshop: Structural Health Monitoring pp 1135–43.
Li, H.-N., D.-S. Li, and G.-B. Song, (2004): Recent applications of fiber optic sensors to health monitoring in civil engineering, Engineering Structures, 26(11): p. 1647-1657.
Ling, Q., Tian, Z., Yin, Y., and Li, Y., (2009): Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks, IEEE Sensors Journal, Vol. 9, No. 11.
Ntotsios, E., Christodoulou, K., and Papadimitriou, C., (2006): Optimal sensor location methodology for structural identification and damage detection, Proc. 3rd European Workshop: Structural Health Monitoring, pp 1160–7.
Papadimitriou, C., Beck, J.L., and Au, S-K, (2000): Entropy-based optimal sensor location for structural model updating,” J. Vib. Control , (6) 781–800.
Rivera, H.L., Garcia-Souto, J.A., and Sanz, J., (2000): Measurements of mechanical vibrations at magnetic cores of power transformers with fiber- optic interferometric intrinsic sensor, IEEE Journal on Selected Topics in Quantum Electronics, Vol.6, No.5.
Tanner, Neal A., Wait, Jeannette R., Farrar, Charles R., Sohn, Hoon, (2003): Structural Health Monitoring Using Modular Wireless Sensors,” Journal of Intelligent Material Systems and Structures, 14: 43-56.
Udwadia, F.E., (1994): Methodology for optimal sensor locations for parameter identification in dynamic systems, ASCE J. Eng. Mech. 120 368–90.
Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J-C, and Harley, R.G., (2008): Particle Swarm Optimization: basic concepts, variants and applications in power systems,” IEEE Transaction on Evolutionary Computation, Vol.12, No.2.
Wei, J., Realff, J., (2004): Sample average approximation methods for stochastic MINLPs, Comp. Chem. Eng., 28, 333–346.
Zhao, X., Gao, H., Zhang, G., Ayhan, B., Yan, F. and Kwan, C., (2007): Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect detection, localization and growth monitoring, Smart Materials and Structures, 16(4): p. 1208-1217.
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