Active-sensing platform for structural health monitoring: Development and deployment

Abstract

Embedded sensing for structural health monitoring is a rapidly expanding field, propelled by algorithmic advances in structural health monitoring and the ever-shrinking size and cost of electronic hardware necessary for its implementation. Although commercial systems are available to perform the relevant tasks, they are usually bulky and/or expensive because of their high degree of general utility to a wider range of applications. As a result, multiple separate devices may be required in order to obtain the same results that could be obtained with a structural health monitoring–specific device. This work presents the development and deployment of a versatile, Wireless Active-Sensing Platform, designed for the particular needs of embedded sensing for multi-scale structural health monitoring. The Wireless Active-Sensing Platform combines a conventional data acquisition ability to record voltage output (e.g. from strain or acceleration transducers) with ultrasonic guided wave-based active-sensing, and a seamlessly integrated impedance measurement mode, enabling impedance-based structural health monitoring and piezoelectric sensor diagnostics to reduce the potential for false positives in damage identification. The motivation, capabilities, and hardware design for the Wireless Active-Sensing Platform are reviewed, and three deployment examples are presented, each demonstrating an important aspect of embedded sensing for structural health monitoring.

Keywords

embedded sensing sensor diagnostics multi-scale sensing active-sensing electromechanical impedance

Get full access to this article

View all access options for this article.

References

Farrar

Worden

An introduction to structural health monitoring. P Roy Soc A: Math Phy 1851; 2007(365): 303–315.

Wang

. Simulation studies of damage location in Tsing Ma Bridge deck. In: SPIE smart structures/NDE, vol. 3995, Newport Beach, CA, 6 March 2000, pp. 312–323. Bellingham, WA: SPIE.

Anton

Sodano

HA.

A review of power harvesting using piezoelectric materials (2003–2006). Smart Mater Struct 2007; 16(3): R1.

Mascarenas

Todd

Park

. Development of an impedance-based wireless sensor node for structural health monitoring. Smart Mater Struct 2007; 16(6): 2137.

Taylor

Farinholt

Park

. Multi-scale wireless sensor node for health monitoring of civil infrastructure and mechanical systems. Smart Struct Syst 2010; 6(5–6): 661–673.

Min

Park

Yun

. Development of multi-functional wireless impedance sensor nodes for structural health monitoring. Smart Struct Syst 2010; 6(5–6): 689–709.

Park

Sohn

Farrar

. Overview of piezoelectric impedance-based health monitoring and path forward. Shock Vib Digest 2003; 35(6): 451–463.

Park

Farrar

Rutherford

. Piezoelectric active sensor self-diagnostics using electrical admittance measurements. J Vib Acoust 2006; 128(4): 469–476.

Lynch

Loh

KJ.

A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib Digest 2006; 38(2): 91–128.

10.

Overly

Park

Farinholt

. Piezoelectric active-sensor diagnostics and validation using instantaneous baseline data. IEEE Sens J 2009; 9(11): 1414–1421.

11.

Kessler

Dunn

Borgen

. A cable-free digital sensor-bus for structural health monitoring of large area composite structures. In: Proceedings of the annual conference of the prognostics and health management society, San Diego, CA, September 27–October 1, 2009.

12.

Musiani

Lin

Rosing

. Active sensing platform for wireless structural health monitoring. In: Proceedings of the 6th international conference on information processing in sensor networks, Cambridge, MA, April 25–27, 2007.

13.

Nguyen

Yoon

. Multiscale acceleration-dynamic strain-impedance sensor system for structural health monitoring. Int J Distrib Sens N 2012; 2012: 709208.

14.

Flynn

Todd

Wilcox

. Maximum-likelihood estimation of damage location in guided-wave structural health monitoring. P Roy Soc A: Math Phy 2011; 467(2133): 2575–2596.

15.

Hernandez

Swartz

Zimmerman

A framework for embedded load estimation from structural response of wind turbines. In: Proulx

(ed.) Rotating machinery, structural health monitoring, shock and vibration, vol. 5. New York: Springer, 2011, pp. 295–305.

16.

Gobbato

Conte

Kosmatka

. A reliability-based framework for fatigue damage prognosis of composite aircraft structures. Probabilist Eng Mech 2012; 29: 176–188.

17.

Taylor

Farinholt

Park

. Structural health monitoring of research-scale wind turbine blades. In: Proceedings of the Asia-Pacific workshop on structural health monitoring, Melbourne, Australia, December 5–7, 2012.

18.

Berry

. Design of 9-meter carbon-fiberglass prototype blades: CX-100 and TX-100. Sandia National Laboratories Report. Report no. SAND2007-0201, September 2007. Albuquerque, NM: Sandia National Laboratories.

19.

Taylor

Farinholt

Choi

. Incipient crack detection in composite wind turbine blades. J Intel Mat Syst Str 2014; 25(5): 613–620.

20.

Dervilies

Choi

Taylor

. On damage diagnosis for a wind turbine blade using auto-associative neural networks. J Sound Vib 2014; 333(6): 1833–1850.

21.

Taylor

Park

Farinholt

. Fatigue crack detection performance comparison in a composite wind turbine rotor blade. Int J Struct Health Monit 2013; 12(3): 252–262.

22.

Michaels

Cobb

Michaels

. A comparison of feature-based classifiers for ultrasonic structural health monitoring. In: SPIE smart structures/NDE, vol. 5394, San Diego, CA, 14 March 2004, pp. 363–374. Bellingham, WA: SPIE.

23.

Croxford

Moll

Wilcox

. Efficient temperature compensation strategies for guided wave structural health monitoring. Ultrasonics 2010; 50(4–5): 517–528.

24.

Kay

SM.

Fundamentals of statistical signal processing: detection theory. Upper Saddle River, NJ: Prentice Hall PTR, 1998.

25.

Welch

The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short modified periodograms. IEEE T Acoust Speech 1967; 15(2): 70–73.

26.

Vestrand

Borozdin

Casperson

. RAPTOR: closed loop monitoring of the night sky and the earliest optical detection of GRB 021211. Astron Nachr 2004; 325(6): 549–552.

27.

Stull

Taylor

Wren

. Real-time condition assessment of RAPTOR telescope systems. J Struct Eng: ASCE 2012; 139(10): 1763–1770.

28.

Markel

Digital inverse filtering-a new tool for formant trajectory estimation. IEEE T Acoust Speech 1972; 20(2): 129–137.