A probabilistic hybrid sensor fusion and optimization approach for aircraft composite components

Abstract

An integrated sensor system that continuously monitors the structural integrity of an aircraft’s critical composite components can have a high payoff by reducing risks, costs, inspections, and unscheduled maintenance, while increasing safety. Hybrid sensor networks combine or fuse different types of sensors. Optimal sensor fusion tries to find the optimal number and location of different types of sensors such that their combined probability of detection is maximized. Optimal hybrid sensor networks can be more robust, more accurate, and/or cheaper than networks consisting only of homogeneous sensors. A generic sensor fusion approach that combines the probabilities of detection of heterogeneous sensors is described. A fast greedy optimization approach that provides approximate solutions is described and demonstrated. Computable lower and upper bounds of a probability of detection objective function were determined. Fiber Bragg grating sensors can be inserted in layers of composite structures to provide local damage detection, while surface-mounted piezoelectric lead zirconate titanate sensors can provide global damage detection for the host structure under consideration. The generic approach is demonstrated on such combinations of fiber Bragg grating and lead zirconate titanate sensor networks. It is demonstrated that the proposed approach can be used to answer structural health monitoring network design problems such as the following: (1) Given a number of sensors, what is the maximum probability of detection that the sensors can attain and where should they be positioned to provide the maximum probability of detection? (2) If a given probability of detection is desired, the minimum number, types, and locations of sensors that are needed to attain this probability of detection can be determined. The approach is generic, that is, it can be extended to any number or types of sensors for which probabilities of detection can be defined.

Keywords

Structural health monitoring piezoelectric lead zirconate titanate sensor fiber Bragg grating sensors hybrid sensor fusion sensor optimization global optimization probabilistic sensor fusion optimal sensor fusion

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References

Asanuma

Haga

Ohira

. (2002) Proposal of an active composite with embedded sensor. Science and Technology of Advanced Materials 3(2): 209–216.

Baker

Dutton

Kelly

(2004) Composite Materials for Aircraft Structures. 2nd ed. American Institute of Aeronautics and Astronautics, AIAA, Reston, VA 20191, 2004.

Bao

(2007) Application of information fusion and Shannon entropy in structural damage detection. In: Proceedings of health monitoring of structural and biological systems, Tribikram Kundu, San Diego, CA, 18 March, pp. 1–9. SPIE 6532.

Basir

Yuan

(2005) Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Information Fusion 8: 379–386.

Butler

Gurvich

Ghoshal

. (2011a) Effect of embedded sensors on interlaminar damage in composite structures. Journal of Intelligent Material Systems and Structures 22(16): 1857–1868.

Butler

Ghoshal

Gurvich

. (2010) Analysis of interlaminar damages in thick rotorcraft composite components by embedded sensors. In: American Helicopter Society 66th annual forum, Phoenix, AZ, 11–13 May 2010.

Butler

Gurvich

Ghoshal

. (2011b) Ultrasonic simulation of interlaminar damages in rotorcraft composite components: approach and parametric analysis. In: American Helicopter Society 67th annual forum, Virginia Beach, VA, USA, 3–5 May 2011.

Chakraborty

Kovvali

Chakraborty

. (2011) Structural damage detection with insufficient data using transfer learning techniques. Proceedings of SPIE 7981: 798147–49.

Chang

Markmiller

JFC

Ihn

. (2007) A potential link from damage diagnostics to health prognostics of composites through built-in sensors. Transaction of the ASME 129: 718–729.

10.

COCONUT Project (2012) Algorithms for solving nonlinear constrained optimization problems: the state of the art. Available at: http://www.mat.univie.ac.at/∼neum/glopt/coconut/

11.

Costiner

Winston

Gurvich

. (2011) Optimal FBG sensor placement in composite components: algorithm and sensor trade studies. In: American Helicopter Society 67th annual forum, Virginia Beach, VA, USA, 3–5 May 2011.

12.

Coverley

Staszewski

(2003) Impact damage location in composite structures using optimized sensor triangulation procedure. Smart Materials and Structures 12(5): 795–803.

13.

Dennis

Booker

Frank

. (1999) A rigorous framework for optimization of expensive functions by surrogates. Structural Optimization 17(1): 1–13.

14.

Floudas

(1995) Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications. New York: Oxford University Press.

15.

Flynn

Todd

(2010a) A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing. Mechanical Systems and Signal Processing 24(4): 891–903.

16.

Flynn

Todd

(2010b) Bayesian probabilistic structural modeling for optimal sensor placement in ultrasonic guided wave-based structural health monitoring. Proceedings of SPIE (The International Society for Optical Engineering) 7648, San Diego, CA, 07 March 2010. Available at: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=758224

17.

Garg

Zikry

Anderson

(2001) Current and potential research activities in adaptive structures: an ARO perspective. Smart Materials and Structures 10: 610–623.

18.

Ghoshal

Harrison

Sundaresan

. (2002) Damage detection on a helicopter flexbeams. Journal of Intelligent Materials Systems and Structures 12(5): 315–330.

19.

Ghoshal

Prosser

Kim

. (2010a) Development of embedded piezoelectric acoustic sensor array architecture. Microelectronics Reliability 50(6): 857–863.

20.

Ghoshal

Welsh

Gurvich

. (2010b) Smart embedded sensors in rotorcraft composite components for condition based maintenance. In: American Helicopter Society 66th annual forum, Phoenix, AZ.

21.

Horst

Pardalos

(1995) Handbook of Global Optimization. Kluwer. Academic Publishers, Boston, Dordrecht, London.

22.

Kim

Ghoshal

Chattopadhyay

. (2004) Development of embedded sensor models in composite laminates for structural health monitoring. Journal of Reinforced Plastics and Composites 23(11): 1207–1240.

23.

Kusaka

Qing

(2003) Characterization of loading effects on the performance of SMART layer embedded or surface-mounted on structures. In: Proceeding of the fourth international workshop on structural health monitoring, Stanford University, Stanford, CA, 15–17 September 2003, pp. 1539–1546.

24.

Lee

Staszewski

(2007) Sensor location studies for damage detection with lamb waves. Smart Materials and Structures 16(2): 399–408.

25.

Liu

Giurgiutiu

(2007) Finite element simulation of piezoelectric wafer active sensor for structural health monitoring with coupled-filed element. 6th International Workshop on Structural Health Monitoring, Stanford, CA, 11–13 September 2007, 6529(2): 65293R.1–65293R.13.

26.

Liu

Michaels

(2009) Feature extraction and sensor fusion for ultrasonic structural health monitoring under changing environmental conditions. IEEE Sensors Journal 9(11): 1462–1471.

27.

Meltz

Morey

Glenn

(1989) Formation of Bragg gratings in optical fibers by a transverse holographic method. Optics Letters 14: 823–825.

28.

Nemhauser

Rinnooy Kan

AGH

Todd

(1989) Optimization, North–Holland, Amsterdam, Netherlands. ELSEVIER.

29.

Padula

Kincaid

(1999) Optimization strategies for sensor and actuator placement. NASA TM report/TM-1999-209126.

30.

Park

Ryu

Kang

. (2000) Detection of buckling and crack growth in the delaminated composites using fiber optic sensor. Journal of Composite Materials 34(19): 1602–1623.

31.

Pinter

(ed.) (2010) Global Optimization: Scientific and Engineering Case Studies (Nonconvex Optimization and Its Applications). USA: Springer.

32.

Schulz

Pai

Inman

(1999) Health monitoring and active control of composite structures using piezoceramic patches. Composites Part B: Engineering 30(7): 713–725.

33.

Sidek

Kabir

Quadri

(2011) Multi-sensor data fusion system for enhanced analysis of deterioration in concrete structures. In: Progress in electromagnetics research symposium proceedings, Suzhou, China, 12–16 September 2011.

34.

Swann

Chattopadhyay

(2006) Optimization of piezoelectric sensor location for delamination detection in composite laminates. Engineering Optimization 38(5): 511–528.

35.

Yang

B-S

Kim

(2006) Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing 20: 403–420.

36.

Zhou

Kovvali

Papandreou-Suppappola

. (2010) Sensor optimization for progressive damage diagnosis in complex structures. Proceedings of SPIE 7650, p. 76502S. DOI: 10.1117/12.848910.