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Sensors 2016, 16(3), 320; doi:10.3390/s16030320

Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method

State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China
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Academic Editor: Vittorio M.N. Passaro
Received: 9 December 2015 / Revised: 16 February 2016 / Accepted: 26 February 2016 / Published: 3 March 2016
(This article belongs to the Section Physical Sensors)
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Abstract

Prognostics and health management techniques have drawn widespread attention due to their ability to facilitate maintenance activities based on need. On-line prognosis of fatigue crack propagation can offer information for optimizing operation and maintenance strategies in real-time. This paper proposes a Lamb wave-particle filter (LW-PF)-based method for on-line prognosis of fatigue crack propagation which takes advantages of the possibility of on-line monitoring to evaluate the actual crack length and uses a particle filter to deal with the crack evolution and monitoring uncertainties. The piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The state space model relating to crack propagation is established by the data-driven and finite element methods. Fatigue experiments performed on hole-edge crack specimens have validated the advantages of the proposed method. View Full-Text
Keywords: prognostics and health management; fatigue crack propagation; on-line; active Lamb wave monitoring; particle filter prognostics and health management; fatigue crack propagation; on-line; active Lamb wave monitoring; particle filter
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, J.; Yuan, S.; Qiu, L.; Cai, J.; Yang, W. Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method. Sensors 2016, 16, 320.

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