Next Article in Journal
Real-Time Electrical Bioimpedance Characterization of Neointimal Tissue for Stent Applications
Next Article in Special Issue
Crack Monitoring of Operational Wind Turbine Foundations
Previous Article in Journal
A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications
Previous Article in Special Issue
Ultra-Weak Fiber Bragg Grating Sensing Network Coated with Sensitive Material for Multi-Parameter Measurements
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1733; https://doi.org/10.3390/s17081733

Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors

1
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, 210000 Nanjing, China
2
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, 210000 Nanjing, China
*
Author to whom correspondence should be addressed.
Received: 20 June 2017 / Revised: 23 July 2017 / Accepted: 25 July 2017 / Published: 28 July 2017
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
Full-Text   |   PDF [3284 KB, uploaded 30 July 2017]   |  

Abstract

Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method, numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance. View Full-Text
Keywords: load identification; FBG sensors; Kalman filter; CKF; recursive least-squares algorithm load identification; FBG sensors; Kalman filter; CKF; recursive least-squares algorithm
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Song, X.; Zhang, Y.; Liang, D. Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors. Sensors 2017, 17, 1733.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top