Journal Article10.1088/0964-1726/14/1/004
Sensor validation using principal component analysis
TL;DR: In this paper, the authors present a procedure based on principal component analysis which is able to perform detection, isolation and reconstruction of a faulty sensor, which is assessed using an experimental application.
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Abstract: For a reliable on-line vibration monitoring of structures, it is necessary to have accurate sensor information. However, sensors may sometimes be faulty or may even become unavailable due to failure or maintenance activities. The problem of sensor validation is therefore a critical part of structural health monitoring. The objective of the present study is to present a procedure based on principal component analysis which is able to perform detection, isolation and reconstruction of a faulty sensor. Its efficiency is assessed using an experimental application.
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Citations
The Method of Proper Orthogonal Decomposition for Dynamical Characterization and Order Reduction of Mechanical Systems: An Overview
Gaëtan Kerschen,Gaëtan Kerschen,Gaëtan Kerschen,Jean Claude Golinval,Alexander F. Vakakis,Alexander F. Vakakis,Lawrence A. Bergman +6 more
TL;DR: In this article, a different approach is adopted, and proper orthogonal decomposition is considered, and modes extracted from the decomposition may serve two purposes, namely order reduction by projecting high-dimensional data into a lower-dimensional space and feature extraction by revealing relevant but unexpected structure hidden in the data.
968
Mechanical systems and signal processing
ScienceDirect
- 01 Jan 1987
TL;DR: Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc.
925
Energy Harvesting for Structural Health Monitoring Sensor Networks
TL;DR: Some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes are defined.
Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring
TL;DR: A novel data anomaly detection method based on a convolutional neural network (CNN) that imitates human vision and decision making is proposed, which could detect the multipattern anomalies of SHM data efficiently with high accuracy.
343
Performance assessment and validation of piezoelectric active-sensors in structural health monitoring
TL;DR: A sensor diagnostics and validation process that performs in situ monitoring of the operational status of piezoelectric active-sensors in structural health monitoring (SHM) applications is presented in this article.
285
References
LIII. On lines and planes of closest fit to systems of points in space
TL;DR: This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves.
•Book
Mechanical Vibrations: Theory and Application to Structural Dynamics
Michel Géradin,Daniel J. Rixen +1 more
- 01 Jan 1994
TL;DR: In this paper, the authors present a method for the Eigenvalue Problem with Direct Time-Integration Methods (DTIM) for estimating the approximate Eigenvalues of continuous systems.
978
Numerical methods for computing angles between linear subspaces
Ake Bjoerck,Gene H. Golub +1 more
TL;DR: Experimental results are given, which indicates that MGS gives $\theta_k$ with equal precision and fewer arithmetic operations than HT, however, HT gives principal vectors, which are orthogonal to working accuracy, which is not in general true for MGS.
Nonlinear principal component analysis—Based on principal curves and neural networks
Dong Dong,Thomas J. McAvoy +1 more
TL;DR: In this paper, a nonlinear principal component analysis (NLPCA) method which integrates the principal curve algorithm and neural networks is presented. But when applied to data sets the algorithm does not yield an NLPCA model in the sense of principal loadings.
666