Validation of Strain Gauges for Structural Health Monitoring With Bayesian Belief Networks

The application of structural health monitoring (SHM) often employs multiple sensors to monitor the state of health and usage of the structures. The fault of any sensor may lead to an inaccurate or even incorrect inference with the collected sensor data, which will accordingly create a negative impact on higher-level decisions for maintenance and services. Thus, sensor validation becomes a critical process to the performance of the whole SHM system. This paper presents the use of Bayesian belief network to validate the reading of strain gauges on an aluminum plate for loading monitoring. The Bayesian belief network is constructed with the training data. The factors investigated in this paper, which may affect the validation process, include sensor configuration, sensor redundancy, and sensor data range for the discretization. The feasibility of using a Bayesian belief network for SHM sensor validation is demonstrated with the experimental results.

[1]  N C Ramirez,et al.  DIAGNOSIS OF BREAST CANCER USING BAYESIAN NETWORKS: A CASE STUDY , 2007 .

[2]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[3]  Gaëtan Kerschen,et al.  Sensor validation using principal component analysis , 2005 .

[4]  Remco R. Bouckaert.  Voting Massive Collections of Bayesian Network Classifiers for Data Streams , 2006, Australian Conference on Artificial Intelligence.

[5]  Andrès Márquez,et al.  Fault Detection in Distributed Climate Sensor Networks Using Dynamic Bayesian Networks , 2010, 2010 IEEE Sixth International Conference on e-Science.

[6]  Pablo H. Ibargüengoytia,et al.  Constructing Virtual Sensors Using Probabilistic Reasoning , 2006, MICAI.

[7]  Walter Lang,et al.  Application of Computational Intelligence for Sensor Fault Detection and Isolation , 2007 .

[8]  B. Minsker,et al.  Bayesian Belief Networks to Integrate Monitoring Evidence of Water Distribution System Contamination , 2006 .

[9]  K. Gross,et al.  Sensor fault detection in nuclear power plants using multivariate state estimation technique and support vector machines. , 2000 .

[10]  Ole J. Mengshoel,et al.  Sensor Validation using Bayesian Networks , 2008 .

[11]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[12]  John F. MacGregor.  STATISTICAL PROCESS CONTROL OF MULTIVARIATE PROCESSES , 1994 .

[13]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[14]  Alice M. Agogino,et al.  Intelligent Sensor Validation And Fusion For Vehicle Guidance Using Probabilistic And Fuzzy Methods , 1997 .

[15]  A. Fahr,et al.  AUTOMATIC CLASSIFICATION AND CHARACTERIZATION OF HIDDEN CORROSION USING PULSED EDDY CURRENT DATA , 2004 .

[16]  Sunil Vadera,et al.  A Probabilistic Model for Sensor Validation , 1996, UAI.

[17]  Ziya Arnavut,et al.  Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization , 2007, Parallel Comput..

[18]  Ermanno G. Grinzato,et al.  Statistical analysis of IR thermographic sequences by PCA , 2004 .

[19]  Remco R. Bouckaert,et al.  Bayesian network classifiers in Weka , 2004 .

[20]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[21]  Mohd Fauzi Othman,et al.  Comparison of different classification techniques using WEKA for breast cancer , 2007 .

[22]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[23]  Sunil Vadera,et al.  Any Time Probabilistic Reasoning for Sensor Validation , 1998, UAI.

[24]  J. Chen,et al.  Detecting incipient sensor faults in flight control systems , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[25]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[26]  José Manuel Gutiérrez,et al.  Bayesian Networks for Probabilistic Weather Prediction , 2002, ECAI.