Using Cooperative Clustering for Structural Health Monitoring


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Research Proposal

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Using Cooperative Clustering for Structural Health Monitoring

  1. 1. Using Cooperative Clustering for Structural Health Monitoring Structural health monitoring (SHM) is a process to detect damage(s) in engineering structures and to determine the damage locations and types. Typically, damages in a structure are detected by using measurements collected from sensors built for that purpose. Research in SHM is a multidisciplinary research in nature aimed towards improving structural systems performance by employing; signal processing techniques, advance clustering and classification methods and, decision fusion theories. Normally this research starts with, advanced signal processing technique used to extract frequency features from the vibration data. A transformation approach is used to generate unique set of features that can be used for clustering and classification of the status of the structure's health. Second, a clustering algorithm is used to group the extracted sets of features into homogeneous classes of similar features. Third, soft computing approach is used as the decision fusion technique to: resolve any conflict, reduce the level of uncertainty, and produce trustful decision with high level of confidence in the decision made by the clustering and classification algorithm. This research proposal focus on enhancing the cluster analysis step. Generally, using a cluster analysis for inputs can help on finding groups of homogeneous features that may deliver additional accuracy of the proposed approach. It will also help in discovering the features that have a negative impact on the accuracy of the models. Many different clustering algorithms have been proposed in the literature. To combine the strengths of various clustering algorithms, researchers have also suggested the use of Consensus Based Techniques (CBTs), where more than one actors (e.g. algorithms) work together to achieve a common goal. CBTs is not newly applied in SHM, but we propose a new ensemble clustering approach that combines a variety of graph-based software clustering techniques in order to provide measurably better modularity clustering than that produced by any single individual technique. We believe that applying this approach in SHM will be promising. Finally, this approach can be integrated into an operational software system that can perform structural health monitoring in real-time. Testing and evaluation results of the developed SHM system using proposed approach will be reported and presented by the end of research. References A. Ibrahim, D. Rayside, R. Kashef, “Cooperative Based Software Clustering on Dependency Graphs”, IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2014), Toronto, Canada (2014). Charles R. Farrar, Keith Worden, 2013, Structural Health Monitoring: A Machine Learning Perspective, ISBN: 978-1- 119-99433-6 Forestier, G., Ganc¸ arski, P., Wemmert, C., 2010. Collaborative clustering with background knowledge. Data & Knowledge Engineering 69 (2), 211–228. Jian Li , Stephen Swift , Xiaohui Liu, 2009, Multi-Optimization Consensus Clustering, Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII. Kashef, R., Kamel, M.S., 2010. Cooperative clustering. Journal of Pattern Recognition43 (6), 2315–2329. Kashef, R.F., 2008. Cooperative clustering model and its applications. Ph.D. thesis, University of Waterloo. Mitra, S., Banka, H., Pedrycz, W., 2005. Collaborative rough clustering. Pattern Recognition and Machine Intelligence, 768–773.