Effective localized regression for damage detection in large complex mechanical structures

In this paper, we propose a novel data mining technique for the efficient damage detection within the large-scale complex mechanical structures. Every mechanical structure is defined by the set of finite elements that are called structure elements. Largescale complex structures may have extremely large number of structure elements, and predicting the failure in every single element using the original set of natural frequencies as features is exceptionally time-consuming task. Traditional data mining techniques simply predict failure in each structure element individually using global prediction models that are built considering all data records. In order to reduce the time complexity of these models, we propose a localized clustering-regression based approach that consists of two phases: (1) building a local cluster around a data record of interest and (2) predicting an intensity of damage only in those structure elements that correspond to data records from the built cluster. For eac...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Aleksandar Lazarevic, Ramdev Kanapady, Chandrika Kamath
• Keywords: Data Mining, Data Records, KDD 2004, Structure Element