Effective localized regression for damage detection in large complex mechanical structures

Aleksandar Lazarevic, Ramdev Kanapady, Chandrika Kamath, Vipin Kumar, Kumar K Tamma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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. Large-scale 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 each test data record, we first build a cluster of data records from training data around it. Then, for each data record that belongs to discovered cluster, we identify corresponding structure elements and we build a localized regression model for each of these structure elements. These regression models for specific structure elements are constructed using only a specific set of relevant natural frequencies and merely those data records that correspond to the failure of that structure element. Experiments performed on the problem of damage prediction in a large electric transmission tower frame indicate that the proposed localized clustering-regression based approach is significantly more accurate and more computationally efficient than our previous hierarchical clustering approach, as well as global prediction models.

Original languageEnglish (US)
Title of host publicationKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsR. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh
Pages450-459
Number of pages10
StatePublished - Dec 1 2004
EventKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Seattle, WA, United States
Duration: Aug 22 2004Aug 25 2004

Publication series

NameKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySeattle, WA
Period8/22/048/25/04

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Keywords

  • Clustering
  • Damage detection
  • Localized regression
  • Mechanical structures
  • Structure elements

Cite this

Lazarevic, A., Kanapady, R., Kamath, C., Kumar, V., & Tamma, K. K. (2004). Effective localized regression for damage detection in large complex mechanical structures. In R. Kohavi, J. Gehrke, W. DuMouchel, & J. Ghosh (Eds.), KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 450-459). (KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).