Design of a robust classification fusion platform for structural health diagnostics
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Tamilselvan, Prasanna; Wang, Pingfeng; Hu, Chao. 2014. Design of a robust classification fusion platform for structural health diagnostics. ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 3A: 39th Design Automation Conference Portland, Oregon, USA, August 4–7, 2013
Efficient health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of engineered systems. This paper presents a multi-attribute classification fusion approach which leverages the strengths provided by multiple membership classifiers to form a robust classification model for structural health diagnostics. Health diagnosis using the developed approach consists of three primary steps: (i) fusion formulation using a k-fold cross validation model; (ii) diagnostics with multiple multi-attribute classifiers as member algorithms; and (iii) classification fusion through a weighted majority voting with dominance system. State-of-the-art classification techniques from three broad categories (i.e., supervised learning, unsupervised learning, and statistical inference) were employed as the member algorithms. The proposed classification fusion approach is demonstrated with a bearing health diagnostics problem. Case study results indicated that the proposed approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.
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