A generic fusion platform of failure diagnostics for resilient engineering system design

No Thumbnail Available
Issue Date
Embargo End Date
Abdolsamadi, Amirmahyar
Wang, Pingfeng
Tamilselvan, Prasanna

Abdolsamadi, Amirmahyar; Wang, Pingfeng; Tamilselvan, Prasanna. 2016. A generic fusion platform of failure diagnostics for resilient engineering system design. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 2A: 41st Design Automation Conference Boston, Massachusetts, USA, August 2–5, 2015


Effective health diagnostics provides benefits such as improved safety, improved reliability, and reduced costs for the operation and maintenance of complex 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. The developed classification fusion approach conducts the health diagnostics with three primary stages: (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) are employed. as the member algorithms. The developed classification fusion approach is demonstrated with the 2008 PHM challenge problem. The developed fusion diagnostics approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.

Table of Content
Click on the DOI link to access the article (may not be free).