Structural health diagnosis using deep belief network based state classification

Prasanna Tamilselvan, Pingfeng Wang

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

Abstract

Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN) based state classification. The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and collecting sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnostics using DBN based health state classification is compared with four existing classification methods and demonstrated with two case studies.

Original languageEnglish (US)
Title of host publication53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
StatePublished - Dec 1 2012
Externally publishedYes
Event53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Honolulu, HI, United States
Duration: Apr 23 2012Apr 26 2012

Publication series

NameCollection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
ISSN (Print)0273-4508

Other

Other53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
Country/TerritoryUnited States
CityHonolulu, HI
Period4/23/124/26/12

ASJC Scopus subject areas

  • Architecture
  • Materials Science(all)
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering

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