Deep belief network based state classification for structural health diagnosis

Prasanna Tamilselvan, Yibin Wang, Pingfeng Wang

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


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). 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 preprocessing the 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 diagnosis using DBN based health state classification is compared with support vector machine technique and demonstrated with aircraft wing structure health diagnostics and aircraft engine health diagnosis using 2008 PHM challenge data.

Original languageEnglish (US)
Title of host publication2012 IEEE Aerospace Conference
StatePublished - 2012
Externally publishedYes
Event2012 IEEE Aerospace Conference - Big Sky, MT, United States
Duration: Mar 3 2012Mar 10 2012

Publication series

NameIEEE Aerospace Conference Proceedings
ISSN (Print)1095-323X


Other2012 IEEE Aerospace Conference
Country/TerritoryUnited States
CityBig Sky, MT


  • Fault diagnosis
  • artificial intelligence in diagnostic classification
  • deep belief networks

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science


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