Failure diagnosis using deep belief learning based health state classification

Prasanna Tamilselvan, Pingfeng Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). 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 DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach.

Original languageEnglish (US)
Pages (from-to)124-135
Number of pages12
JournalReliability Engineering and System Safety
Volume115
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Artificial intelligence in diagnosis Classification
  • Deep belief networks
  • Fault diagnosis

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering

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