TY - GEN
T1 - Deep belief network based state classification for structural health diagnosis
AU - Tamilselvan, Prasanna
AU - Wang, Yibin
AU - Wang, Pingfeng
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Fault diagnosis
KW - artificial intelligence in diagnostic classification
KW - deep belief networks
UR - http://www.scopus.com/inward/record.url?scp=84861142975&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861142975&partnerID=8YFLogxK
U2 - 10.1109/AERO.2012.6187366
DO - 10.1109/AERO.2012.6187366
M3 - Conference contribution
AN - SCOPUS:84861142975
SN - 9781457705564
T3 - IEEE Aerospace Conference Proceedings
BT - 2012 IEEE Aerospace Conference
T2 - 2012 IEEE Aerospace Conference
Y2 - 3 March 2012 through 10 March 2012
ER -