Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification
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Tamilselvan, Prasanna (2011). Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification. -- In Proceedings: 7th Annual Symposium: Graduate Research and Scholarly Projects. Wichita, KS: Wichita State University, p. 46-47
Effective health diagnosis provides multifarious benefits such as improved safety, reliability and economical maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Network (DBN) based state classification. 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 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 proposed DBN health state classification is compared with four other existing classification methods and demonstrated with a case study.
Third Place winner of oral presentations at the 7th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, May 4, 2011.
Research completed at the Department of Industrial and Manufacturing Engineering