Prasanna Tamilselvan

TL;DR: A novel multi-sensor health diagnosis method using deep belief network (DBN) that is compared with four existing diagnosis techniques to demonstrate the efficacy of the proposed approach.

TL;DR: 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 DBNbased classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset.

TL;DR: This study finds that the developed classification fusion model based on multiple member classification algorithms outperformed each stand-alone method for IGBT health diagnostics by providing better diagnostic accuracy and robustness.

TL;DR: Case study results indicated that, in both problems, the developed fusion diagnostics approach outperforms any stand-alone member algorithm with better diagnostic accuracy and robustness.

TL;DR: The project completed at the Wichita State University Department of Industrial and Manufacturing Engineering focused on automating the manufacturing process from the design phase to the production phase.