Paper Info
Title
Multi-model diagnostics for various machining conditions: A similarity-based approach
Abstract
In this paper, a similarity-based multi-model approach based on a pre-existing physically segmented hidden Markov model with continuous output (PSHMCO) is proposed for diagnostics and tool wear monitoring. The proposed approach helps to improve the estimation accuracy in cases that the machinery system undertakes different operating (machining) conditions. The proposed multi-model approach is compared with its single model variant on a tool wear monitoring dataset with various machining conditions. The results indicate that using a similarity function to identify and apply the most similar model out of multiple models can significantly improve the prediction performance compared to blindly utilizing a single general model while adequate training data is available.
Year
DOI
Venue
2015
10.1109/IECON.2015.7392121
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society
Keywords
Field
DocType
multimodel diagnostics,similarity-based multimodel approach,physically segmented hidden Markov model with continuous output,tool wear monitoring,machinery system,operating conditions,similarity function,PSHMCO
Training set,Data modeling,Data mining,Machining,Tool wear,Probability distribution,Artificial intelligence,Engineering,Hidden Markov model,Machine learning,Multiple Models
Conference
ISSN
Citations 
PageRank 
1553-572X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Omid Geramifard1353.88
Le Tung200.34