Please use this identifier to cite or link to this item:
|Title:||A physically segmented hidden markov model approach for continuous tool condition monitoring: Diagnostics and prognostics|
hidden Markov model (HMM)
tool condition monitoring (TCM)
|Source:||Geramifard, O., Xu, J.-X., Zhou, J.-H., Li, X. (2012). A physically segmented hidden markov model approach for continuous tool condition monitoring: Diagnostics and prognostics. IEEE Transactions on Industrial Informatics 8 (4) : 964-973. ScholarBank@NUS Repository. https://doi.org/10.1109/TII.2012.2205583|
|Abstract:||In this paper, a temporal probabilistic approach based on the hidden Markov model (HMM), named physically segmented HMM with continuous output, is introduced for continuous tool condition monitoring in machinery systems. The proposed approach has the advantage of providing an explicit relationship between the actual health states and the hidden state values. The provided relationship is further exploited for formulation and parameter estimation in the proposed approach. The introduced approach is tested for continuous tool wear prediction in a computer numerical control milling machine and compared with two well-established neural network (NN) approaches, namely, multilayer perceptron and Elman network. In the experimental study, the prediction results are provided and compared after adopting appropriate hyper-parameter values for all the approaches by cross-validation. Based on the experimental results, physically segmented HMM approach outperforms the NN approaches. Moreover, the prognosis ability of the proposed approach is studied. © 2012 IEEE.|
|Source Title:||IEEE Transactions on Industrial Informatics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 8, 2018
WEB OF SCIENCETM
checked on Feb 6, 2018
checked on Mar 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.