Please use this identifier to cite or link to this item: https://doi.org/10.1109/TII.2012.2205583
Title: A physically segmented hidden markov model approach for continuous tool condition monitoring: Diagnostics and prognostics
Authors: Geramifard, O.
Xu, J.-X. 
Zhou, J.-H.
Li, X.
Keywords: Diagnostics
feature selection
hidden Markov model (HMM)
prognostics
tool condition monitoring (TCM)
Issue Date: 2012
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
URI: http://scholarbank.nus.edu.sg/handle/10635/54707
ISSN: 15513203
DOI: 10.1109/TII.2012.2205583
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