Name
Title | ||
|---|---|---|
A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics. |
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TII.2012.2205583 | IEEE Trans. Industrial Informatics |
Keywords | Field | DocType |
computerised numerical control,condition monitoring,control engineering computing,hidden Markov models,mechanical engineering computing,milling machines,multilayer perceptrons,probability,recurrent neural nets,wear,Elman network,HMM,NN approach,computer numerical control milling machine,continuous tool condition monitoring,continuous tool wear prediction,health states,hidden state values,machinery systems,multilayer perceptron,neural network approach,physically segmented hidden Markov model approach,temporal probabilistic approach,Diagnostics,feature selection,hidden Markov model (HMM),prognostics,tool condition monitoring (TCM) | Prognostics,Pattern recognition,Computer science,Markov model,Multilayer perceptron,Condition monitoring,Artificial intelligence,Probabilistic logic,Estimation theory,Artificial neural network,Hidden Markov model | Journal |
Volume | Issue | ISSN |
8 | 4 | 1551-3203 |
Citations | PageRank | References |
15 | 0.83 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omid Geramifard | 1 | 35 | 3.88 |
| Jian-Xin Xu | 2 | 34 | 2.49 |
| Junhong Zhou | 3 | 146 | 27.73 |
| Xiang Li | 4 | 194 | 26.82 |