Please use this identifier to cite or link to this item:
|Title:||A multi-modal hidden Markov model based approach for continuous health assessment in machinery systems|
|Citation:||Geramifard, O.,Xu, J.-X.,Sicong, T.,Zhou, J.-H.,Li, X. (2011). A multi-modal hidden Markov model based approach for continuous health assessment in machinery systems. IECON Proceedings (Industrial Electronics Conference) : 2294-2299. ScholarBank@NUS Repository. https://doi.org/10.1109/IECON.2011.6119667|
|Abstract:||In this paper 1, a multi-modal approach based on the single hidden Markov model (HMM) with continuous output is introduced for continuous health condition monitoring in machinery systems. Comparing with existing approaches such as single HMM-based approach, artificial neural networks (ANN) approach, auto-regressive moving average with exogenous inputs (ARMAX), the proposed approach improves the performance of health condition monitoring (HCM) by using multiple HMM models in parallel. Each model emphasizes on different regiments, and outputs of all models are integrated as the ultimate output. The integration of HMM outputs are conducted by either a parametric or a semi-nonparametric hindsight method. The proposed approach is applied to tool wear prediction of a CNC-milling machine, and results are compared with an existing HMM-based approach. © 2011 IEEE.|
|Source Title:||IECON Proceedings (Industrial Electronics Conference)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 15, 2018
checked on Sep 21, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.