Please use this identifier to cite or link to this item:
|Title:||Multimodal hidden markov model-based approach for tool wear monitoring|
hidden Markov model (HMM)
tool condition monitoring
|Citation:||Geramifard, O., Xu, J.-X., Zhou, J.-H., Li, X. (2014-06). Multimodal hidden markov model-based approach for tool wear monitoring. IEEE Transactions on Industrial Electronics 61 (6) : 2900-2911. ScholarBank@NUS Repository. https://doi.org/10.1109/TIE.2013.2274422|
|Abstract:||In this paper, a novel multimodal hidden Markov model (HMM)-based approach is proposed for tool wear monitoring (TWM). The proposed approach improves the performance of a pre-existing HMM-based approach named physically segmented HMM with continuous output (PSHMCO) by using multiple PSHMCOs in parallel. In this multimodal approach, each PSHMCO captures and emphasizes on a different tool wear regiment. In this paper, three weighting schemes, namely, bounded hindsight, discounted hindsight, and semi-nonparametric hindsight, are proposed, and two switching strategies named soft and hard switching are introduced to combine the outputs from multiple modes into one. As an illustrative example, the proposed approach is applied to TWM in a computer numerically controlled milling machine. The performance of the multimodal approach with various weighting schemes and switching strategies is reported and compared with PSHMCO. © 1982-2012 IEEE.|
|Source Title:||IEEE Transactions on Industrial Electronics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 22, 2018
WEB OF SCIENCETM
checked on May 15, 2018
checked on May 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.