Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICPHM.2011.6024333
Title: Continuous health condition monitoring: A single Hidden Semi-Markov Model approach
Authors: Geramifard, O.
Xu, J.-X. 
Zhou, J.-H.
Li, X.
Issue Date: 2011
Source: Geramifard, O.,Xu, J.-X.,Zhou, J.-H.,Li, X. (2011). Continuous health condition monitoring: A single Hidden Semi-Markov Model approach. 2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Conference Proceedings : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICPHM.2011.6024333
Abstract: In this paper, a single Hidden Semi-Markov Model (HSMM) approach is introduced for continuous health condition monitoring in machinery systems. Contrary to previous attempts in using hidden Markov models in this area which have not provided the relationship between the hidden state values and the physical states, this method provides the aforementioned relationship. In this paper, HSMM is applied as the core model being used in the method in order to increase flexibility of our previously used HMM-based method and consequently its generalization capability. The newly introduced method is compared with our initial HMM-based method which previously outperformed the conventional Artificial Neural Networks approach. Results show that the additional flexibility provided in the new method has improved the performance. As an example, the proposed method is used for tool wear prediction in a CNC-milling machine and results of the study is provided. 482 features are extracted from 7 signals (three force signals, three vibration signals and Acoustic Emission) acquired for each experiment of our dataset. These features include, 48 statistical features extracted from force signals in three directions (16 from each force signal) and 434 averaged wavelet coefficients from all seven signals (62 from each signal). After feature extraction phase, Fisher Discriminant Ratio is applied to find the most discriminant features to construct the prediction model. 38 features out of 482 extracted features are selected to be used in the prediction models. The prediction results are provided for three different cases i.e. cross-validation, diagnostics and prognostics. © 2011 IEEE.
Source Title: 2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/69721
ISBN: 9781424498260
DOI: 10.1109/ICPHM.2011.6024333
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