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|Title:||Precognitive maintenance and probabilistic assessment of tool wear using particle filters||Authors:||Yan, H.-C.
|Issue Date:||2013||Citation:||Yan, H.-C.,Pang, C.K.,Zhou, J.-H. (2013). Precognitive maintenance and probabilistic assessment of tool wear using particle filters. IECON Proceedings (Industrial Electronics Conference) : 7382-7387. ScholarBank@NUS Repository. https://doi.org/10.1109/IECON.2013.6700361||Abstract:||In condition-based maintenance of a machine degradation process, both estimation and prediction of hidden states are critical. In this paper, a novel approach was presented for intelligent prognosis of a hidden state. Based on the estimation results from an SVM-based ARMAX dynamic model, an integrated methodology using a NARX model and the monotonic particle filter was proposed. The robustness and monotonicity of results were guaranteed by introducing an error equation into the state-space model and adopting a monotonic algorithm for the particle filter, respectively. Our approach was validated on an industrial high speed milling machine, and the experimental results as well as analysis utilizing several criteria defined in this paper demonstrated the feasibility of our proposed methodology. © 2013 IEEE.||Source Title:||IECON Proceedings (Industrial Electronics Conference)||URI:||http://scholarbank.nus.edu.sg/handle/10635/84109||ISBN:||9781479902248||DOI:||10.1109/IECON.2013.6700361|
|Appears in Collections:||Staff Publications|
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