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|Title:||Prognostics for Tool Condition Monitoring Based on Long-Term and Short-Term Prognostic Approaches||Authors:||WU YUE||Keywords:||Tool Condition Monitoring, Prognostics, Signal Processing, HMM, VDHMM, Feature Selection||Issue Date:||15-Aug-2012||Citation:||WU YUE (2012-08-15). Prognostics for Tool Condition Monitoring Based on Long-Term and Short-Term Prognostic Approaches. ScholarBank@NUS Repository.||Abstract:||Tool condition monitoring (TCM) plays an important role in modern manufacturing system. At present, the researches in TCM focus mainly on diagnostics of different tool conditions. Such detection approaches may sometime be too late in avoiding damage or quality issues associated with the worn or broken tool. Hence, this thesis proposes a TCM prognostic system which aims to enable a future scheduling decision as well as optimal tool replacement time. The proposed TCM prognostic system consists of two parts: long term prognostics, and short term prognostics. In the long term prognostics, the remaining useful life (RUL) is prognosticated by utilizing variable duration Hidden Markov model (VDHMM). VDHMM overcomes the state duration limitation of the traditional HMM. The relation between the model structure and the tool wear process is studied to understand and address issues regarding the factors that affect the prognostic results. It is found that VDHMM with Gaussian distribution as the state duration offers effective prognosis for the machining conditions studied. Several features have been derived from the force signal captured during the machining process and identified to correlate with tool conditions. As appropriate selection of these features affects the prognostic results, a feature selection method is proposed. The method identifies and selects a sub-set of complementary and supportive features. The proposed method is compared with the feature ranking methods, which rank the features based on their relevance with tool wear progress. The result shows that features considered relevant to tool wear may worsen the prognostic results, while those considered not relevant to tool wear could improve the prognostic results, when the latter features complement and support other features. Arising from variations in material characteristics of the workpiece and tool, there might be unexpected or pre-matured tool wear occurring before the failure time expected from the long-term prognostics. Hence, a short term prognostics capturing the short term dynamics of the tool wear is proposed. This is achieved by adding a cutting force prediction part to a diagnostic system. Different cutting force prediction structures are analyzed. It is found that Sauer?s local linear model can achieve reasonable prediction accuracy and the shortest computation time.||URI:||http://scholarbank.nus.edu.sg/handle/10635/36530|
|Appears in Collections:||Ph.D Theses (Open)|
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checked on Apr 19, 2019
checked on Apr 19, 2019
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