Title :
An exTS based neuro-fuzzy algorithm for prognostics and tool condition monitoring
Author :
Massol, O. ; Li, X. ; Gouriveau, R. ; Zhou, J.H. ; Gan, O.P.
Author_Institution :
Autom. Control & Micro-Mechatron. Syst. Dept., FEMTO-ST Inst., Besançon, France
Abstract :
The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving extended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed machining conditions. The performance of evolving Neuro-Fuzzy model is compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Multiple Regression Model (MRM) in term of accuracy and reliability through a case study of tool condition monitoring. The reliability of exTS also investigated.
Keywords :
condition monitoring; fuzzy reasoning; maintenance engineering; neural nets; regression analysis; tools; adaptive neurofuzzy inference system; artificial intelligence methods; evolving extended Takagi Sugeno based neurofuzzy algorithm; exTS based neurofuzzy algorithm; high-speed machining conditions; multiple regression model; predictive maintenance; prognostics; tool condition monitoring; Accuracy; Data models; Feature extraction; Force; Prediction algorithms; Predictive models; Reliability; Evolving extended Takagi Sugeno Neuro-Fuzzy algorithm; Prognostics; Tool condition monitoring; Tool wear estimation;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-7814-9
DOI :
10.1109/ICARCV.2010.5707842
