Title: A module-based software system for spindle condition monitoring

Authors: Ruqiang Yan, Robert X. Gao, Li Zhang, Kang B. Lee

Addresses: Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA. ' Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA. ' Global Research Center, General Electric Corporation, Niskayuna, NY 12309, USA. ' Manufacturing Metrology Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA

Abstract: Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.

Keywords: modular software design; OSA-CBM architecture; spindle monitoring; condition monitoring; analytic wavelets; SSI; stochastic subspace identification; smart machining systems; self-diagnosis; online defect identification; defect localisation; human-machine interface; HMI; smart machine tools.

DOI: 10.1504/IJMMS.2009.028079

International Journal of Mechatronics and Manufacturing Systems, 2009 Vol.2 No.5/6, pp.532 - 551

Published online: 03 Sep 2009 *

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