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Improvement of Defect Detectability in Machine Tools Using Sensor-based Condition Monitoring Applications

 
: Demilia, G.; Gaspari, A.; Hohwieler, E.; Laghmouchi, A.; Uhlmann, E.

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Volltext ()

Procedia CIRP 67 (2018), S.325-331
ISSN: 2212-8271
Conference on Intelligent Computation in Manufacturing Engineering (ICME) <11, 2017, Naples>
Englisch
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPK ()

Abstract
The aim of this paper is to present a reliable methodology for condition monitoring of components of high performance centerless grinding machines. This enables the detection and localization of the defects on the ball screw. The fault detection is realized using a self-implemented classification algorithm and other pattern recognition algorithms. The diagnosis is based on acceleration and AE measurement data performed on an axis test rig using various damaged ball screws at different operating parameters. Moreover, the structure of the pattern recognition process will be introduced, this includes the signal pre-processing, the selection of the most suitable features for this specific application using MATLAB®. Finally, the evaluation of the developed solution will be showed. The actions allowing improving the accuracy of measurement data and the effectiveness of the processing algorithms, based on MATLAB® applications, are described throughout the paper. Satisfactory classification results will be obtained and discussed.

: http://publica.fraunhofer.de/dokumente/N-520795.html