Fingerprint analysis for machine tool health condition monitoring

نویسندگان

چکیده

One of the pillars smart factory concept within Industry 4.0 paradigm is capability to monitor health conditions production systems and their critical components in a continuous effective way. This could be enabled through implementation innovative diagnosis, prognosis predictive maintenance actions. A wide literature has been devoted methodologies manufacturing process tool wear. parallel research field dedicated isolate condition machine from external source noise. study presents novel solution for monitoring based on so-called “fingerprint” cycle approach. fingerprint pre-defined test no-load conditions, where axes spindle are activated sequential order. Several signals extracted controller characterize current state machine. The method suitable separate drifts, trends shifts CNC caused by change any variation related cutting factors. learning that combines Principal Component Analysis statistical allows one quickly detect degraded affecting or multiple components. real case presented highlight potentials benefits provided proposed

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ژورنال

عنوان ژورنال: IFAC-PapersOnLine

سال: 2021

ISSN: ['2405-8963', '2405-8971']

DOI: https://doi.org/10.1016/j.ifacol.2021.08.144