Publication detail
Quantities and Sensors for Machine Tool Spindle Condition Monitoring
JANÁK, L. ŠTETINA, J. FIALA, Z. HADAŠ, Z.
English title
Quantities and Sensors for Machine Tool Spindle Condition Monitoring
Type
journal article in Scopus
Language
en
Original abstract
The state-of-art machine tools incorporate a wide variety of sensors and associated signals that are used within the control system or as a process monitoring variables. Machine tool canalso be equipped with additional sensors required by customer or manufacturer with relatively no limitation. Therefore, the key issue is in “separating the wheat from the chaff”. Only those data that can be linked to machine tool failures, unintended customers’ behaviour, or (exceeding) machine loading, are suitable for further implementation in machine tool condition monitoring system. This paper uses the methods formerly known from system safety and reliability analysis – namely Failure Modes and Effects Analyses (FMEA) and its Diagnostics extension (FMEDA) – to identify such data and physical quantities. The outlined approach is supported by a practical case study on machine tool spindle condition monitoring. The proposed spindle monitoring is based on noise intensity and indirect cutting force measurement.
English abstract
The state-of-art machine tools incorporate a wide variety of sensors and associated signals that are used within the control system or as a process monitoring variables. Machine tool canalso be equipped with additional sensors required by customer or manufacturer with relatively no limitation. Therefore, the key issue is in “separating the wheat from the chaff”. Only those data that can be linked to machine tool failures, unintended customers’ behaviour, or (exceeding) machine loading, are suitable for further implementation in machine tool condition monitoring system. This paper uses the methods formerly known from system safety and reliability analysis – namely Failure Modes and Effects Analyses (FMEA) and its Diagnostics extension (FMEDA) – to identify such data and physical quantities. The outlined approach is supported by a practical case study on machine tool spindle condition monitoring. The proposed spindle monitoring is based on noise intensity and indirect cutting force measurement.
Keywords in English
machine tool diagnostics, condition based maintenance, sensor fusion, Industry 4.0, HUMS, FMEA, TCM
Released
14.12.2016
Publisher
MM Science Journal
Location
Praha
ISSN
1805-0476
Volume
2016
Number
December
Pages from–to
1648–1653
Pages count
6
BIBTEX
@article{BUT130676,
author="Luděk {Janák} and Jakub {Štetina} and Zdeněk {Fiala} and Zdeněk {Hadaš},
title="Quantities and Sensors for Machine Tool Spindle Condition Monitoring",
year="2016",
volume="2016",
number="December",
month="December",
pages="1648--1653",
publisher="MM Science Journal",
address="Praha",
issn="1805-0476"
}