Publication detail

Quantities and Sensors for Machine Tool Spindle Condition Monitoring

JANÁK, L. ŠTETINA, J. FIALA, Z. HADAŠ, Z.

Original Title

Quantities and Sensors for Machine Tool Spindle Condition Monitoring

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

machine tool diagnostics, condition based maintenance, sensor fusion, Industry 4.0, HUMS, FMEA, TCM

Released

14.12.2016

Publisher

MM Science Journal

Location

Praha

ISBN

1805-0476

Periodical

MM Science Journal

Year of study

2016

Number

December

State

CZ

Pages from

1648

Pages to

1653

Pages count

6

URL

Documents

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",
  annote="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.",
  address="MM Science Journal",
  chapter="130676",
  doi="10.17973/MMSJ.2016_12_2016204",
  howpublished="online",
  institution="MM Science Journal",
  number="December",
  volume="2016",
  year="2016",
  month="december",
  pages="1648--1653",
  publisher="MM Science Journal",
  type="journal article in Scopus"
}