Publication detail

Prognosis and Health Management in electric drives applications implemented in existing systems with limited data rate

KLÍMA, B. BUCHTA, L. DOSEDĚL, M. HAVRÁNEK, Z. BLAHA, P.

Original Title

Prognosis and Health Management in electric drives applications implemented in existing systems with limited data rate

English Title

Prognosis and Health Management in electric drives applications implemented in existing systems with limited data rate

Type

conference paper

Language

en

Original Abstract

Importance of the condition monitoring and predictive maintenance in motion systems is growing up as motion systems quantum and their complexity (number of axes, performance parameters) increases with increasing the automation of huge range of human activities and manufacturing processes. Probability of failures increases with the system complexity. Many faults and indication of their propagation in the electric drives would require additional sensors or hardware, higher bandwidth and sampling frequencies of feedback sensors, high computing power etc. for development of sophisticated methods to detect specific faults with good sensitivity, robustness and reliability under any operating condition. This paper presents an approach to the condition monitoring and prognosis applicable into the existing systems. These methods use the information available in the traditional electric drives – especially the information from the individual sensors in a voltage source inverter (VSI) and/or an electric motor. Condition indicators for these methods are based on application specific operating states or actions, which generates typical patterns in the signals. The condition monitoring is based on observing the deviations of these patterns between the healthy system and the system with fault propagating. The implementation strategy is described in the paper and some demonstration examples are shown as well.

English abstract

Importance of the condition monitoring and predictive maintenance in motion systems is growing up as motion systems quantum and their complexity (number of axes, performance parameters) increases with increasing the automation of huge range of human activities and manufacturing processes. Probability of failures increases with the system complexity. Many faults and indication of their propagation in the electric drives would require additional sensors or hardware, higher bandwidth and sampling frequencies of feedback sensors, high computing power etc. for development of sophisticated methods to detect specific faults with good sensitivity, robustness and reliability under any operating condition. This paper presents an approach to the condition monitoring and prognosis applicable into the existing systems. These methods use the information available in the traditional electric drives – especially the information from the individual sensors in a voltage source inverter (VSI) and/or an electric motor. Condition indicators for these methods are based on application specific operating states or actions, which generates typical patterns in the signals. The condition monitoring is based on observing the deviations of these patterns between the healthy system and the system with fault propagating. The implementation strategy is described in the paper and some demonstration examples are shown as well.

Keywords

prognosis and health management, PHM, predictive maintenance, electric drive, condition indicator

Released

10.09.2019

Publisher

IEEE

Location

Zaragoza, Spain

ISBN

978-1-7281-0303-7

Book

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Pages from

870

Pages to

876

Pages count

7

URL

Full text in the Digital Library

BibTex


@inproceedings{BUT159092,
  author="Bohumil {Klíma} and Luděk {Buchta} and Martin {Doseděl} and Zdeněk {Havránek} and Petr {Blaha}",
  title="Prognosis and Health Management in electric drives applications implemented in existing systems with limited data rate",
  annote="Importance of the condition monitoring and predictive maintenance in motion systems is growing up as motion systems quantum and their complexity (number of axes, performance parameters) increases with increasing the automation of huge range of human activities and manufacturing processes. Probability of failures increases with the system complexity. Many faults and indication of their propagation in the electric
drives would require additional sensors or hardware, higher bandwidth and sampling frequencies of feedback sensors, high computing power etc. for development of sophisticated methods to detect specific faults with good sensitivity, robustness and reliability under any operating condition. This paper presents an approach to the condition monitoring and prognosis applicable into the existing systems. These methods use the information available in the traditional electric drives – especially the information from the individual sensors in a voltage source inverter (VSI) and/or an electric motor. Condition indicators for these methods are based on application specific operating states or actions, which generates typical patterns in the signals. The condition monitoring is based on observing the deviations of these patterns between the healthy system and the system with fault propagating. The implementation strategy is described in the paper and some demonstration examples are shown as well.",
  address="IEEE",
  booktitle="2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)",
  chapter="159092",
  doi="10.1109/ETFA.2019.8869520",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2019",
  month="september",
  pages="870--876",
  publisher="IEEE",
  type="conference paper"
}