---
_id: '22507'
abstract:
- lang: eng
  text: Several methods, including order analysis, wavelet analysis and empirical
    mode decomposition have been proposed and successfully employed for the health
    state estimation of technical systems operating under varying conditions. However,
    where information such as the speed of rotating machinery, component specifications
    or other domain-specific information is unavailable, such methods are often infeasible.
    Thus, this paper investigates the application of classical time-domain features,
    features from the medical field and novel features from the highly comparative
    time-series analysis (HCTSA) package, for the health state estimation of rotating
    machinery operating under varying conditions. Furthermore, several feature selection
    methods are investigated to identify features as viable health indicators for
    the diagnostics and prognostics of technical systems. As a case study, the presented
    methods are evaluated on real-world and experimentally acquired vibration data
    of bearings operating under varying speed. The results show that the selected
    features can successfully be employed as health indicators for technical systems
    operating under varying conditions.
author:
- first_name: Osarenren Kennedy
  full_name: Aimiyekagbon, Osarenren Kennedy
  id: '9557'
  last_name: Aimiyekagbon
- first_name: Amelie
  full_name: Bender, Amelie
  id: '54290'
  last_name: Bender
- first_name: Walter
  full_name: Sextro, Walter
  id: '21220'
  last_name: Sextro
citation:
  ama: 'Aimiyekagbon OK, Bender A, Sextro W. On the applicability of time series features
    as health indicators for technical systems operating under varying conditions.
    In: <i>Proceedings of the Seventeenth International Conference on Condition Monitoring
    and Asset Management (CM 2021)</i>.'
  apa: Aimiyekagbon, O. K., Bender, A., &#38; Sextro, W. (n.d.). On the applicability
    of time series features as health indicators for technical systems operating under
    varying conditions. <i>Proceedings of the Seventeenth International Conference
    on Condition Monitoring and Asset Management (CM 2021)</i>. Seventeenth International
    Conference on Condition Monitoring and Asset Management (CM 2021).
  bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro, title={On the applicability
    of time series features as health indicators for technical systems operating under
    varying conditions}, booktitle={Proceedings of the Seventeenth International Conference
    on Condition Monitoring and Asset Management (CM 2021)}, author={Aimiyekagbon,
    Osarenren Kennedy and Bender, Amelie and Sextro, Walter} }'
  chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “On
    the Applicability of Time Series Features as Health Indicators for Technical Systems
    Operating under Varying Conditions.” In <i>Proceedings of the Seventeenth International
    Conference on Condition Monitoring and Asset Management (CM 2021)</i>, n.d.
  ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “On the applicability of time
    series features as health indicators for technical systems operating under varying
    conditions,” presented at the Seventeenth International Conference on Condition
    Monitoring and Asset Management (CM 2021).
  mla: Aimiyekagbon, Osarenren Kennedy, et al. “On the Applicability of Time Series
    Features as Health Indicators for Technical Systems Operating under Varying Conditions.”
    <i>Proceedings of the Seventeenth International Conference on Condition Monitoring
    and Asset Management (CM 2021)</i>.
  short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: Proceedings of the Seventeenth
    International Conference on Condition Monitoring and Asset Management (CM 2021),
    n.d.'
conference:
  end_date: 2021-06-18
  name: Seventeenth International Conference on Condition Monitoring and Asset Management
    (CM 2021)
  start_date: 2021-06-14
date_created: 2021-06-23T05:24:39Z
date_updated: 2023-09-22T08:10:34Z
ddc:
- '620'
department:
- _id: '151'
file:
- access_level: open_access
  content_type: application/pdf
  creator: kennedy
  date_created: 2021-06-23T06:43:44Z
  date_updated: 2021-06-23T06:50:07Z
  description: 'This is a post-print version of the article presented at the Seventeenth
    International Con-ference on Condition Monitoring and Asset Management (CM 2021).
    The event websiteis available at:  https://www.bindt.org/events/CM-2021/ and the
    abstract is available at:https://www.bindt.org/events/CM-2021/abstract-9a7/.'
  file_id: '22508'
  file_name: Aimiyekagbon_et_al_2021_On_the_applicability_of_time_series_features_as_health_indicators_postPrint.pdf
  file_size: 1875572
  relation: main_file
  title: On the applicability of time series features as health indicators for technical
    systems operating under varying conditions
file_date_updated: 2021-06-23T06:50:07Z
has_accepted_license: '1'
keyword:
- Wind turbine diagnostics
- bearing diagnostics
- non-stationary operating conditions
- varying operating conditions
- feature extraction
- feature selection
- fault detection
- failure detection
language:
- iso: eng
oa: '1'
publication: Proceedings of the Seventeenth International Conference on Condition
  Monitoring and Asset Management (CM 2021)
publication_status: inpress
quality_controlled: '1'
status: public
title: On the applicability of time series features as health indicators for technical
  systems operating under varying conditions
type: conference
user_id: '9557'
year: '2021'
...