On the applicability of time series features as health indicators for technical systems operating under varying conditions

O.K. Aimiyekagbon, A. Bender, W. Sextro, in: Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021), n.d.

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Conference Paper | In Press | English
Abstract
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.
Publishing Year
Proceedings Title
Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)
Conference
Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)
Conference Date
2021-06-14 – 2021-06-18
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Aimiyekagbon OK, Bender A, Sextro W. On the applicability of time series features as health indicators for technical systems operating under varying conditions. In: Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021).
Aimiyekagbon, O. K., Bender, A., & Sextro, W. (n.d.). On the applicability of time series features as health indicators for technical systems operating under varying conditions. In Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021).
@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} }
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 Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021), n.d.
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,” in Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021).
Aimiyekagbon, Osarenren Kennedy, et al. “On the Applicability of Time Series Features as Health Indicators for Technical Systems Operating under Varying Conditions.” Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021).
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On the applicability of time series features as health indicators for technical systems operating under varying conditions
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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/.
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2021-06-23T06:50:07Z


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