[{"date_created":"2021-11-22T07:42:44Z","status":"public","publication":"VDI-Berichte 2391","keyword":["run-to-failure","rubber-metal element","bearing prognostics","non-stationary operating conditions","varying operating conditions","feature extraction","feature selection"],"publisher":"VDI Verlag GmbH","author":[{"id":"9557","last_name":"Aimiyekagbon","full_name":"Aimiyekagbon, Osarenren Kennedy","first_name":"Osarenren Kennedy"},{"full_name":"Bender, Amelie","first_name":"Amelie","id":"54290","last_name":"Bender"},{"full_name":"Sextro, Walter","first_name":"Walter","id":"21220","last_name":"Sextro"}],"user_id":"9557","abstract":[{"text":"Aufgrund der Fortschritte der Digitalisierung finden Systeme zur Zustandsüberwachung vermehrt Einsatz in der Industrie, um durch eine zustandsbasierte oder eine prädiktive Instandhaltung Vorteile, wie eine verbesserte Zuverlässigkeit und geringere Kosten zu erzielen. Dabei beruhen Zustandsüberwachungssysteme auf den folgenden Bausteinen: Sensorik, Datenvorverarbeitung, Merkmalsextraktion und -auswahl, Diagnose bzw. Prognose sowie einer Entscheidungsfindung basierend auf den Ergebnissen. Jeder dieser Bausteine erfordert individuelle Einstellungen, um ein geeignetes Zustandsüberwachungssystem für die jeweilige Anwendung zu entwickeln. Eine offene Fragestellung im Bereich der Zustandsüberwachung ergibt sich aufgrund der Unsicherheit der Zukunft, die sich in den zukünftigen Betriebs- und Umgebungsbedingungen zeigt. Diese Unsicherheit gilt es in allen Bausteinen zu berücksichtigen.\r\nDieser Beitrag konzentriert sich auf den Baustein Merkmalsextraktion und -selektion, mit dem Ziel anhand geeigneter Merkmale eine Prognose der nutzbaren Restlebensdauer mit hoher Genauigkeit realisieren zu können. Daher werden geeignete Merkmale aus dem Zeitbereich und daraus abgeleitete Zustandsindikatoren für die Restlebensdauerprognose von technischen Systemen vorgestellt. Dabei sind Zustandsindikatoren Kenngrößen zur Beobachtung des Zustands der kritischen Systemkomponenten. Anhand dreier Anwendungsbeispiele wird ihre Eignung evaluiert. Dabei werden Daten aus Lebensdauerversuchen unter instationären Betriebs- und Umgebungsbedingungen ausgewertet. Die auftretenden Unsicherheiten der Zukunft werden somit berücksichtigt. Die Beispielsysteme beruhen auf Gummi-Metall-Elementen und Wälzlagern. Aus den generierten Ergebnissen lässt sich schließen, dass die Zustandsindikatoren aus der betrachteten Zeitreihen-Toolbox auch unter unbekannten Betriebs- und Umgebungsbedingungen robust sind.\r\n","lang":"ger"},{"text":"Due to the advances in digitalization, condition monitoring systems have found numerous applications in the industry due to benefits such as improved reliability and lowered costs through condition-based or predictive maintenance. Condition monitoring systems typically involve elements, such as data acquisition via suitable sensors, data preprocessing, feature extraction and selection, diagnostics, prognostics and (maintenance) decisions based on diagnosis or prognosis. For the application-specific development of a suitable condition monitoring system, each of these elements requires individual settings. Due to the uncertainty of the future, an open question arises in the condition monitoring field, which is reflected in unknown future operating and environmental conditions. This uncertainty needs consideration in all elements of a condition monitoring system.\r\nThis article focuses on feature extraction and selection, building on the hypothesis that the remaining useful life of a technical system can be predicted with high accuracy utilizing suitable features. In this article, health indicators derived from time-domain features that permit the monitoring of the health of critical system components are presented for predicting the remaining useful life of technical systems. Three distinct application examples based on rubber-metal elements and rolling-element bearings are evaluated to validate the suitability of the presented methods. Experimental data from accelerated lifetime tests conducted under non-stationary operating and environmental conditions are considered to take possible future uncertainties into account. It can be concluded from the acquired results that health indicators derived from the presented time series toolbox are robust to varying operating and environmental conditions.\r\n","lang":"eng"}],"page":"197 - 210","year":"2021","type":"conference","citation":{"mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten .” VDI-Berichte 2391, VDI Verlag GmbH, 2021, pp. 197–210.","bibtex":"@inproceedings{Aimiyekagbon_Bender_Sextro_2021, place={Düsseldorf}, title={Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten }, booktitle={VDI-Berichte 2391}, publisher={VDI Verlag GmbH}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2021}, pages={197–210} }","chicago":"Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten .” In VDI-Berichte 2391, 197–210. Düsseldorf: VDI Verlag GmbH, 2021.","ama":"Aimiyekagbon OK, Bender A, Sextro W. Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten . In: VDI-Berichte 2391. VDI Verlag GmbH; 2021:197-210.","apa":"Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2021). Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten . VDI-Berichte 2391, 197–210.","ieee":"O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten ,” in VDI-Berichte 2391, Würzburg, 2021, pp. 197–210.","short":"O.K. Aimiyekagbon, A. Bender, W. Sextro, in: VDI-Berichte 2391, VDI Verlag GmbH, Düsseldorf, 2021, pp. 197–210."},"conference":{"start_date":"2021-11-16","name":"3. VDI-Fachtagung ","location":"Würzburg","end_date":"2021-11-17"},"_id":"27652","publication_status":"published","publication_identifier":{"issn":["0083-5560 "],"isbn":["978-3-18-092391-8"]},"department":[{"_id":"151"}],"title":"Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten ","place":"Düsseldorf","language":[{"iso":"ger"}],"date_updated":"2022-01-06T06:57:43Z"},{"department":[{"_id":"151"}],"publication_status":"inpress","title":"On the applicability of time series features as health indicators for technical systems operating under varying conditions","language":[{"iso":"eng"}],"date_updated":"2023-09-22T08:10:34Z","oa":"1","file":[{"access_level":"open_access","date_created":"2021-06-23T06:43:44Z","file_name":"Aimiyekagbon_et_al_2021_On_the_applicability_of_time_series_features_as_health_indicators_postPrint.pdf","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/.","relation":"main_file","content_type":"application/pdf","date_updated":"2021-06-23T06:50:07Z","title":"On the applicability of time series features as health indicators for technical systems operating under varying conditions","creator":"kennedy","file_id":"22508","file_size":1875572}],"quality_controlled":"1","author":[{"full_name":"Aimiyekagbon, Osarenren Kennedy","first_name":"Osarenren Kennedy","id":"9557","last_name":"Aimiyekagbon"},{"last_name":"Bender","id":"54290","first_name":"Amelie","full_name":"Bender, Amelie"},{"id":"21220","last_name":"Sextro","full_name":"Sextro, Walter","first_name":"Walter"}],"file_date_updated":"2021-06-23T06:50:07Z","publication":"Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)","keyword":["Wind turbine diagnostics","bearing diagnostics","non-stationary operating conditions","varying operating conditions","feature extraction","feature selection","fault detection","failure detection"],"has_accepted_license":"1","status":"public","date_created":"2021-06-23T05:24:39Z","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."}],"user_id":"9557","ddc":["620"],"type":"conference","year":"2021","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: Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021).","apa":"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. Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021). Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021).","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 Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021), n.d.","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} }","mla":"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).","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.","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)."},"_id":"22507","conference":{"end_date":"2021-06-18","name":"Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)","start_date":"2021-06-14"}},{"doi":"10.5162/SENSOREN2019/P2.9","corporate_editor":["AMA Service GmbH"],"date_updated":"2022-01-06T06:52:27Z","_id":"15488","citation":{"chicago":"Thiel, Christian, Carolin Steidl, and Bernd Henning. “P2.9 Comparison of Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial Piercing Press.” In 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019, edited by AMA Service GmbH. Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019. https://doi.org/10.5162/SENSOREN2019/P2.9.","ama":"Thiel C, Steidl C, Henning B. P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In: AMA Service GmbH, ed. 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019. Von-Münchhausen-Str. 49, 31515 Wunstorf; 2019. doi:10.5162/SENSOREN2019/P2.9","apa":"Thiel, C., Steidl, C., & Henning, B. (2019). P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019. Von-Münchhausen-Str. 49, 31515 Wunstorf. https://doi.org/10.5162/SENSOREN2019/P2.9","bibtex":"@inproceedings{Thiel_Steidl_Henning_2019, place={Von-Münchhausen-Str. 49, 31515 Wunstorf}, title={P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press}, DOI={10.5162/SENSOREN2019/P2.9}, booktitle={20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}, author={Thiel, Christian and Steidl, Carolin and Henning, Bernd}, editor={AMA Service GmbHEditor}, year={2019} }","mla":"Thiel, Christian, et al. “P2.9 Comparison of Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial Piercing Press.” 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019, edited by AMA Service GmbH, 2019, doi:10.5162/SENSOREN2019/P2.9.","short":"C. Thiel, C. Steidl, B. Henning, in: AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019, Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019.","ieee":"C. Thiel, C. Steidl, and B. Henning, “P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press,” in 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019, 2019."},"type":"conference","year":"2019","language":[{"iso":"eng"}],"title":"P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press","user_id":"11829","abstract":[{"text":"The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths.","lang":"eng"}],"place":"Von-Münchhausen-Str. 49, 31515 Wunstorf","publication_identifier":{"isbn":["978-3-9819376-0-2"]},"date_created":"2020-01-10T16:03:58Z","status":"public","keyword":["Dynamic Time Warping","Feature Extraction","Masking","Neural Networks"],"publication":"20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019","department":[{"_id":"49"}],"author":[{"first_name":"Christian","full_name":"Thiel, Christian","last_name":"Thiel"},{"last_name":"Steidl","full_name":"Steidl, Carolin","first_name":"Carolin"},{"id":"213","last_name":"Henning","full_name":"Henning, Bernd","first_name":"Bernd"}]},{"user_id":"49051","title":"FPGA-based acceleration of high density myoelectric signal processing","date_created":"2020-02-11T07:48:56Z","status":"public","publication_identifier":{"isbn":["9781467394062"]},"publication_status":"published","department":[{"_id":"78"}],"keyword":["Electromyography","Feature extraction","Delays","Hardware Pattern recognition","Prosthetics","High definition video"],"publication":"2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)","author":[{"last_name":"Boschmann","first_name":"Alexander","full_name":"Boschmann, Alexander"},{"last_name":"Agne","first_name":"Andreas","full_name":"Agne, Andreas"},{"first_name":"Linus Matthias","full_name":"Witschen, Linus Matthias","last_name":"Witschen","id":"49051"},{"last_name":"Thombansen","first_name":"Georg","full_name":"Thombansen, Georg"},{"last_name":"Kraus","first_name":"Florian","full_name":"Kraus, Florian"},{"first_name":"Marco","full_name":"Platzner, Marco","last_name":"Platzner","id":"398"}],"publisher":"IEEE","doi":"10.1109/reconfig.2015.7393312","conference":{"location":"Mexiko City, Mexiko","name":"2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)"},"_id":"15873","date_updated":"2022-01-06T06:52:38Z","language":[{"iso":"eng"}],"type":"conference","year":"2016","citation":{"short":"A. Boschmann, A. Agne, L.M. Witschen, G. Thombansen, F. Kraus, M. Platzner, in: 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), IEEE, 2016.","ieee":"A. Boschmann, A. Agne, L. M. Witschen, G. Thombansen, F. Kraus, and M. Platzner, “FPGA-based acceleration of high density myoelectric signal processing,” in 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), Mexiko City, Mexiko, 2016.","chicago":"Boschmann, Alexander, Andreas Agne, Linus Matthias Witschen, Georg Thombansen, Florian Kraus, and Marco Platzner. “FPGA-Based Acceleration of High Density Myoelectric Signal Processing.” In 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). IEEE, 2016. https://doi.org/10.1109/reconfig.2015.7393312.","apa":"Boschmann, A., Agne, A., Witschen, L. M., Thombansen, G., Kraus, F., & Platzner, M. (2016). FPGA-based acceleration of high density myoelectric signal processing. In 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). Mexiko City, Mexiko: IEEE. https://doi.org/10.1109/reconfig.2015.7393312","ama":"Boschmann A, Agne A, Witschen LM, Thombansen G, Kraus F, Platzner M. FPGA-based acceleration of high density myoelectric signal processing. In: 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). IEEE; 2016. doi:10.1109/reconfig.2015.7393312","mla":"Boschmann, Alexander, et al. “FPGA-Based Acceleration of High Density Myoelectric Signal Processing.” 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), IEEE, 2016, doi:10.1109/reconfig.2015.7393312.","bibtex":"@inproceedings{Boschmann_Agne_Witschen_Thombansen_Kraus_Platzner_2016, title={FPGA-based acceleration of high density myoelectric signal processing}, DOI={10.1109/reconfig.2015.7393312}, booktitle={2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)}, publisher={IEEE}, author={Boschmann, Alexander and Agne, Andreas and Witschen, Linus Matthias and Thombansen, Georg and Kraus, Florian and Platzner, Marco}, year={2016} }"}},{"user_id":"55222","title":"An approach for feature extraction and selection from non-trending data for machinery prognosis","abstract":[{"lang":"eng","text":"With the paradigm shift towards prognostic and health management (PHM) of machinery, there is need for reliable PHM methodologies with narrow error bounds to allow maintenance engineers take decisive maintenance actions based on the prognostic results. Prognostics is mainly concerned with the estimation of the remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods is usually a function of the features extracted from the raw data obtained from sensors. In cases where the extracted features do not display clear degradation trends, for instance highly loaded bearings, the accuracy of the state of the art PHM methods is significantly affected. The data which lacks clear degradation trend is referred to as non-trending data. This study presents a method for extracting degradation trends from non-trending condition monitoring data for RUL estimation. The raw signals are first filtered using a discrete wavelet transform (DWT) denoising filter to remove noise from the acquired signals. Time domain, frequency domain and time-frequency domain features are then extracted from the filtered signals. An autoregressive model is then applied to the extracted features to identify the degradation trends. Features representing the maximum health information are then selected based on a performance evaluation criteria using extreme learning machine (ELM) algorithm. The selected features can then be used as inputs in a prognostic algorithm. The feasibility of the method is demonstrated using experimental bearing vibration data. The performance of the method is evaluated on the accuracy of RUL estimation and the results show that the method can be used to accurately estimate RUL with a maximum error of 10\\%."}],"date_created":"2019-05-20T13:13:00Z","status":"public","volume":5,"keyword":["autoregressive model ELM feature extraction feature selection non-trending Remaining useful Life"],"publication":"Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014","department":[{"_id":"151"}],"author":[{"last_name":"Kimotho","first_name":"James Kuria","full_name":"Kimotho, James Kuria"},{"first_name":"Walter","full_name":"Sextro, Walter","last_name":"Sextro","id":"21220"}],"quality_controlled":"1","_id":"9880","intvolume":" 5","date_updated":"2019-09-16T10:37:35Z","language":[{"iso":"eng"}],"year":"2014","type":"conference","citation":{"short":"J.K. Kimotho, W. Sextro, in: Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014, 2014.","ieee":"J. K. Kimotho and W. Sextro, “An approach for feature extraction and selection from non-trending data for machinery prognosis,” in Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014, 2014, vol. 5.","chicago":"Kimotho, James Kuria, and Walter Sextro. “An Approach for Feature Extraction and Selection from Non-Trending Data for Machinery Prognosis.” In Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014, Vol. 5, 2014.","ama":"Kimotho JK, Sextro W. An approach for feature extraction and selection from non-trending data for machinery prognosis. In: Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014. Vol 5. ; 2014.","apa":"Kimotho, J. K., & Sextro, W. (2014). An approach for feature extraction and selection from non-trending data for machinery prognosis. In Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014 (Vol. 5).","bibtex":"@inproceedings{Kimotho_Sextro_2014, title={An approach for feature extraction and selection from non-trending data for machinery prognosis}, volume={5}, booktitle={Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014}, author={Kimotho, James Kuria and Sextro, Walter}, year={2014} }","mla":"Kimotho, James Kuria, and Walter Sextro. “An Approach for Feature Extraction and Selection from Non-Trending Data for Machinery Prognosis.” Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014, vol. 5, 2014."}}]