--- _id: '51518' abstract: - lang: eng text: In applications of piezoelectric actuators and sensors, the dependability and particularly the reliability throughout their lifetime are vital to manufacturers and end-users and are enabled through condition-monitoring approaches. Existing approaches often utilize impedance measurements over a range of frequencies or velocity measurements and require additional equipment or sensors, such as a laser Doppler vibrometer. Furthermore, the non-negligible effects of varying operating conditions are often unconsidered. To minimize the need for additional sensors while maintaining the dependability of piezoelectric bending actuators irrespective of varying operating conditions, an online diagnostics approach is proposed. To this end, time- and frequency-domain features are extracted from monitored current signals to reflect hairline crack development in bending actuators. For validation of applicability, the presented analysis method was evaluated on piezoelectric bending actuators subjected to accelerated lifetime tests at varying voltage amplitudes and under external damping conditions. In the presence of a crack and due to a diminished stiffness, the resonance frequency decreases and the root-mean-square amplitude of the current signal simultaneously abruptly drops during the lifetime tests. Furthermore, the piezoelectric crack surfaces clapping is reflected in higher harmonics of the current signal. Thus, time-domain features and harmonics of the current signals are sufficient to diagnose hairline cracks in the actuators. article_number: '521' article_type: original 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: Tobias full_name: Hemsel, Tobias id: '210' last_name: Hemsel - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro citation: ama: Aimiyekagbon OK, Bender A, Hemsel T, Sextro W. Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions. Electronics. 2024;13(3). doi:10.3390/electronics13030521 apa: Aimiyekagbon, O. K., Bender, A., Hemsel, T., & Sextro, W. (2024). Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions. Electronics, 13(3), Article 521. https://doi.org/10.3390/electronics13030521 bibtex: '@article{Aimiyekagbon_Bender_Hemsel_Sextro_2024, title={Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions}, volume={13}, DOI={10.3390/electronics13030521}, number={3521}, journal={Electronics}, publisher={MDPI AG}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Hemsel, Tobias and Sextro, Walter}, year={2024} }' chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, Tobias Hemsel, and Walter Sextro. “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions.” Electronics 13, no. 3 (2024). https://doi.org/10.3390/electronics13030521. ieee: 'O. K. Aimiyekagbon, A. Bender, T. Hemsel, and W. Sextro, “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions,” Electronics, vol. 13, no. 3, Art. no. 521, 2024, doi: 10.3390/electronics13030521.' mla: Aimiyekagbon, Osarenren Kennedy, et al. “Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions.” Electronics, vol. 13, no. 3, 521, MDPI AG, 2024, doi:10.3390/electronics13030521. short: O.K. Aimiyekagbon, A. Bender, T. Hemsel, W. Sextro, Electronics 13 (2024). date_created: 2024-02-20T06:46:43Z date_updated: 2024-03-15T16:15:56Z department: - _id: '151' doi: 10.3390/electronics13030521 funded_apc: '1' intvolume: ' 13' issue: '3' keyword: - piezoelectric transducer - self-sensing - fault detection - diagnostics - hairline crack - condition monitoring language: - iso: eng publication: Electronics publication_identifier: issn: - 2079-9292 publication_status: published publisher: MDPI AG quality_controlled: '1' status: public title: Diagnostics of Piezoelectric Bending Actuators Subjected to Varying Operating Conditions type: journal_article user_id: '9557' volume: 13 year: '2024' ... --- _id: '47116' abstract: - lang: eng text: This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %. author: - first_name: Osarenren Kennedy full_name: Aimiyekagbon, Osarenren Kennedy id: '9557' last_name: Aimiyekagbon - first_name: Alexander full_name: Löwen, Alexander last_name: Löwen - first_name: Amelie full_name: Bender, Amelie id: '54290' last_name: Bender - first_name: Lars full_name: Muth, Lars id: '77313' last_name: Muth orcid: 0000-0002-2938-5616 - first_name: Walter full_name: Sextro, Walter id: '21220' last_name: Sextro citation: ama: 'Aimiyekagbon OK, Löwen A, Bender A, Muth L, Sextro W. Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System. In: Proceedings of the Asia Pacific Conference of the PHM Society 2023 . Vol 4. ; 2023. doi:10.36001/phmap.2023.v4i1.3596' apa: Aimiyekagbon, O. K., Löwen, A., Bender, A., Muth, L., & Sextro, W. (2023). Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System. Proceedings of the Asia Pacific Conference of the PHM Society 2023 , 4(1). https://doi.org/10.36001/phmap.2023.v4i1.3596 bibtex: '@inproceedings{Aimiyekagbon_Löwen_Bender_Muth_Sextro_2023, title={Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System}, volume={4}, DOI={10.36001/phmap.2023.v4i1.3596}, number={1}, booktitle={Proceedings of the Asia Pacific Conference of the PHM Society 2023 }, author={Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Bender, Amelie and Muth, Lars and Sextro, Walter}, year={2023} }' chicago: Aimiyekagbon, Osarenren Kennedy, Alexander Löwen, Amelie Bender, Lars Muth, and Walter Sextro. “Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System.” In Proceedings of the Asia Pacific Conference of the PHM Society 2023 , Vol. 4, 2023. https://doi.org/10.36001/phmap.2023.v4i1.3596. ieee: 'O. K. Aimiyekagbon, A. Löwen, A. Bender, L. Muth, and W. Sextro, “Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System,” in Proceedings of the Asia Pacific Conference of the PHM Society 2023 , 2023, vol. 4, no. 1, doi: 10.36001/phmap.2023.v4i1.3596.' mla: Aimiyekagbon, Osarenren Kennedy, et al. “Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System.” Proceedings of the Asia Pacific Conference of the PHM Society 2023 , vol. 4, no. 1, 2023, doi:10.36001/phmap.2023.v4i1.3596. short: 'O.K. Aimiyekagbon, A. Löwen, A. Bender, L. Muth, W. Sextro, in: Proceedings of the Asia Pacific Conference of the PHM Society 2023 , 2023.' date_created: 2023-09-18T07:52:32Z date_updated: 2023-09-21T14:51:27Z department: - _id: '151' doi: 10.36001/phmap.2023.v4i1.3596 intvolume: ' 4' issue: '1' keyword: - PHM - Fault Diagnostics - Multiple Fault Modes - Expert-Informed Diagnostics - Anomaly Detection language: - iso: eng main_file_link: - open_access: '1' url: https://www.papers.phmsociety.org/index.php/phmap/article/view/3596 oa: '1' publication: 'Proceedings of the Asia Pacific Conference of the PHM Society 2023 ' quality_controlled: '1' status: public title: Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System type: conference user_id: '77313' volume: 4 year: '2023' ... --- _id: '47159' 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. Condition Monitor. 2022:5-10. apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2022). On the applicability of time series features as health indicators for technical systems operating under varying conditions. Condition Monitor, 425, 5–10. bibtex: '@article{Aimiyekagbon_Bender_Sextro_2022, title={On the applicability of time series features as health indicators for technical systems operating under varying conditions}, number={425}, journal={ Condition Monitor}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2022}, pages={5–10} }' 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.” Condition Monitor, 2022. 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,” Condition Monitor, no. 425, pp. 5–10, 2022. mla: Aimiyekagbon, Osarenren Kennedy, et al. “On the Applicability of Time Series Features as Health Indicators for Technical Systems Operating under Varying Conditions.” Condition Monitor, no. 425, 2022, pp. 5–10. short: O.K. Aimiyekagbon, A. Bender, W. Sextro, Condition Monitor (2022) 5–10. date_created: 2023-09-22T09:25:48Z date_updated: 2023-09-22T09:29:51Z department: - _id: '151' issue: '425' language: - iso: eng page: 5 - 10 publication: ' Condition Monitor' publication_date: 2022-08 publication_identifier: issn: - 0268-8050 publication_status: published status: public title: On the applicability of time series features as health indicators for technical systems operating under varying conditions type: newspaper_article user_id: '9557' year: '2022' ... --- _id: '27652' abstract: - lang: ger 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: eng 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" 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. 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. 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.' 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. 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. short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: VDI-Berichte 2391, VDI Verlag GmbH, Düsseldorf, 2021, pp. 197–210.' conference: end_date: 2021-11-17 location: Würzburg name: '3. VDI-Fachtagung ' start_date: 2021-11-16 date_created: 2021-11-22T07:42:44Z date_updated: 2022-01-06T06:57:43Z department: - _id: '151' keyword: - run-to-failure - rubber-metal element - bearing prognostics - non-stationary operating conditions - varying operating conditions - feature extraction - feature selection language: - iso: ger page: 197 - 210 place: Düsseldorf publication: VDI-Berichte 2391 publication_identifier: isbn: - 978-3-18-092391-8 issn: - '0083-5560 ' publication_status: published publisher: VDI Verlag GmbH status: public title: 'Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten ' type: conference user_id: '9557' year: '2021' ... --- _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: 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). 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 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). 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.' 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' ... --- _id: '27111' abstract: - lang: eng text: In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions. author: - first_name: Osarenren Kennedy full_name: Aimiyekagbon, Osarenren Kennedy id: '9557' last_name: Aimiyekagbon - first_name: Lars full_name: Muth, Lars id: '77313' last_name: Muth orcid: 0000-0002-2938-5616 - first_name: Meike Claudia full_name: Wohlleben, Meike Claudia id: '43991' last_name: Wohlleben orcid: 0009-0009-9767-7168 - 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, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics of a Production Line. In: Do P, King S, Fink O, eds. Proceedings of the European Conference of the PHM Society 2021. Vol 6. ; 2021:527-536. doi:10.36001/phme.2021.v6i1.3042' apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro, W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, & O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021 (Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042 bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based Diagnostics of a Production Line}, volume={6}, DOI={10.36001/phme.2021.v6i1.3042}, number={1}, booktitle={Proceedings of the European Conference of the PHM Society 2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve and Fink, Olga}, year={2021}, pages={527–536} }' chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve King, and Olga Fink, 6:527–36, 2021. https://doi.org/10.36001/phme.2021.v6i1.3042. ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based Diagnostics of a Production Line,” in Proceedings of the European Conference of the PHM Society 2021, 2021, vol. 6, no. 1, pp. 527–536, doi: 10.36001/phme.2021.v6i1.3042.' mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production Line.” Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042. short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P. Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021, 2021, pp. 527–536.' conference: name: PHM Society European Conference date_created: 2021-11-03T12:26:39Z date_updated: 2023-09-22T09:13:01Z department: - _id: '151' doi: 10.36001/phme.2021.v6i1.3042 editor: - first_name: Phuc full_name: Do, Phuc last_name: Do - first_name: Steve full_name: King, Steve last_name: King - first_name: Olga full_name: Fink, Olga last_name: Fink intvolume: ' 6' issue: '1' keyword: - PHME 2021 - Feature Selection Classification - Feature Selection Clustering - Interpretable Model - Transparent Model - Industry 4.0 - Real-World Diagnostics - Quality Control - Predictive Maintenance language: - iso: eng main_file_link: - open_access: '1' url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812 oa: '1' page: 527-536 publication: Proceedings of the European Conference of the PHM Society 2021 publication_status: published quality_controlled: '1' status: public title: Rule-based Diagnostics of a Production Line type: conference user_id: '9557' volume: 6 year: '2021' ... --- _id: '17810' abstract: - lang: eng text: In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach. 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. Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. In: PHM Society European Conference. Vol 5. ; 2020.' apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2020). Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. PHM Society European Conference, 5(1). bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2020} }' chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” In PHM Society European Conference, Vol. 5, 2020. ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data,” in PHM Society European Conference, 2020, vol. 5, no. 1. mla: Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” PHM Society European Conference, vol. 5, no. 1, 2020. short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference, 2020.' date_created: 2020-08-11T13:32:40Z date_updated: 2023-09-22T09:13:16Z department: - _id: '151' intvolume: ' 5' issue: '1' keyword: - PHM 2019 - crack propagation - forecasting - unevenly spaced time series - step ahead prediction - short time series language: - iso: eng publication: PHM Society European Conference quality_controlled: '1' status: public title: Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data type: conference user_id: '9557' volume: 5 year: '2020' ...