Steffen Klein, Yannick Wilhelm, Andreas Schütze, T. Schneider
Machine learning in industrial condition monitoring is currently a rapidly developing field of research, to improve the efficiency and reliability of industrial processes. Many of the used algorithms are supervised methods, which can learn and recognize hidden patterns in the data. However, training data is required to learn these patterns, which can only be generated to a limited extent in an industrial environment due to the high costs involved. Furthermore, it is impossible to represent all possible events in the training data. In contrast, unsupervised or semi-supervised methods can be used to detect new conditions or events. However, these usually do not allow diagnosis or quantification of a fault condition, which is why their usefulness for modern maintenance strategies is limited. Consequently, a robust condition monitoring system should combine the functionality of both approaches. This paper presents a methodology for the combination of supervised classification and semi-supervised novelty detection to build an expandable and adaptable condition monitoring by transferring recurring novelties as new conditions to the supervised classification. A superordinate algorithm is proposed to achieve a stepwise extension of the supervised model based on new conditions detected by novelty detection. With this approach, a condition monitoring system can at first be based on “normal” data of a new machine or process by adding failures or novel conditions step-by-step. Furthermore, the supervised methods can be used to help the corresponding staff identify unknown conditions by analyzing the features selected by the supervised classification. The general workflow is demonstrated for condition monitoring of the pneumatic drive system of a welding gun.
{"title":"Combination of generic novelty detection and supervised classification pipelines for industrial condition monitoring","authors":"Steffen Klein, Yannick Wilhelm, Andreas Schütze, T. Schneider","doi":"10.1515/teme-2024-0016","DOIUrl":"https://doi.org/10.1515/teme-2024-0016","url":null,"abstract":"\u0000 Machine learning in industrial condition monitoring is currently a rapidly developing field of research, to improve the efficiency and reliability of industrial processes. Many of the used algorithms are supervised methods, which can learn and recognize hidden patterns in the data. However, training data is required to learn these patterns, which can only be generated to a limited extent in an industrial environment due to the high costs involved. Furthermore, it is impossible to represent all possible events in the training data. In contrast, unsupervised or semi-supervised methods can be used to detect new conditions or events. However, these usually do not allow diagnosis or quantification of a fault condition, which is why their usefulness for modern maintenance strategies is limited. Consequently, a robust condition monitoring system should combine the functionality of both approaches. This paper presents a methodology for the combination of supervised classification and semi-supervised novelty detection to build an expandable and adaptable condition monitoring by transferring recurring novelties as new conditions to the supervised classification. A superordinate algorithm is proposed to achieve a stepwise extension of the supervised model based on new conditions detected by novelty detection. With this approach, a condition monitoring system can at first be based on “normal” data of a new machine or process by adding failures or novel conditions step-by-step. Furthermore, the supervised methods can be used to help the corresponding staff identify unknown conditions by analyzing the features selected by the supervised classification. The general workflow is demonstrated for condition monitoring of the pneumatic drive system of a welding gun.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"86 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.
{"title":"Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring","authors":"S. Eichstädt, Olav Werhahn","doi":"10.1515/teme-2024-0042","DOIUrl":"https://doi.org/10.1515/teme-2024-0042","url":null,"abstract":"\u0000 \u0000 In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"67 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anna-Lena Knott, M. Huber, Ugur Karakus, Tobias Müller, R. Schmitt
Die Hauptgefahr bei minimalinvasiven Bohrungen für die Gesundheit des Patienten ist eine thermische Verletzung von Nerven- oder Knochengewebe durch einen erhöhten Wärmeeintrag. Für die prozessparallele Ermittlung der Bohrgrundtemperatur wurde ein Bohrer mit integrierter Temperatursensorik entwickelt. Diese gemessene Temperatur steht jedoch in einem unbekannten Zusammenhang mit der realen Bohrgrundtemperatur, da Unsicherheiten die Messung beeinflussen. Um die Temperatur als verlässliche Entscheidungsgrundlage während der minimalinvasiven Bohrung verwenden zu können, müssen systematische Abweichungen der gemessenen Temperatur von der realen Temperatur bekannt sein. Zufällige Abweichungen und solche systematischen Abweichungen, die nicht korrigiert werden können, müssen in einer Messunsicherheitsbetrachtung zusammengefasst werden. Zur Bestimmung der Kalibrierkurve wird ein Messaufbau entworfen, um systematische Fehler der Temperaturmessung mit dem Bohrer kompensieren zu können. Die Ergebnisse der Unsicherheitsbetrachtung zeigen, dass die Unsicherheit mit steigender Temperatur steigt. Die Unsicherheit wird konservativ mit u T = 1 K abgeschätzt. Zur Beurteilung einer thermischen Schädigung des Gewebes wird der CEM43 herangezogen, dessen Güte jedoch in erheblichem Maß von der zugrundeliegenden Datenqualität abhängt. In einer Analyse des Einflusses der Messunsicherheit auf den CEM43 mit der Unsicherheitsfortpflanzung und Monte-Carlo-Methoden wird festgestellt, dass bereits geringe Unsicherheiten in der Temperaturmessung zu erheblichen Abweichungen des CEM43 führen. Der intraoperative Einsatz des CEM43 als Kennwert für eine thermische Gewebeschädigung ist deshalb nicht möglich.
在微创钻孔过程中，患者健康面临的主要风险是因输入热量增加而对神经或骨组织造成的热损伤。已开发出一种集成温度传感器的钻头，可在钻孔过程中同时测定钻头基部的温度。然而，由于测量结果受到不确定因素的影响，因此所测得的温度与实际钻头基座温度之间的相关性尚不清楚。为了在微创钻孔过程中将温度作为可靠的决策依据，必须知道测量温度与实际温度之间的系统偏差。随机偏差和无法纠正的系统偏差必须在测量不确定性分析中进行总结。为了确定校准曲线，设计了一套测量装置，以补偿钻头温度测量中的系统误差。不确定度分析的结果表明，不确定度随着温度的升高而增加。不确定度的保守估计值为 u T = 1 K。CEM43 用于评估组织的热损伤，但其质量在很大程度上取决于基础数据的质量。在使用不确定性传播和蒙特卡罗方法分析测量不确定性对 CEM43 的影响时发现，即使温度测量的不确定性很小，也会导致 CEM43 出现相当大的偏差。因此，不可能在术中使用 CEM43 作为热组织损伤的参数。
{"title":"Kalibrierung und Messunsicherheitsbetrachtung eines medizinischen Bohrers mit integrierter Temperatursensorik zur Minimierung des Patientenrisikos bei minimalinvasiven Bohrungen an der lateralen Schädelbasis","authors":"Anna-Lena Knott, M. Huber, Ugur Karakus, Tobias Müller, R. Schmitt","doi":"10.1515/teme-2024-0030","DOIUrl":"https://doi.org/10.1515/teme-2024-0030","url":null,"abstract":"\u0000 Die Hauptgefahr bei minimalinvasiven Bohrungen für die Gesundheit des Patienten ist eine thermische Verletzung von Nerven- oder Knochengewebe durch einen erhöhten Wärmeeintrag. Für die prozessparallele Ermittlung der Bohrgrundtemperatur wurde ein Bohrer mit integrierter Temperatursensorik entwickelt. Diese gemessene Temperatur steht jedoch in einem unbekannten Zusammenhang mit der realen Bohrgrundtemperatur, da Unsicherheiten die Messung beeinflussen. Um die Temperatur als verlässliche Entscheidungsgrundlage während der minimalinvasiven Bohrung verwenden zu können, müssen systematische Abweichungen der gemessenen Temperatur von der realen Temperatur bekannt sein. Zufällige Abweichungen und solche systematischen Abweichungen, die nicht korrigiert werden können, müssen in einer Messunsicherheitsbetrachtung zusammengefasst werden. Zur Bestimmung der Kalibrierkurve wird ein Messaufbau entworfen, um systematische Fehler der Temperaturmessung mit dem Bohrer kompensieren zu können. Die Ergebnisse der Unsicherheitsbetrachtung zeigen, dass die Unsicherheit mit steigender Temperatur steigt. Die Unsicherheit wird konservativ mit u\u0000 \u0000 T\u0000 = 1 K abgeschätzt. Zur Beurteilung einer thermischen Schädigung des Gewebes wird der CEM43 herangezogen, dessen Güte jedoch in erheblichem Maß von der zugrundeliegenden Datenqualität abhängt. In einer Analyse des Einflusses der Messunsicherheit auf den CEM43 mit der Unsicherheitsfortpflanzung und Monte-Carlo-Methoden wird festgestellt, dass bereits geringe Unsicherheiten in der Temperaturmessung zu erheblichen Abweichungen des CEM43 führen. Der intraoperative Einsatz des CEM43 als Kennwert für eine thermische Gewebeschädigung ist deshalb nicht möglich.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":" 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Für Prüflabore ist eine Aussage zur Konformität, ihrer Mess- und Prüfergebnisse gegenüber einer Spezifikationsanforderung stets mit dem Risiko einer falschen Annahme oder Ablehnung verbunden. Entscheidenden Einfluss, wie groß dieses statistische Risiko ist, hat neben dem eigentlichen Messwert die zugehörige Messunsicherheit. Daher haben Prüfaboratorien ein vitales Interesse daran, die Messunsicherheit fachlich angemessen zu ermitteln und ggf. zu minimieren. Dazu bildet die Auswahl geeigneter Mess- und Prüfeinrichtungen die Grundlage, um Ergebnisse möglichst präzise und genau, aber auch vergleichbar und reproduzierbar zu ermitteln. In diesem Beitrag wird ein konkretes Beispiel aus der Materialprüfung am Beispiel des Zugversuchs an metallischen Werkstoffen bei erhöhter Temperatur nach DIN EN ISO 6892-2:2018-09 vorgestellt. Diese international harmonisierte Prüfnorm gibt Anforderungen an die einzusetzende Temperaturmesseinrichtung, hinsichtlich der zulässigen Abweichung und Messunsicherheit, vor. Zusammen mit den Kompetenzanforderungen aus DIN EN ISO/IEC 17025:2018-03, der Norm, die zusammen mit weiteren Regelwerken zur Akkreditierung von Prüflaboren herangezogen wird, ergeben sich weitere Anforderungen an die nachgewiesene, metrologische Rückführbarkeit und die Messunsicherheit. Fragen zu notwendigen Kalibrierintervallen und Wartungen runden den Informationsbedarf über Mess- und Prüfgeräte ab. Die damit verbleibenden Freiheitsgrade für die Anwender in Bezug auf die Auswahl der Temperaturmesseinrichtung werden weiter beschränkt, wenn für die Konformitätsaussage eine entsprechend geringe Messunsicherheit erforderlich ist. Der Beitrag beinhaltet konkrete Beispiele aus der Praxis zum Einfluss der Messunsicherheit der Temperaturmessung im Zugversuch nach DIN EN ISO 6892-2. Es werden sowohl die Herausforderungen an die Prüflaboratorien als auch mögliche Lösungsansätze aufgezeigt.
对于测试实验室来说，如果要说明其测量和测试结果是否符合规范要求，就必须承担错误验收或拒收的风险。除了实际测量值之外，相关的测量不确定度对这种统计风险的大小也有决定性的影响。因此，如何确定测量不确定度，并在必要时以技术适当的方式将测量不确定度降到最低，对测试实验室来说至关重要。选择合适的测量和测试设备是确定结果的基础，不仅要尽可能精确和准确，还要具有可比性和可重复性。本文以材料测试为例，介绍根据 DIN EN ISO 6892-2:2018-09，在高温下对金属材料进行拉伸测试的具体实例。该国际统一测试标准规定了所用温度测量设备在允许偏差和测量不确定性方面的要求。除了 DIN EN ISO/IEC 17025:2018-03 中的能力要求（该标准与其他测试实验室认证法规一起使用）外，还对经过验证的计量溯源性和测量不确定性提出了进一步要求。关于必要的校准周期和维护的问题也是测量和测试设备信息需求的一部分。如果合格声明要求相应的低测量不确定度，那么用户在选择温度测量设备方面的剩余自由度就会受到进一步限制。文章列举了在拉伸试验中按照 DIN EN ISO 6892-2 标准进行温度测量时测量不确定度影响的具体实例，并说明了测试实验室面临的挑战和可能的解决方案。
{"title":"Der Einfluss der Messunsicherheit in der Materialprüfung – Von der Messmittelauswahl zur Konformitätsaussage am Beispiel des Zugversuchs bei erhöhter Temperatur nach DIN EN ISO 6892-2:2018-09","authors":"S. Wieler, H. Frenz","doi":"10.1515/teme-2024-0039","DOIUrl":"https://doi.org/10.1515/teme-2024-0039","url":null,"abstract":"\u0000 Für Prüflabore ist eine Aussage zur Konformität, ihrer Mess- und Prüfergebnisse gegenüber einer Spezifikationsanforderung stets mit dem Risiko einer falschen Annahme oder Ablehnung verbunden. Entscheidenden Einfluss, wie groß dieses statistische Risiko ist, hat neben dem eigentlichen Messwert die zugehörige Messunsicherheit. Daher haben Prüfaboratorien ein vitales Interesse daran, die Messunsicherheit fachlich angemessen zu ermitteln und ggf. zu minimieren. Dazu bildet die Auswahl geeigneter Mess- und Prüfeinrichtungen die Grundlage, um Ergebnisse möglichst präzise und genau, aber auch vergleichbar und reproduzierbar zu ermitteln. In diesem Beitrag wird ein konkretes Beispiel aus der Materialprüfung am Beispiel des Zugversuchs an metallischen Werkstoffen bei erhöhter Temperatur nach DIN EN ISO 6892-2:2018-09 vorgestellt. Diese international harmonisierte Prüfnorm gibt Anforderungen an die einzusetzende Temperaturmesseinrichtung, hinsichtlich der zulässigen Abweichung und Messunsicherheit, vor. Zusammen mit den Kompetenzanforderungen aus DIN EN ISO/IEC 17025:2018-03, der Norm, die zusammen mit weiteren Regelwerken zur Akkreditierung von Prüflaboren herangezogen wird, ergeben sich weitere Anforderungen an die nachgewiesene, metrologische Rückführbarkeit und die Messunsicherheit. Fragen zu notwendigen Kalibrierintervallen und Wartungen runden den Informationsbedarf über Mess- und Prüfgeräte ab. Die damit verbleibenden Freiheitsgrade für die Anwender in Bezug auf die Auswahl der Temperaturmesseinrichtung werden weiter beschränkt, wenn für die Konformitätsaussage eine entsprechend geringe Messunsicherheit erforderlich ist. Der Beitrag beinhaltet konkrete Beispiele aus der Praxis zum Einfluss der Messunsicherheit der Temperaturmessung im Zugversuch nach DIN EN ISO 6892-2. Es werden sowohl die Herausforderungen an die Prüflaboratorien als auch mögliche Lösungsansätze aufgezeigt.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"89 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wärmestromsensoren werden in den verschiedensten Applikationen eingesetzt. Dabei ist eine Kalibrierung der Sensoren unumgänglich. In diesem Beitrag wird die Bedeutung von Wärmestromsensoren und die Notwendigkeit einer Kalibrierung aufgezeigt. Zu diesem Zweck wurde am Institut für Prozessmess- und Sensortechnik der Technischen Universität Ilmenau ein Kalibrierstand entwickelt und aufgebaut, der die rückführbare Kalibrierung von Wärmestromsensoren ermöglicht. Mit dieser Einrichtung können Wärmestromsensoren durch einen Vergleich gegenüber einem Referenzwärmestromsensor, basierend auf Wärmeleitprozessen, kalibriert werden. Vor diesem Hintergrund wird der Aufbau der Kalibriereinrichtung sowie der enthaltenen Sensorik diskutiert und eine umfassende Unsicherheitsbetrachtung zum eingeprägten Wärmestrom angestellt. Der Schwerpunkt dieser Unsicherheitsbetrachtung liegt auf der Messung von Temperaturdifferenzen innerhalb der Einrichtung, die einen maßgeblichen Einfluss auf die Unsicherheit des rückführbaren Wärmestroms haben. Aus der umfassenden Unsicherheitsbetrachtung resultiert für den bereitgestellten Wärmestrom in der Kalibriereinrichtung eine um (k = 2) erweiterte relative Messunsicherheit von 2,9 %.
{"title":"Messunsicherheit einer Kalibriereinrichtung für Wärmestromsensoren – Unsicherheit der Temperaturdifferenz","authors":"J. Beerel, Frederik Bartz, Thomas Fröhlich","doi":"10.1515/teme-2024-0034","DOIUrl":"https://doi.org/10.1515/teme-2024-0034","url":null,"abstract":"\u0000 Wärmestromsensoren werden in den verschiedensten Applikationen eingesetzt. Dabei ist eine Kalibrierung der Sensoren unumgänglich. In diesem Beitrag wird die Bedeutung von Wärmestromsensoren und die Notwendigkeit einer Kalibrierung aufgezeigt. Zu diesem Zweck wurde am Institut für Prozessmess- und Sensortechnik der Technischen Universität Ilmenau ein Kalibrierstand entwickelt und aufgebaut, der die rückführbare Kalibrierung von Wärmestromsensoren ermöglicht. Mit dieser Einrichtung können Wärmestromsensoren durch einen Vergleich gegenüber einem Referenzwärmestromsensor, basierend auf Wärmeleitprozessen, kalibriert werden. Vor diesem Hintergrund wird der Aufbau der Kalibriereinrichtung sowie der enthaltenen Sensorik diskutiert und eine umfassende Unsicherheitsbetrachtung zum eingeprägten Wärmestrom angestellt. Der Schwerpunkt dieser Unsicherheitsbetrachtung liegt auf der Messung von Temperaturdifferenzen innerhalb der Einrichtung, die einen maßgeblichen Einfluss auf die Unsicherheit des rückführbaren Wärmestroms haben. Aus der umfassenden Unsicherheitsbetrachtung resultiert für den bereitgestellten Wärmestrom in der Kalibriereinrichtung eine um (k = 2) erweiterte relative Messunsicherheit von 2,9 %.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"143 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spectral reconstruction in filter-based miniature spectrometers remains challenging due to the ill-posed nature of identifying stable solutions. Even minor deviations in sensor data can cause misleading reconstruction outcomes, particularly in the absence of proper regularization techniques. While previous research has attempted to mitigate this instability by incorporating neural networks into the reconstruction pipeline to denoise the data before reconstruction or correct it after reconstruction, these approaches have not fully resolved the underlying issue. This work functions as a proof-of-concept for data-driven reconstruction that relies exclusively on neural networks, thereby circumventing the need to address the ill-posed inverse problem. We curate a dataset holding transmission spectra from various colored foils, commonly used in theatrical, and train five distinct neural networks optimized for spectral reconstruction. Subsequently, we benchmark these networks against each other and compare their reconstruction capabilities with a linear reconstruction model to show the applicability of cognitive sensors to the problem of spectral reconstruction. In our testing, we discovered that (i) spectral reconstruction can be achieved using neural networks with an end-to-end approach, and (ii) while a classic linear model can perform equal to neural networks under optimal conditions, the latter can be considered more robust against data deviations.
{"title":"Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers","authors":"J. Wissing, Lidia Fargueta, Stephan Scheele","doi":"10.1515/teme-2024-0063","DOIUrl":"https://doi.org/10.1515/teme-2024-0063","url":null,"abstract":"\u0000 Spectral reconstruction in filter-based miniature spectrometers remains challenging due to the ill-posed nature of identifying stable solutions. Even minor deviations in sensor data can cause misleading reconstruction outcomes, particularly in the absence of proper regularization techniques. While previous research has attempted to mitigate this instability by incorporating neural networks into the reconstruction pipeline to denoise the data before reconstruction or correct it after reconstruction, these approaches have not fully resolved the underlying issue. This work functions as a proof-of-concept for data-driven reconstruction that relies exclusively on neural networks, thereby circumventing the need to address the ill-posed inverse problem. We curate a dataset holding transmission spectra from various colored foils, commonly used in theatrical, and train five distinct neural networks optimized for spectral reconstruction. Subsequently, we benchmark these networks against each other and compare their reconstruction capabilities with a linear reconstruction model to show the applicability of cognitive sensors to the problem of spectral reconstruction. In our testing, we discovered that (i) spectral reconstruction can be achieved using neural networks with an end-to-end approach, and (ii) while a classic linear model can perform equal to neural networks under optimal conditions, the latter can be considered more robust against data deviations.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"78 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Hausotte, L. Butzhammer, Tamara Reuter, Matthias Braun, Ulrich Grömme
Systematic and random measurement errors are the cause of uncertain conformance and non-conformance statements. A wrong conformance statement occurs if the workpiece is accepted and is a reject part (type II error or false-negative) and a wrong non-conformance statement if the workpiece is rejected and is an in-spec part (type I error or false-positive). In order to avoid type I and type II errors, measurement uncertainty must be taken into account in the conformance and non-conformance testing. In practice, some procedures are used to consider measurement errors or the uncertainty that deviate from the state-of-the-art in research and technology. As these methods have become established over many years, they are still widely used despite better theoretical knowledge. The standard ISO 14253-1:2017 specifies a procedure based on probability and measurement uncertainty that is in accordance to the internationally accepted “Guide to the expression of uncertainty in measurement” and its supplements but is often not used due to the complexity of the evaluation of measurement uncertainty. In this contribution we give an overview and comparison of the different existing methods and provide an suggestion for supplementing the standard ISO 14253-1:2017, as Monte Carlo simulations enable a direct probability-based conformance and non-conformance testing even for complex measurement processes.
系统和随机测量误差是造成不确定的合格和不合格声明的原因。如果工件被验收，但属于剔除部件（II 类错误或假阴性），则会出现错误的一致性声明；如果工件被剔除，但属于合规部件（I 类错误或假阳性），则会出现错误的不符合声明。为了避免 I 类和 II 类错误，必须在一致性和非一致性测试中考虑测量不确定性。在实践中，一些程序被用来考虑偏离最先进研究和技术的测量误差或不确定性。由于这些方法已确立多年，尽管有了更好的理论知识，但仍被广泛使用。ISO 14253-1:2017 标准规定了一种基于概率和测量不确定度的程序，该程序与国际公认的 "测量不确定度表达指南 "及其补充协议一致，但由于测量不确定度评估的复杂性，该程序通常不被使用。在本文中，我们对现有的不同方法进行了概述和比较，并提出了对 ISO 14253-1:2017 标准进行补充的建议，因为蒙特卡罗模拟可以直接进行基于概率的符合性和不符合性测试，即使是复杂的测量过程也不例外。
{"title":"Test of conformance or non-conformance with geometrical specifications","authors":"T. Hausotte, L. Butzhammer, Tamara Reuter, Matthias Braun, Ulrich Grömme","doi":"10.1515/teme-2024-0022","DOIUrl":"https://doi.org/10.1515/teme-2024-0022","url":null,"abstract":"\u0000 Systematic and random measurement errors are the cause of uncertain conformance and non-conformance statements. A wrong conformance statement occurs if the workpiece is accepted and is a reject part (type II error or false-negative) and a wrong non-conformance statement if the workpiece is rejected and is an in-spec part (type I error or false-positive). In order to avoid type I and type II errors, measurement uncertainty must be taken into account in the conformance and non-conformance testing. In practice, some procedures are used to consider measurement errors or the uncertainty that deviate from the state-of-the-art in research and technology. As these methods have become established over many years, they are still widely used despite better theoretical knowledge. The standard ISO 14253-1:2017 specifies a procedure based on probability and measurement uncertainty that is in accordance to the internationally accepted “Guide to the expression of uncertainty in measurement” and its supplements but is often not used due to the complexity of the evaluation of measurement uncertainty. In this contribution we give an overview and comparison of the different existing methods and provide an suggestion for supplementing the standard ISO 14253-1:2017, as Monte Carlo simulations enable a direct probability-based conformance and non-conformance testing even for complex measurement processes.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"40 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141663484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert H. Schmitt, M. Bodenbenner, Tobias Hamann, Mark P. Sanders, Mario Moser, Anas Abdelrazeq
The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand. To ensure this data quality, the data management must be applied consequently throughout the complete Data Life-Cycle (DLC) and adhere to the FAIR guiding principles. In the two research consortia NFDI4Ing and the Cluster of Excellence “Internet of Production,” we investigate approaches to increase the measurement of data quality by integrating the FAIR guiding principles in all data management activities of the DLC. To facilitate the uptake of the FAIR guiding principles, we underline the significance of FAIR data for the reuse of high-quality data. Second, we are introducing a harmonised DLC to streamline data management activities. Third, we concisely review current trends and best practices in FAIR-aware data management and give suggestions for implementing the FAIR guiding principles.
{"title":"Leveraging measurement data quality by adoption of the FAIR guiding principles","authors":"Robert H. Schmitt, M. Bodenbenner, Tobias Hamann, Mark P. Sanders, Mario Moser, Anas Abdelrazeq","doi":"10.1515/teme-2024-0040","DOIUrl":"https://doi.org/10.1515/teme-2024-0040","url":null,"abstract":"\u0000 The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand. To ensure this data quality, the data management must be applied consequently throughout the complete Data Life-Cycle (DLC) and adhere to the FAIR guiding principles. In the two research consortia NFDI4Ing and the Cluster of Excellence “Internet of Production,” we investigate approaches to increase the measurement of data quality by integrating the FAIR guiding principles in all data management activities of the DLC. To facilitate the uptake of the FAIR guiding principles, we underline the significance of FAIR data for the reuse of high-quality data. Second, we are introducing a harmonised DLC to streamline data management activities. Third, we concisely review current trends and best practices in FAIR-aware data management and give suggestions for implementing the FAIR guiding principles.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"98 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yixing Wei, H. Fang, Jianhong Yang, Guoyi Tan, Feizhi Huang
To quickly measure the water absorption (WA) of Recycled Coarse Aggregates (RCA), we utilize a detection platform designed for RCA to collect two-dimensional images. Utilizing the RCA-net network, we segment the areas of the mortar and aggregate on the RCA surface. Segmentations allow us to extract critical parameters for characterizing the quality of RCA, the proportion of mortar area (PMA). Subsequently, we construct three regression functions between PMA and WA. The experimental results demonstrate that our proposed segmentation method effectively separates both adhered particles of RCA and distinct areas of mortar and aggregate on RCA surfaces. Next, sprinkling water on RCA surfaces can enhance the accuracy of the segmentation. Notably, within particle size ranges of 5–10 mm, 10–20 mm, and 20–31.5 mm, we all observed a significant linear relationship between PMA and WA. We used those linear relationships and the equivalent mass of RCA detected by the image method in each particle size range to construct the prediction model of water absorption. According to the validation result of 24 groups RCA, this model’s maximum relative error of RCA water absorption predicted value was 10.6 %. The detection time of this method is short, and the detection time of 2 kg RCA is 3.8 min, with an average computation time per image of merely 0.659 s. This efficiency fulfills the requirements for real-time industrial inspection.
{"title":"Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation","authors":"Yixing Wei, H. Fang, Jianhong Yang, Guoyi Tan, Feizhi Huang","doi":"10.1515/teme-2023-0155","DOIUrl":"https://doi.org/10.1515/teme-2023-0155","url":null,"abstract":"\u0000 To quickly measure the water absorption (WA) of Recycled Coarse Aggregates (RCA), we utilize a detection platform designed for RCA to collect two-dimensional images. Utilizing the RCA-net network, we segment the areas of the mortar and aggregate on the RCA surface. Segmentations allow us to extract critical parameters for characterizing the quality of RCA, the proportion of mortar area (PMA). Subsequently, we construct three regression functions between PMA and WA. The experimental results demonstrate that our proposed segmentation method effectively separates both adhered particles of RCA and distinct areas of mortar and aggregate on RCA surfaces. Next, sprinkling water on RCA surfaces can enhance the accuracy of the segmentation. Notably, within particle size ranges of 5–10 mm, 10–20 mm, and 20–31.5 mm, we all observed a significant linear relationship between PMA and WA. We used those linear relationships and the equivalent mass of RCA detected by the image method in each particle size range to construct the prediction model of water absorption. According to the validation result of 24 groups RCA, this model’s maximum relative error of RCA water absorption predicted value was 10.6 %. The detection time of this method is short, and the detection time of 2 kg RCA is 3.8 min, with an average computation time per image of merely 0.659 s. This efficiency fulfills the requirements for real-time industrial inspection.","PeriodicalId":509687,"journal":{"name":"tm - Technisches Messen","volume":"50 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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