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data model-based sensor fault diagnosis algorithm for closed-loop control systems

MEASUREMENT 2025年 246卷

作者： Han, Xinhao Zhou, Xin Lu, Feng Huang, Jinquan Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China

The varying operational parameters and random noise make it difficult to determine the fault diagnosis thresholds for engine sensors under different working conditions. Therefore, an adaptive threshold-based fault diagnosis method for aeroengine sensors is proposed. A multivariable control system based on the MFAC method is established for the aeroengine. The OS-ELM algorithm employs historical sensor data to train and update the engine baseline model. MFAC dynamically establishes a linear model based on the pseudo-gradient change of control variables from the current sensor data and designs a baseline model tracker to calculate reasonable diagnostic thresholds based on historical sensor data characteristics, thereby improving the efficiency of threshold calculation and diagnostic accuracy. The experimental results validate that this method improves the fault detection rate by at least 30% while ensuring a low false alarm rate, reduces the minimum detectable fault magnitude by 39%, and keeps the fault detection time within 0.2 s.

关键词： Turboprop engine Neural network Adaptive threshold setting Real-time sensor fault diagnosis Nonlinear tracker data model

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data model to enable multidimensional process mining for data farming based value stream planning in production networks

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2025年第2期19卷 307-327页

作者： Kroeger, Sebastian Rafles, Alrisyadani Jordan, Patrick Soellner, Christoph Zaeh, Michael F. Tech Univ Munich TUM Sch Engn & Design Inst Machine Tools & Ind Management Bolzmannstr 15 D-85747 Munich Bavaria Germany Bayer Motoren Werke BMW AG Petuelring 130 D-80809 Munich Bavaria Germany

International industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.

关键词： data model Production networks Value stream data farming Multidimensional process mining data cubes

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Uncertainty quantification data model for the probabilistic design of the thermal protection system of a reusable launch vehicle stage

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CEAS SPACE JOURNAL 2025年 1-19页

作者： Koch, Aaron D. Wilken, Jascha Alder, Marko German Aerosp Ctr DLR Inst Space Syst Robert Hooke Str 7 D-28359 Bremen Germany German Aerosp Ctr DLR Inst Syst Architectures Aeronaut Hein Sass Weg 22 D-21129 Hamburg Germany

Early design phases significantly impact a system's life-cycle costs, yet they are fraught with large uncertainties. Hence, it is important to incorporate uncertainties in preliminary design activities. However, sampling the uncertain design space instead of analyzing a single system entails a considerable computational cost. It also demands a high degree of automation. Standardized data models can significantly aid this effort by offering a structured way of storing and handling large amounts of data. Therefore, this paper proposes an uncertainty quantification (UQ) data model for dealing with probabilistic data. Its hierarchical structure has been specified using Extensible Markup Language Schema Definition (XSD). It is complemented by a library written in C++ and provides additional Python bindings. The proposed approach is used in a case study in which the design of a thermal protection system (TPS) of the wing profile of a reusable launch vehicle stage is assessed. The UQ data model was used to store the UQ data of the TPS design study.

关键词： Probabilistic design Uncertainty quantification data model XML schema definition (XSD) Reusable launch vehicle (RLV) Thermal protection system (TPS)

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Designing Minimum data Set and data model for Electronic Health Record Systems in Indonesia

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第3期16卷 554-563页

作者： Darmizal, Teddie Ubaidullah, Nor Hasbiah Saad, Aslina Univ Pendidikan Sultan Idris SIG Informat Syst & Technol Integrat ISTI Fac Comp & Meta Technol Tanjung Malim Malaysia Univ Islam Negeri Sultan Syarif Kasim Riau Fac Sci & Technol Departemen Informat Engn Pekanbaru Indonesia

This study aimed to design a minimum data set (MDS) and data model for electronic health record system (EHRS) in Indonesia. The content of the MDS in this study is different from the MDS from the results of the study in other advanced countries. The technical preparation of the MDS in this study follows the medical service process provided to patients from the time they first enter the hospital until they complete receiving services at the hospital with the aim that the MDS designed is aligned with real-world hospital workflows. The initial stage of this research began by identifying data elements through literature reviews sourced from medical record documents of general hospitals and psychiatric hospitals in Indonesia, papers regarding minimum data set in other advanced countries, websites, and clinical guidelines. The Delphi technique was employed to validate the identified data elements through a survey of medical experts. A questionnaire was designed to determine data elements in both administrative and clinical departments. There were 5 and 21 data classes agreed upon by experts in the administrative and clinical sections with 28 and 858 data elements, respectively. This MDS could be a reliable tool for data standardization in EHRS that can improve the quality of data and medical services in hospitals. The designed data model consist of conceptual, logical and physical component. This MDS and data model can facilitate system developers to build physical EHRS database and health surveillance center for more efficient health data management.

关键词： Minimum data set data element data model electronic health record electronic health record system

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Conversion of Sensitive data to the Observational Medical Outcomes Partnership Common data model: Protocol for the Development and Use of Carrot

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JMIR RESEARCH PROTOCOLS 2025年 14卷 e60917页

作者： Cox, Samuel Masood, Erum Panagi, Vasiliki Macdonald, Calum Milligan, Gordon Horban, Scott Santos, Roberto Hall, Chris Lea, Daniel Tarr, Simon Mumtaz, Shahzad Akashili, Emeka Rae, Andy Urwin, Esmond Cole, Christian Sheikh, Aziz Jefferson, Emily Quinlan, Philip Roy Univ Nottingham Digital Res Serv Nottingham England Univ Dundee Hlth Informat Ctr Dundee Scotland Univ Edinburgh Edinburgh Scotland Univ Nottingham NIHR Nottingham Biomed Res Ctr Sch Med Nottingham England Univ Oxford Nuffield Dept Primary Care Hlth Sci Oxford England

Background: The use of data standards is low across the health care system, and converting data to a common data model (CDM) is usually required to undertake international *** is the Observational Medical Outcomes Partnership (OMOP) CDM. It has gained substantial traction across researchers and those who have developed data platforms. The Observational Health Care data Sciences and Informatics (OHDSI) partnership manages OMOP and provides many open-source tools to assist in converting data to the OMOP CDM. The challenge, however, is in the skills, knowledge, know-how, and capacity within teams to convert their data to OMOP. The European Health Care data Evidence Network provided funds to allow data owners to bring in external resources to do the required conversions. The Carrot software (University of Nottingham) is a new set of open-source tools designed to help address these challenges while not requiring data access by external resources. Objective: The use of data protection rules is increasing, and privacy by design is a core principle under the European and UK legislations related to data protection. Our aims for the Carrot software were to have a standardized mechanism for managing the data curation process, capturing the rules used to convert the data, and creating a platform that can reuse rules across projects to drive standardization of process and improve the speed without compromising on quality. Most importantly, we aimed to deliver this design-by-privacy approach without requiring data access to those creating the rules. Methods: The software was developed using Agile approaches by both software engineers and data engineers, who would ultimately use the system. Experts in OMOP were used to ensure the approaches were correct. An incremental release program was initiated to ensure we delivered continuous progress. Results: Carrot has been delivered and used on a project called COVID-Curated and Open Analysis and Research Platform (CO-C

关键词： data standardization OMOP Observational Medical OutcomesPartnership ETL extract, transform, and load data discovery transparency Carrot tool common data model data standard health care data model data protection data privacy open-source

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Adapting OGC's SensorThings API and data model to support data management and sharing for environmental sensors

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ENVIRONMENTAL modelLING & SOFTWARE 2025年 183卷

作者： Horsburgh, Jeffery S. Lippold, Kenneth Slaugh, Daniel L. Utah State Univ Dept Civil & Environm Engn Utah Water Res Lab 8200 Old Main Hill Logan UT 84322 USA Utah State Univ Utah Water Res Lab Logan UT USA

Software is critical in managing environmental sensor data. The Open Geospatial Consortium (OGC) developed the "OGC SensorThings API" (STA) standard to address variability across sensors, observed variables, platforms, and protocols, facilitating development of sensing and Internet of Things applications. This paper details a Python/Django implementation of the STA application programming interface (API) and a PostgreSQL/Timescale implementation of the STA data model, enhancing availability of robust software for management and sharing of environmental sensor data. STA offers a RESTful interface with JSON data encoding, aligning with modern development patterns and facilitating interoperability. Integration of metadata from the Observations data model ensures data can be adequately described and interpreted. STA's flexibility allows lightweight query responses or comprehensive metadata inclusion, and a complementary data management API enhances use of STA within multi-user systems. Open-source code and deployment instructions in GitHub enable standalone or cloud deployments, enhancing accessibility and usability for researchers and practitioners.

关键词： OGC SensorThings API data model Environmental sensors ODM Water resources HydroServer

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data model Analysis and Integration Technology Based on Electric Power

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2019年第10期91卷 1249-1257页

作者： Guo, Gang Dai, Bo Li, Dong Lin, YongHui Xu, Zheng Li, Bo Xi An Jiao Tong Univ Xian 710000 Shaanxi Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou 310000 Zhejiang Peoples R China State Grid Shandong Prov Elect Power Co Ltd Jinan 250000 Shandong Peoples R China State Grid Informat & Telecommun Grp Co Ltd Beijing 100000 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China

Based on the construction of the smart grid in Sino-Singapore Tianjin Eco-city as the background, the data model between the electric power system and the other industries is studied in this paper. Through the analysis of the source and characteristics of the multivariate energy data in Sino-Singapore Tianjin Eco-City, it is put forward that the data model analysis problem should be modeled into the multi attribute negotiation problem. In addition, the multi attribute negotiation utility function that can reflect the degree of association between different attributes is provided. The data model analysis and integration technology study based on the electric power is implemented by using the MATLAB, and the analysis on several sets of numerical examples is carried out. The experimental results show that valuable data attribute values can be obtained by using the data model analysis and the integration technology.

关键词： Meta energy data data model Integration technology Negotiated bargaining

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data model MANAGEMENT IN A CLINICAL SERVICE - APPLICATION IN AN ECHOGRAPHY UNIT FOR TOCHO-GYNECOLOGY

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MEDICAL INFORMATICS 1994年第4期19卷 331-346页

作者： DONO, L RIOS, F CODESIDO, J FAC MED GALICIA DEPT OBSTET & GINECOLGALICIASPAIN

The organization and management of clinical information must take place according to the classical concept of department section or specialty area. Traditional organization and communication models must be respected, and those to be executed by the computer system must be developed on top of them. Information models that describe the organic structure of any specialty area must be proposed independently from the content and with the possibility of particularizing in each case. Taking these specifications into account and using our general entity relationship model for a specialty service, we present the description of the 'central clinical services function, particularizing for an echography unit within a tocho-gynaecology area. We present entity and relationship tables, data dictionary and organization and description of the basic user views.

关键词： data model INFORMATION model CLINICAL ORGANIZATION AND PLANNING

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data model for a decision support in comprehensive nutrient management in the United States

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ENVIRONMENTAL modelLING & SOFTWARE 2006年第6期21卷 852-867页

作者： De, Sahadeb Bezuglov, Anton Univ S Carolina Inst Earth Sci & Resources Columbia SC 29208 USA

Decision Support Systems (DSSs) have been developed to aid planners in achieving a 'suitable plan' from a complex decision space. AFOPro is one such DSS, developed in the present work, for comprehensive nutrient management in the United States. More specifically, AFOPro is a response to the need to track nitrogen (N), phosphorus (P) and potassium (K) in commercial fertilizer and in the livestock manure generated by the Confined Animal Feeding Operations (CAFOs), applied as nutrient source in agricultural fields in the United States. This system may be used by the planner to make allocation decisions optimum for an agricultural field to meet the crop needs, while minimizing the possible impacts on the watersheds. AFOPro consists of two essential modules - (1) AFOPro Core, the nutrient management decision module, (2) AFOPro Web, the web module for updating the knowledge base. AFOPro is flexible, transparent and interactive and supported 21 states of the United States as of February 2005. The number of users of this system has grown to more than 350 nationwide since its first release in May 2003. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： animal feeding operation data model decision support system nutrient management access control AFOPro United States

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data model for system conceptualization in groundwater studies

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2010年第5期24卷 677-694页

作者： Yang, Xiaoying Steward, David R. de Lange, Willem J. Lauwo, Simon Y. Chubb, Richard M. Bernard, Eric A. Kansas State Univ Dept Civil Engn Manhattan KS 66506 USA RIZA Inst Inland Water Management & Waste Water Manage Lelystad Netherlands Kansas State Univ Off Mediated Educ Manhattan KS 66506 USA Kansas State Univ Dept Landscape Architecture Reg & Community Plann Manhattan KS 66506 USA

Development of a conceptualization of a hydrogeologic system serves as the basis of groundwater modeling. While existing groundwater data models are designed to store groundwater system information, none is designed to capture its conceptual view. This study addresses this need by presenting a new object-oriented Conceptualization Groundwater data model that represents a groundwater system as a series of aquifer layers with defined aquifer properties and water boundary conditions. A case study is presented that develops the conceptual view of the groundwater system beneath Konza Prairie. This single conceptualization is used to support groundwater models across existing technologies of finite difference, finite element, and analytical element methods. While the models each employ different mathematics, data input files, and formats, all models are founded on the same conceptualization process that is represented using this new data model. The case study illustrates the data model's promise as an effective mechanism for groundwater system conceptualization and data storage, and utility for various groundwater computational models. This conceptualization of a groundwater data model suggests a new focus on incorporating system conceptualization into data model design.

关键词： data model conceptualization groundwater simulation

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