{"id":"pbkn43bgjc","doi":{"id":"10.17632/pbkn43bgjc.1","status":"allocated","prefix":"10.17632"},"name":"D2TSAero Dataset for digital twin application in Aeronautics ","description":"The focus of this project is to develop a Digital Thread Twin architecture for Smart maintenance of Aeronautic systems. For the development of a reliable model that can ensure a reliable representation of the real system an Offline environment that mimics real system behavior was developed. \n\nThe simulation is based on the proposed model of an a Aircraft distribution System described in “Safety + ai: A novel approach to update safety models using artificial intelligence\". \n\nThe proposed data set for the use case includes four Runs and five scenarios for each run. Five operating points for each run were simulated in order to represents healthy and Faulty conditions of the system. Faulty conditions are generated using MATLAB by injecting several faults categories at each epoch. \n\nThe proposed data concerns four components of the system mainly hydraulic pump,the  three tanks, Engines, rear and front pumps. Dataset for 11 healthy and faulty scenarios was produced with several failure types for each of these components. Failures includes Noise NOI, Minor in Service Problem SER, Abnormal Instrument Reading AIR , External Leakage ELU and Parameter Deviation PDE. Noise on sensor was also considered to stimulate security threats. \nFault Type 1 : Noise on Instruments \nFault Type 2:  Abnormal Instrument Reading\nFault Type 3: Minor in Service Problem \nFault Type 4: External Leakage\nFault Type 5:  Parameter Deviation\nFault Type 6: Structural Deficiency\nFault Type 7: Breakdown\n\nFeatures for Pumps : Pump flow for the five pumps \nFeatures for Pump Hydraulic: Pump motor speed \nFeatures for Driver: Driver Power \nFeatures for Tank : Volume and temperature for the three tanks \n\nThe produced data set can be used to compare performance of different asset health estimation models and for the development of new predictive maintenance strategies. \n\nThis data empowers researchers to create new models for aeronautics systems maintenance management by leveraging digital twin technology. By using digital twins with muliagent systems and AI, scientists can predict and emulate the behavior of aircraft complex logic from these larger datasets.","version":1,"contributors":[{"profile_id":"84be4df6-81a1-49e7-8229-62519fe44b84","first_name":"ghita","last_name":"Mezzour"}],"files":[{"filename":"DatasetPump.zip","id":"ae3e3c92-42be-4b72-b706-1931e37acdfa","content_details":{"id":"53391d87-04a4-403e-aef6-9e34442e0f96","sha256_hash":"bf2d4be69ad614da16a1549d0373473c5eb37c1d3a4c2f5f0692d6da7ee7b65a","content_type":"application/x-zip-compressed","size":20087422,"created_date":"2024-01-25T23:32:47.78Z","download_url":"https://data.mendeley.com/public-files/datasets/pbkn43bgjc/files/ae3e3c92-42be-4b72-b706-1931e37acdfa/file_downloaded","view_url":"https://data.mendeley.com/public-files/datasets/pbkn43bgjc/files/ae3e3c92-42be-4b72-b706-1931e37acdfa/file_viewed","download_expiry_time":"2126-09-15T07:15:33.422775677Z"},"metrics":{"downloads":0,"previews":0},"size":20087422,"status":"COMPLETED"}],"versions":[{"version":1,"available":true,"publish_date":"2024-01-26T07:13:58.998Z"}],"articles":[],"categories":[{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-170590751","label":"Aeronautics"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-249523549","label":"Knowledge Maintenance"},{"id":"data.elsevier.com/vocabulary/OmniScience/Concept-602555460","label":"Digital Twin Technology"}],"institutions":[],"metrics":{"views":0,"file_downloads":0,"file_previews":0},"available":true,"method":"Faults and their combination can simulated through the introduction of a Failure_Scenarios_Simulation () function that can be manipulated by systems users to test, simulate, and generate ensemble fault stores of Faults data stores. Models for the simulated environment and faults introduction can be found in related paper to the project. ","size":20087422,"owner":{"profile_id":"84be4df6-81a1-49e7-8229-62519fe44b84","first_name":"ghita","last_name":"Mezzour"},"channel":"WEB","owner_id":"84be4df6-81a1-49e7-8229-62519fe44b84","publish_date":"2024-01-26T07:13:58.998Z","data_licence":{"id":"01d9c749-3c4d-4431-9df3-620b2dcfe144","description":"You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.","url":"http://creativecommons.org/licenses/by/4.0","category":"Creative","short_name":"CC BY 4.0","full_name":"Creative Commons Attribution 4.0 International"},"related_links":[],"funders":[],"customer_id":"7377952c-6404-4428-bbfc-77242cfc301e","modified_on":"2024-01-26T00:02:59.575Z","created_on":"2024-01-25T22:22:42.819Z","confidential":false,"links":{"view":"https://data.mendeley.com/datasets/pbkn43bgjc"},"repository":{"id":"MENDELEY_DATA","name":"Mendeley Data"}}