Sëlwer Interface · 2023
Smart Wind Turbine Prediction Prediktiv Maintenance Interface
- A' Design Award an Sëlwer 2023 in Interface
- CLIENT: Jianxin Technology Co., Ltd
- Estimated Number of Design Views: 52,172
- Verification code 5059508A13 · Verify this award
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About Smart Wind Turbine Prediction Prediktiv Maintenance Interface
Smart Wind Turbine Prediction ass eng Software-Interface fir den intelligente Betrib an d'Maintenance vu Wandparken. D'Interface benotzt dreidimensional Visualiséierung, virtuell Simulatioun an Dategrafiken, fir den Zoustand vun den Turbinnen ze weisen a Feeler séier ze lokaliséieren. Machine-Learning-Modeller ginn duerch Drag-and-Drop vun Daten a Modell-Astellunge konfiguréiert, sou datt eenzel oder verschidden Turbinnentypen ofgedeckt kënne ginn. Feelerwarnunge ginn am Duerchschnëtt ongeféier zwanzeg Minutte méi fréi geliwwert, an d'Modellgenauegkeet erreecht 81 Prozent. Manuell Maintenancekäschten a Sécherheetsrisike gi reduzéiert.
About the A' Design Award an Sëlwer
Smart Wind Turbine Prediction Prediktiv Maintenance Interface ass e Gewënner vum A' Design Award an Sëlwer, enger aussergewéinlech prestigiéiser Unerkennung, déi un Designe vun der héchster Qualitéit, prächteg a beandrockend vergi gëtt. Sëlwer-Gewënner si bemierkenswäert brillant a ganz kreativ Wierker, déi d'erausragend Expertise, d'Talent an d'Schaarfsënnegkeet vun hire Designer weisen. Et gëtt erwaart, datt Smart Wind Turbine Prediction Prediktiv Maintenance Interface e wonnerbart, mustergëlteg, bemierkenswäert professionellt an déif innovatiivt Wierk ass. Dëst mam Sëlwer ausgezeechent Design ass eng bemierkenswäert, bewonnert Erfindung, déi dozou bäidréit, d'Grenze vun der Konscht, der Wëssenschaft, vum Design an der Technologie ze erweideren. Smart Wind Turbine Prediction Prediktiv Maintenance Interface ass eng Kreatioun, op déi vill neidesch sinn, déi Wënsch erweckt, positiv Gefiller bréngt an Erstaunen a Wonner erweckt.
Press Kit
- Unique Properties
- Through the visualization of wind turbine models and data graphs, we can quickly pinpoint the fault in wind turbines and efficiently safeguard the operations and maintenance process. Through the “drag and drop” data and model settings, we can meet the custom settings of “same type and same module” and “multiple types and multiple modules” from various machine learning models to hone targeted operations and maintenance trouble-shooting capabilities.
- Dimensions & Packaging
- Width 1920 Height 1080
- Tags
- AI System Design , AI , AI Applications , Machine Learning , Modeling , Big Data
- Production Technology
- Using 3D visualization modeling, virtual simulation, surveillance visualization, and other technical means, we intelligently display the physical reality space in the digital system, provide interpretable and traceable machine learning capabilities, and simulate the logic behind users’ real interactive operations in the physical world. Our solution enables enterprise customers to receive fault warnings (on average) 20 minutes earlier, and the overall model accuracy rate has reached 81%.
- Design Challenge
- It also enables more effective use of wind resources and contributes to the global governance effort to combat climate change.
- Research
- It addresses user pain points of high manual operations and maintenance costs and improves operations and maintenance personnel safety and efficiency.
- Inspiration
- The system is an intelligent operation solution for renewable energy scenarios based on the digital base of Industrial IoT and the prediction capability of machine learning models. It provides decision support for the wind farm's intelligent operation and maintenance, turbine start/stop, and fault analysis.The system provides manageability for power generation capacity and allows stable and continuous output through intelligent, decision-driven solutions.
- Project Duration
- The project was completed in Beijing in 2022
- Operation Flow
- The design language follows the principle of intelligence and efficiency, and it aims to deliver a brand-new experience to users through digital and intelligent interactions.
- Image Credits
- Design creativity：Yun Xu , Wei Wang
Interface design: Yanfen Zhang
Interaction design：Xinjie Li
Video Copyright：Shaoxiong Bai
