Prata Interface · 2023
Smart Wind Turbine Prediction Interface de Mantemento Preditivo
- A' Design Award de Prata 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 Interface de Mantemento Preditivo
Smart Wind Turbine Prediction é unha interface de software para a operación e o mantemento intelixentes de parques eólicos. A interface utiliza visualización tridimensional, simulación virtual e gráficos de datos para mostrar o estado dos aeroxeradores e localizar avarías rapidamente. Os modelos de aprendizaxe automática configúranse arrastrando e soltando datos e axustes do modelo, de xeito que se poden cubrir tipos de aeroxeradores individuais ou múltiples. As alertas de avaría entréganse, de media, uns vinte minutos antes, e a precisión do modelo alcanza o 81 por cento. Redúcense os custos de mantemento manual e os riscos de seguridade.
About the A' Design Award de Prata
Smart Wind Turbine Prediction Interface de Mantemento Preditivo gañou o A' Design Award de Prata, un recoñecemento enormemente prestixioso outorgado a deseños de gama alta, fermosos e impresionantes. Os gañadores Prata son obras tremendamente brillantes e sumamente creativas que ilustran a pericia, o talento e a agudeza sobresaíntes dos seus deseñadores. Espérase que Smart Wind Turbine Prediction Interface de Mantemento Preditivo sexa unha obra marabillosa, escaparate de oficio, notablemente profesional e profundamente innovadora. Este deseño gañador do A' Design Award de Prata é unha invención moi notable e admirada que beneficia e fai avanzar os límites da arte, a ciencia, o deseño e a tecnoloxía. Smart Wind Turbine Prediction Interface de Mantemento Preditivo é unha creación moi envexada que esperta desexos, achega sentimentos positivos e prende asombro e marabilla.
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
