Smart Wind Turbine Prediction Entèfas Antretyen Prediktif by 4Paradigm UED is a highly esteemed winner of the very prestigious 2023 A' Design Award Ajan in Entèfas Competition Category.
AjanEntèfas· 2023
Smart Wind Turbine Prediction Entèfas Antretyen Prediktif
Client: Jianxin Technology Co., Ltd
A' Design Award Ajan Winner in Entèfas Category
- 52,172
- Verified Award Winner
- Verify Award Status for #141912
The Design
About Smart Wind Turbine Prediction Entèfas Antretyen Prediktif
Smart Wind Turbine Prediction se yon entèfas lojisyèl pou operasyon ak antretyen entèlijan pak eolyèn yo. Entèfas la itilize vizyalizasyon twa dimansyon, simulation vityèl ak grafik done pou montre kondisyon tibin yo epi pou lokalize pann yo byen vit. Modèl aprantisaj otomatik yo konfigire lè ou trennen epi depoze done ak paramèt modèl yo, kidonk yon sèl tip tibin oswa plizyè tip tibin ka kouvri. Avètisman sou pann yo rive apeprè ven minit pi bonè an mwayèn, epi presizyon modèl la rive nan 81 pousan. Kou antretyen manyèl yo ak risk sekirite yo diminye.
The Recognition
About the A' Design Award Ajan
Smart Wind Turbine Prediction Entèfas Antretyen Prediktif se yon ganyan A' Design Award Ajan, yon rekonesans ki gen yon prestij enmans yo bay design nivo siperyè, bèl anpil e enpresyonan. Ganyan Ajan yo se travay ki trè briyan e ki trè kreyatif ki montre ekspètiz, talan ak finès designer yo. Yo atann Smart Wind Turbine Prediction Entèfas Antretyen Prediktif pou se yon bèl travay modèl, remakab nan pwofesyonalis e ki gen gwo inovasyon. Design sa a ki ganyen A' Design Award Ajan se yon envansyon trè remakab, admire ki pote benefis e ki pouse limit atizay, syans, design ak teknoloji. Smart Wind Turbine Prediction Entèfas Antretyen Prediktif se yon kreyasyon anpil moun jalou ki reveye dezi, ki pote santiman pozitif e ki limen sipriz ak mèvèy.
Behind the Design
Press Kit
Credits, team members and technical details provided by the designer.
- 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.
- Team Members
- Design Director：Yun Xu
Design Manager：Wei Wang
Designer：Shaoxiong Bai
Designer：Xinjie Li
Designer：Yanfen Zhang - Image Credits
- Design creativity：Yun Xu , Wei Wang
Interface design: Yanfen Zhang
Interaction design：Xinjie Li
Video Copyright：Shaoxiong Bai
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