由設計師 4Paradigm UED 創作嘅 Smart Wind Turbine Prediction 預測性維護介面 係備受推崇嘅 2023 A' Design Award 銀獎 得獎作品，獲獎類別為 介面、互動及用戶體驗設計。
銀介面· 2023
Smart Wind Turbine Prediction 預測性維護介面
客戶: Jianxin Technology Co., Ltd
介面 類別嘅 A' Design Award 銀獎 得獎者
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經驗證嘅獲獎者
驗證設計 #141912 嘅獲獎狀態設計
關於 Smart Wind Turbine Prediction 預測性維護介面
智能風機預測係一套用於風場智能運維嘅軟件介面。介面透過三維可視化、虛擬仿真同數據圖表嚟展示風機狀態,並快速定位故障。機器學習模型可以透過拖放數據同模型設定嚟配置,因此能夠涵蓋單一或多種風機機型。故障預警平均可以提早大約二十分鐘發出,模型準確率達到81%。人工維護成本同安全風險都得以降低。
榮譽認可
關於A' Design Award 銀獎
Smart Wind Turbine Prediction 預測性維護介面是 A' Design Award 銀獎得主，這是授予頂級、華美而令人印象深刻的設計的極為崇高的認可。銀獎得主是極其卓越、極具創造力的作品，展現出其設計師傑出的專業技能、才華與敏銳。預計Smart Wind Turbine Prediction 預測性維護介面將成為一件出色的典範之作，兼具非凡的專業性與深刻的創新性。這件贏得 A' Design Award 銀獎的設計是一項備受矚目、令人欽佩的發明，造福並拓展藝術、科學、設計和技術的界限。Smart Wind Turbine Prediction 預測性維護介面是一件令人羨慕的創作，它喚起渴望，帶來積極的情感，並激發驚嘆與讚嘆。
設計背後
新聞資料包
由設計師提供嘅製作人員名單、團隊成員同技術詳情。
- 獨特屬性
- 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.
- 尺寸同包裝
- Width 1920 Height 1080
- 標籤
- AI System Design , AI , AI Applications , Machine Learning , Modeling , Big Data
- 生產技術
- 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%.
- 設計挑戰
- It also enables more effective use of wind resources and contributes to the global governance effort to combat climate change.
- 研究
- It addresses user pain points of high manual operations and maintenance costs and improves operations and maintenance personnel safety and efficiency.
- 靈感
- 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.
- 項目時長
- The project was completed in Beijing in 2022
- 操作流程
- 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.
- 團隊成員
- Design Director：Yun Xu
Design Manager：Wei Wang
Designer：Shaoxiong Bai
Designer：Xinjie Li
Designer：Yanfen Zhang - 圖片來源
- Design creativity：Yun Xu , Wei Wang
Interface design: Yanfen Zhang
Interaction design：Xinjie Li
Video Copyright：Shaoxiong Bai
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