设计师 4Paradigm UED 的作品 Smart Wind Turbine Prediction 预测性维护界面 荣获了极具声望的 2023 A' Design Award 银奖，竞赛类别为界面、交互与用户体验设计。
银界面· 2023
Smart Wind Turbine Prediction 预测性维护界面
设计师： 4Paradigm UED
客户: Jianxin Technology Co., Ltd
界面类别A' Design Award 银奖获奖者
53,044
已验证的获奖者
验证 #141912 的获奖状态设计作品
关于 Smart Wind Turbine Prediction 预测性维护界面
智能风机预测是一款面向风电场智能运维的软件界面。该界面运用三维可视化、虚拟仿真与数据图表展现风机运行状态,并可快速定位故障。通过拖拽数据与模型配置项即可完成机器学习模型的搭建,从而适配单一或多种机型。故障预警平均可提前约二十分钟发出,模型准确率达百分之八十一,有效降低了人工运维成本与安全风险。
荣誉认可
关于A' Design Award 银奖
Smart Wind Turbine Prediction 预测性维护界面是 A' Design Award 银奖得主，这是授予顶级、华美而令人印象深刻的设计的极为崇高的认可。银奖得主是极其卓越、极具创造力的作品，展现出其设计师杰出的专业技能、才华与敏锐。预计Smart Wind Turbine Prediction 预测性维护界面将成为一件出色的典范之作，兼具非凡的专业性与深刻的创新性。这件赢得 A' Design Award 银奖的设计是一项备受瞩目、令人钦佩的发明，造福并拓展艺术、科学、设计和技术的边界。Smart Wind Turbine Prediction 预测性维护界面是一件令人羡慕的创作，它唤起渴望，带来积极的情感，并激发惊叹与赞叹。
设计幕后
媒体资料包
由设计师提供的致谢、团队成员及技术详情。
- 独特属性
- 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.
- 尺寸与包装
- Width 1920 Height 1080
- 标签
- AI System Design , AI , AI Applications , Machine Learning , Modeling , Big Data
- 生产技术
- 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%.
- 设计挑战
- 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.
- 灵感
- 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.
- 项目周期
- 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.
- 团队成员
- Design Director：Yun Xu
Design Manager：Wei Wang
Designer：Shaoxiong Bai
Designer：Xinjie Li
Designer：Yanfen Zhang - 图片版权
- Design creativity：Yun Xu , Wei Wang
Interface design: Yanfen Zhang
Interaction design：Xinjie Li
Video Copyright：Shaoxiong Bai
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