Infineon > 实例探究 > 边缘计算如何实现阀门的预测性维护

边缘计算如何实现阀门的预测性维护

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 How Edge Computing Enables Predictive Maintenance of Valves - IoT ONE Case Study
技术
  • 分析与建模 - 边缘分析
  • 分析与建模 - 机器学习
  • 平台即服务 (PaaS) - 边缘计算平台
  • 处理器与边缘智能 - 嵌入式和边缘计算机
适用行业
  • 半导体
适用功能
  • 离散制造
用例
  • 边缘计算与边缘智能
服务
  • 系统集成
客户
关于客户

一家跨国半导体合同制造和设计公司,在开曼群岛注册成立,总部位于纽约马耳他。

挑战

用于监控生产关键型超纯水阀的解决方案,旨在及早发现可能的故障并更好地规划维护流程。

解决方案

边缘人工智能传感器平台。特征:

  • 多核:9 个 RISC-V 集群内核
  • 4MB RAM 和 1MB MRAM
  • 丰富的数字和模拟接口
  • CAN-FD核心
  • 以太网-TSN 核心
  • 高安全性:TRNG、OTP、AES、安全启动……
  • 各种支持的传感器
  • 可定制的封装外设
  • 软件 SDK 和示例

将人工智能嵌入智能传感器。

运营影响
  • [Data Management - Connectivity Stability]

    Application-independent, the solution prevents downtimes in production through AI-based early fault detection.

    Edge AI - benefits and advantages:

    • Increase performance by reducing latency
    • Large number of data points can be measured
    • Evaluation even without the internet
    • No data transfer between shopfloors, countries etc. needed
    • Companies can expand their computing capacity through a combination of IoT devices and edge data centers
  • [Efficiency Improvement - Maintenance]

    Thanks to the scalable edge computing solution, incipient defects are now detected at an early stage. Maintenance measures can be planned effectively. Monitoring of the valves is possible in real-time even if the WLAN connection is briefly interrupted and is visualized by means of a clear dashboard.

    • Maintenance processes can be planned in a demand-oriented and cost-efficient manner.
    • Data-based condition monitoring is possible in real-time.
    • Fail-safe operation of the production facilities.
    • Time & personnel expenditure in the maintenance process is reduced.

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