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How Edge Computing Enables Predictive Maintenance of Valves

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 How Edge Computing Enables Predictive Maintenance of Valves - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Edge Analytics
  • Analytics & Modeling - Machine Learning
  • Platform as a Service (PaaS) - Edge Computing Platforms
  • Processors & Edge Intelligence - Embedded & Edge Computers
Applicable Industries
  • Semiconductors
Applicable Functions
  • Discrete Manufacturing
Use Cases
  • Edge Computing & Edge Intelligence
  • System Integration
The Customer


About The Customer

A multinational semiconductor contract manufacturing and design company incorporated in the Cayman Islands and headquartered in Malta, New York.

The Challenge

Solution approach for monitoring production-critical ultrapure water valves with the aim of detecting possible failures at an early stage and better planning maintenance processes.

The Solution

Edge AI sensor platform. Features:

  • Multicore: 9 RISC-V cluster cores
  • 4MB RAM & 1MB MRAM
  • Rich digital & analog interfaces
  • CAN-FD Core
  • Ethernet-TSN Core
  • High Security: TRNG, OTP, AES, Secure Boot…
  • Variety of supported Sensors
  • Customizable Package-on-Package Peripherals
  • Software SDK & Examples

Embedding AI in smart sensors.

Operational Impact
  • [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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