Microchip Technology > Case Studies > Predictive Maintenance for Industry 4.0

Predictive Maintenance for Industry 4.0

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 Predictive Maintenance for Industry 4.0 - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
  • System Integration
The Challenge

Industrial robotic systems see increasing levels of vibration on their mechanical elements which often indicates the need for service. Factory operations personnel need to find a way to increase their awareness of vibration and use AI/ML analytics to interpret the data and address the issue in a timely manner. This is a fairly standard interpretation of predictive maintenance that many Industry 4.0 advocates aspire to.

In the past, service schedules were accommodated using a paper log file, and the intervals between were often decided arbitrarily by a supervisor based on their experience and feel for the operation. As robotic systems in modern manufacturing become more complex, factory managers need to be aware of their maintenance requirements in real-time and be on top of both routine and critical service scheduling in order to avoid an interruption in service.

The Solution

This hyper-aware status and the related collection of data is paramount to a smart factory’s evolution and is best analyzed at the edge.

With Microchip's Total Systems Solution (TSS) approach, Microchip offers a fully integrated, state-of-the-art technology solution for Linux applications, enabling the customers to efficiently design next-gen solutions in a smart, connected and secure manner. This application of IoT technology is ideal for those managing a sensor network while controlling a robot and reporting data through a Human Machine Interface (HMI).

Operational Impact
  • [Process Optimization - Predictive Maintenance]

    Predictive maintenance reduces downtime and repair costs while also extending equipment life and ensuring output quality.

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