Predictive Analytics to Reduce Unplanned Downtime | ONETech
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Scheduled Downtime on Assembly Lines and the Power of Predictive Maintenance

Scheduled Downtime on Assembly Lines and the Power of Predictive Maintenance

04 Jun 20

Predictive Maintenance

Scheduled Downtime on Assembly Lines and the Power of Predictive Maintenance

The Industry Challenge

From its inception until current day the goal of the assembly line has been to increase manufacturing efficiency. As much as assembly lines have improved, one thing can still stop them and halt production: machine breakdowns. This has led the manufacturing industry to employ scheduled downtime for maintenance on equipment that is showing signs of wear and tear or on equipment that is near service dates to try and prevent breakdowns. This solution, while effective for many cases, will fail to catch machines whose breakdowns appear to be more sudden. Another unintended side-effect of this approach is that unnecessary maintenance will be performed on healthy equipment that is near or at its service date.

The Solution

ONE Tech has developed MicroAI™, an artificial intelligence (AI) based system that accepts machine performance data as an input. MicroAI™ then examines the performance data to detect abnormalities that may be difficult to spot with the human eye. From there, the AI can calculate both a health score as well as the days to next maintenance for the device. Manufactures can make more intelligent maintenance decisions by doing the following:

  • Host MicroAI™ on any Windows or Linux based server.
  • Select appropriate sensor measurement for each piece of equipment that adequately track its performance.
  • Train MicroAI™ with normal measurements for each piece of equipment.
  • Use MicroAI™ to calculate health scores and days to next maintenance.
  • Schedule maintenance in accordance with the calculated metrics.

 

The Impact

ONE Tech’s MicroAI™ solution enables manufacturers to make more intelligent maintenance decisions. This real-time, data-driven technology’s positive impacts include the following:

  • Reduce on the job machine breakdowns by identifying abnormally behaving machines and scheduling maintenance accordingly.
  • Postpone maintenance on machines who are at the service date but are still performing normally.
  • Alert users of rapid changes in machine health that could point to an impending breakdown.

Provide greater insight into assembly line health to enable intelligent decisions that will increase overall assembly line efficiency.