SOLUTIONS POWERED BY AI
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The Challenge.

  • Equipment maintenance is usually done after a specific number of hours or cycles of operation.
  • However, this is often based on an average and not on sensor data of operational parameters.
  • Sometimes operational parameters lead to unplanned downtime of equipment in a production line, which can lead to major costs.
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The Solution.

  • Designed and built a Machine Learning model to predict equipment time to failure.
  • Optimized maintenance scheduling by minimizing the planned downtime.
  • Analyzing the importance of model features determined which factors have the highest impact on equipment time to failure.
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The Results.

  • Minimized planned downtime due to optimized maintenance scheduling.
  • Reduced unplanned downtime of equipment and production lines.
  • Increased equipment lifetime by improving operational conditions based on understanding factors impacting equipment time to failure.
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