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.
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.
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.