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Conveyor system with multiple belts transporting items in an industrial setting.
REAL-WORLD APPLICATION

Automated fault prediction in conveyor systems

Take control of your automotive production lines with automated fault prediction. Detect anomalies in your conveyor systems and identify their root causes, so you can prevent bottlenecks, decrease downtime and save costs.

The image shows a conveyor system in an automotive factory.

Challenge

Unexpected downtimes of a single Electrified Monorail System (EMS) carrier can stop the entire automotive production line. Yet manufacturers have a limited ability to detect complex system fault patterns.

Solution

The answer to these challenges is condition monitoring equipped with anomaly detection for predictive maintenance. This solution automatically detects anomalies, identifies weak spots and offers data analysis capabilities.

Keep your conveyor systems running smoothly

Complexity in automotive manufacturing keeps increasing, from rising demand for individualized vehicles to growing costs for resources. Our automated fault prediction solution for conveyor systems simplifies your production line, helping you:

  • Identify weak spots, faults and root causes
  • Detect hidden bottlenecks
  • Automatically log events
  • Increase system availability
  • Shorten commissioning and ramp-up times
  • Save costs by reducing unplanned downtimes
A conveyor belt system in a factory with a worker operating it.
An icon showing a conveyor belt system with a green checkmark indicating machine availability.

Simplified dashboards

Easily view your data as prepared dashboards, heatmaps or top X analysis and benefit from automatic event logging.

An image of a conveyor system icon with a time-less to repair feature.

Condition monitoring

Automatically identify weak spots and detect anomalies in motor current and temperature, cycle time tracing and more.

An icon showing a conveyor belt system with less downtimes and improved efficiency.

Predictive maintenance

Use predictive maintenance tools to prevent unplanned downtime that affects your entire production line.

CASE STUDY

Global automotive manufacturer

Conveyor systems with robots handling car parts in a car factory.

Senseye Predictive Maintenance increases OEE

Results: This manufacturer expanded their predictive maintenance capability across their global production sites.

  • Tens of millions in saved downtime
  • Rapid return on investment of less than 3 months
  • Up to 6 months advance warning of machine failure
  • Reduction in preventive maintenance and secondary activities
  • Year-on-year OEE improvements

Have any questions?

Let’s chat. Reach out and we will help you figure out the best place to start.