POWER-OM Power consumption driven Reliability, Operation and Maintenance optimisation

Summary

The normal strategy to keep production systems in good conditions is to apply preventive maintenance practices, with a supportive workforce reactive in the case of clearly detected malfunctions.

This impact on quality, cost and in general, productivity. Added to this, the uncertainty of machine reliability at any given time, also impacts on product/production delivery times. It is known also that a worn-out mechanism has higher energy consumption. The use of intelligent predictive technologies could contribute to improve the situation, but these techniques are not widely used in the production environment. Often sensors and monitors required for the production environment are non standard and require costly implementations.

Power-OM propose to use the electric current consumption monitoring and profiling, as an easy to implement condition based maintenance (CbM) technique, and manage it also as a way to improve the overall business effectiveness, under a triple perspective:

Optimizing maintenance strategies based on the prediction of potential failures and schedule maintenance operations in convenient periods and avoid unexpected breakdowns
Operation: Managing energy as a production resource and reduce its consumption
Product reliability: Providing the machine tool builder with real data about the behaviour of the product and their critical components

This universal solution should also be compatible with the added value information that could come from existing sources (control) and sensors used at the machine, and jointly this will preserve current and future investment in the field. The project will research also in the required infrastructure and new business model for maintenance services provider.

Power-OM will be applied in machine tools, focussing on spindles and linear guides as responsible for the most common and cost-intensive downtimes. However the technology developed in this sense would have high potentials across other types of machines.

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Web resources:	https://cordis.europa.eu/project/id/314548
Start date:	01-08-2012
End date:	31-07-2015
Total budget - Public funding:	3 788 409,00 Euro - 2 651 294,00 Euro

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Original description

The normal strategy to keep production systems in good conditions is to apply preventive maintenance practices, with a supportive workforce “reactive” in the case of clearly detected malfunctions. This impact on quality, cost and in general, productivity. Added to this, the uncertainty of machine reliability at any given time, also impacts on product/production delivery times. It is known also that a worn-out mechanism has higher energy consumption.
The use of intelligent predictive technologies could contribute to improve the situation, but these techniques are not widely used in the production environment. Often sensors and monitors required for the production environment are non standard and require costly implementations.
Power-OM propose to use the electric current consumption monitoring and profiling, as an easy to implement condition based maintenance (CbM) technique, and manage it also as a way to improve the overall business effectiveness, under a triple perspective:
• Optimizing maintenance strategies based on the prediction of potential failures and schedule maintenance operations in convenient periods and avoid unexpected breakdowns
• Operation: Managing energy as a production resource and reduce its consumption
• Product reliability: Providing the machine tool builder with real data about the behaviour of the product and their critical components
This universal solution should also be compatible with the added value information that could come from existing sources (control) and sensors used at the machine, and jointly this will preserve current and future investment in the field. The project will research also in the required infrastructure and new business model for maintenance services provider.
Power-OM will be applied in machine tools, focussing on spindles and linear guides as responsible for the most common and cost-intensive downtimes. However the technology developed in this sense would have high potentials across other types of machines.