TY - GEN
T1 - Green Condition based Maintenance - An integrated system approach for health assessment and energy optimization of manufacturing machines
AU - Johansson, Carl Anders
AU - Galar, Diego
AU - Villarejo, Roberto
AU - Monnin, Maxime
PY - 2013
Y1 - 2013
N2 - 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 can have 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. Monitoring and profiling the electric current consumption in combination with operational data is an easy to implement Green Condition based Maintenance (Green CBM) technique 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 also opens for new business models for maintenance and service providers. The described Green CBM technique can be applied in many types of machines. In machine tools, focusing on spindles and linear guides, as responsible for the most common and cost-intensive downtimes.
AB - 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 can have 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. Monitoring and profiling the electric current consumption in combination with operational data is an easy to implement Green Condition based Maintenance (Green CBM) technique 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 also opens for new business models for maintenance and service providers. The described Green CBM technique can be applied in many types of machines. In machine tools, focusing on spindles and linear guides, as responsible for the most common and cost-intensive downtimes.
UR - https://www.scopus.com/pages/publications/84905816926
M3 - Conference contribution
AN - SCOPUS:84905816926
SN - 9781629939926
T3 - 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013
SP - 1069
EP - 1084
BT - 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013
PB - British Institute of Non-Destructive Testing
T2 - 10th International Conference on Condition Monitoring, CM 2013 and Machinery Failure Prevention Technologies 2013, MFPT 2013
Y2 - 18 June 2013 through 20 June 2013
ER -