Summary
The experiment, “A Smart Predictive Maintenance Toolbox for drawing lines of car body elements SMPTcar”, carried on by AIN (Asociacion de la Industria Navarra), aimed at extending in the Predictive Maintenance of stamping presses for the car industry a condition monitoring system based on the measure of vibrations, currently in use on rotating machines, by introducing Artificial Intelligence techniques.
Condition monitoring of machinery and industrial installations by vibration measuring and analysis is widely used in the industry on machines in which the consequences, in case of failure, can lead to large production losses and repair costs. Historically it has been applied to rotating machines. However, its use in non-rotating machinery such as stamping presses, and in particular those used in the automobile industry, is very limited. On the one hand, the acquisition of data for its analysis is complex due to the impact that occurs in each stamping cycle. This causes the need to treat the vibration signals so that only the valid information related to the condition of the drive components (shafts, gears, bearings ...) remains. On the other hand, fault analysis and diagnosis, although technically feasible, is extremely laborious, making it practically unfeasible from an economic point of view due to the need for expert personnel in signal analysis and high-end dynamic signal analyzers. This fact acts as a barrier to the access of stamping companies to the advantages of Predictive Maintenance.
For these reasons, the possibility of introducing automated analysis and diagnosis techniques, using Artificial Intelligence, is of the utmost interest since
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It would improve the reliability and availability of stamping facilities by reducing downtime due to unforeseen breakdowns.
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It would reduce the analysis times of specialized personnel.
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It would reduce the costs of the Predictive Maintenance service for stamping companies, facilitating the access of SMEs to the advantages of failure prevention technologies
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It would improve the ability to failure anticipation by making possible a much faster analysis
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It would improve the competitiveness of the stamping sector by reducing production costs due to unforeseen failures
The experiment: A Smart Predictive Maintenance Toolbox for drawing lines of car body elements SMPTcar is an experiment in predictive maintenance, and consists of developing an AI-based system for measuring and analysing vibrations on metal sheet stamping presses in the automotive industry, in order to improve failure prediction and management of maintenance shutdowns.
Similar systems already exist for rotating machines, but in the case of presses, data acquisition is made much more difficult by the vibrations caused by the impact during stamping: these must be eliminated in order to have clean data referring to the elements actually being monitored (shafts, gears, bearings).
The experiment involves the introduction of AI-based analysis and diagnosis techniques, thereby improving the following aspects of maintenance: fault prediction, downtime reduction, reduction in staff deployment, overall efficiency increase, cost reduction.
The outcome of the experiment is a system that is able to autonomously anticipate failure and send a notification to maintainers.
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| Country: | ES |
| Address: | Carretera de Pamplona 1, Cordovilla 31191 |
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Comment:
Higher reliability and availability of stamping facilities by reducing downtime due to unforeseen breakdowns.
Reduced the analysis times of specialized personnel.
Reduce the costs of the Predictive Maintenance service for stamping companies, facilitating the access of SMEs to the advantages of failure prevention technologies
Improved ability to failure anticipation by being possible a much faster analysis,
Improve competitiveness of the stamping sector by reducing production costs due to unforeseen failures, as well as the Predictive Maintenance service provider
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Improved fault detection
Increased facility availability
Reduced maintenance service costs (for the customer)
Reduced analysis & diagnosis times (of the Maintenance Service provider)