The M.J. MAILLIS steel industry use case deals with a cold rolling mill for the production of steel strapping . The demand for changing over the milling rollers comes either from their regular wear or unexpected damage, which can occur due to either defective raw material or an equipment malfunction. When this occurs, rolls are removed from the stand for grinding. This usually happens every eight hours for the work rolls and every week for the backup rolls. Replacement means production downtime which directly impacts costs. The current maintenance is performed frequently at predetermined intervals, which is based on generic performance data or previous experience. It is of utmost importance for MAILLIS to have their machine or a piece of equipment that can tell the machine current health status and the degree to which that status deviates from normal condition along with predictions about its future health state and actions recommendations.
Health monitoring in the context of MAILLIS use case involves inspection of the state of various components that constitute the milling station. As the physical conditions inside the milling station are extreme, health monitoring cannot be carried out by the traditional methods involving visual inspection during operation. Thus, in UPTIME, a set of appropriate and resistant sensors was set up in the sensor network that monitors the most important operational variables. The UPTIME platform will then acquire and process sensor data (UPTIME_SENSE), to provide information about the current health state of the machines (UPTIME_DETECT), predictions about future failures (UPTIME_PREDICT), criticality assessment (UPTIME_FMECA), proactive recommendations (UPTIME_DECIDE), and generate new maintenance plan. Besides that, an analysis of legacy and Overall Equipment Effectiveness (OEE) data is carried out by UPTIME_ANALYZE showing the prediction of operation interruptions from past behaviour.
It is expected that MAILLIS will have a machine that reports its current health status along with the appropriate data analytics. UPTIME system will allow predictions about the equipment‘s future health and provide recommendations for future actions. The ability for machines to perform self-assessment on which basis maintenance decision making can be optimized will allow MAILLIS to reduce its maintenance costs, improve their performance and affect positively the products’ entire life cycle.
| Country: | GR |
| Address: | KIFISSA |
- Not specified
Installation of sensor infrastructure: during the initial design to incorporate the new sensors into the existing infrastructure, it is necessary to take into consideration the extreme physical conditions present inside the milling station, which require special actions to avoid sensors being damaged or falling off. A flexible approach is adopted, which involves the combination of internal and external sensors to allow the sensor network prone to less failure. Quantity and quality of data: it is necessary to have a big amount of collected data for the training of algorithms. Moreover, the integration of real-time analytics and batch data analytics is expected to provide a better insight into the ways the milling and support rollers work and behave under various circumstances.