In companies that are already working with IIoT platforms, the ambition is to expand data analytics and use it beyond the basics. There is a growing awareness to uncover weaknesses in production, and to create use cases so that operations can be improved and plant outputs can be increased.
The blow station use case demonstrates how a hands-on mentality in the use of data analytics in maintenance management leads to improvements in spare parts maintenance. The maintenance manager in a beverage bottling plant has responsibility for all machines at a site. When it comes to the maintenance of relevant spare parts, expensive plant downtimes occur time and again due to broken spare parts.
Initial situation
Using the blowing station of a PET blow molder as an example, the maintenance manager has to visually inspect all valves in the production hall (diagnostic visit). He decides whether the valves are to be overhauled or replaced. With this method, irregular production stoppages occur time and again. For this reason, the Maintenance Manager has recorded all the valves in the blowing station in a dashboard developed by Quantis in order to analyze the wear on the valves.
What was done
For the visual representation, the data of the blowing station with the associated valves are displayed in a dashboard. For the blowing station, the blowing phases and other parameters are used as the basis for the data. Any anomalies that occur or results that deviate from the normal pattern behavior of the valves are analyzed and trigger an alarm message. The application notifies the user by e-mail or SMS that valve deterioration has occurred. The Maintenance Manager at the blow molder can then replace the valve in question.
Results
Data analysis via dashboard takes place once a week at the customer's site, thus eliminating the need for on-site visual inspection. Dashboards as well as automated notifications are modern working tools that a maintenance manager needs for his daily work. With their use, there is less machine downtime, as notification in case of deterioration of spare parts makes timely intervention possible. The replacement of spare parts becomes more predictable based on the data analysis.
Conclusion
no visual inspection on site necessary
automated notification in case of deterioration of blowing station
less uncoordinated machine downtime
longer operating hours of spare parts than specified by the OEM
spare parts inventory, spare parts procurement and their replacement becomes more predictable
Picture by Unsplash Luke Chesser