Alarm Management: Enables Fast Response to Critical Plant Conditions
In the production and manufacturing environment, like everywhere else, nothing always goes to plan. The more important is it to identify critical plant conditions fast, and react to them.
The VisiWinNET alarm system supports you with this by detecting machine and plant conditions, and clearly displaying them on the screen as errors, warnings or reports–that enables fast response. The optional recording of events then allows subsequent analysis.
THE MOST IMPORTANT FUNCTIONS IN KEYWORDS
- Alarm classes
- Acknowledgement performance
- Display, colors
- History
- Alarm groups
- Logical grouping
- Filtering in the user interface
- Accumulative acknowledgement, deactivation…
- Alarm types
- Pre-defined types (digital)
- Self-defined types (status word, bitblock)
- History with comfortable file administration
Organization
The VisiWin alarm system organizes the alarms in classes and groups.
An alarm class determines how the alarm is to perform, and how it is displayed in the user interface. That performance contains for example settings for acknowledgements and storage in the history. A typical arrangement would be alarm, report and information. This can be freely determined and expanded.
Groups allow the arrangement into functional groups with the machine. Groups such as material intake or maybe packaging come to mind. This can be freely defined, too.
The display elements of the user interface can filter by groups and classes. But here, too, there are more setting options.
History
Of course the machine operator has to be in a position to establish when and where an error has occurred in order to improve machine maintenance or staff training. Accountability, too, is becoming important in ever more areas.
For this purpose VisiWin stores the alarms, and retrieves them from the archive for editing or just display if required. As mass storage, in spite of terabyte hard disks, is still restricted in volume VisiWin removes old data if required, freeing space for new data.