For example if the furnace reading drops below a defined temperature Sabisu will add a note to Widget Working View, sending you a Notification.
The Event will also be highlighted on the chart for its duration, where previously only the start of the Event would appear.
Users can then reply to these events with other Notes, create Actions to investigate or mitigate the outcomes, or upload related documents, images and so on.
What’s next for Events?
Machine learning can be used to detect when an identified Event is about to occur (an ‘incipient event’) or has just started to occur (a ‘triggering event’) so that you can take early or pre-emptive action.
After marking the Event, Sabisu will ask whether it should find similar occurrences of this behaviour.
Sabisu will then identify every single similar event in the date range you choose. Machine learning ensures that the match is good. For example, if a compressor packing ring failure has been identified, Sabisu will find and mark out all other failures.
Each of these matches is marked as a new Event and listed alongside the data so you can quickly move between them, looking for similarities or root causes.
All the usual collaboration capabilities are in play; highlight similarities in Notes, allocate an Action to investigate or analyse further, upload files and so on.
An obvious application of this is in the case of asset failures where early warning can be invaluable in reducing downtime and optimising maintenance.
We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.