Multi-property Anomaly Alerts
Hi all,
I have set up a series of anomaly alerts on a demonstration pump rig. The demo rig has flow rate, pressure and temperature sensors, with anomaly alerts set up for each sensor. For the demonstration, the presenter can turn on the pump which then operates for 30 seconds before being automatically switched off. To train the anomaly detection I simply recreated a series of 30 second pump cycles. With the timing between each cycle being random.
The alerts were all trained with the following parameters:
| Outbound Anomaly Rate | Min. Data Collection Time | Certainty |
| 5 seconds | 5 minutes | 95.4 % |
The goal of the demonstration is to have one of the valves in the rig be closed to represent an unwanted blockage in the pipe. This should then trigger one of the anomaly alerts.
After successfully training the anomaly alerts, I find that the pressure anomaly alert triggers as soon as the pump cycle starts. Which, from the expected use case is not anomalous. I have attached a picture of what I think the problem is.
From my understanding; the underlying model is doing a step-ahead prediction using a neural network for a series of sliding windows. For windows 1 to 4 the current pressure states of the system are the same. The step ahead prediction for these windows are relatively easy to predict since it's relatively well-behaved. However, for window 5 the pressure state of the system is the same as windows 1 to 4 but the step ahead prediction is significantly different. One way of overcoming this is to include the current input state of the system as a variable for the step ahead prediction since this would help distinguish window 5 from windows 1 to 4.
What is the best way of implementing multi-property anomaly detection in Analytics since it is not offered out of the box?
Regards,
Brad