This calculation analyses how much variation from the average or mean exists. A low standard deviation indicates that the data points are closer to the mean versus a high standard deviation indicates that the data points are spread out over a large range of values. The square root of the Variance is equal to the standard deviation.
The following equations are used, where xi from i=1 to N represents the sensor values, from the current value and back N periods.
where
Parameter | Description |
---|---|
Input Data* | Defines the time series data fed into the function. This can be a sensor ID or another function. |
Period | Number of data intervals considered in the function |
*Input data is optional in most cases. If Info360 detects that the first input is time series data, it will be applied to the function. Otherwise, the current active sensor's data will be used, which is often the case in Reference Charts.
Example Usage as an Expression:
StdDev(12) - Outputs the Standard Deviation of the previous 12 data points for the current data stream.
StdDev(Sensor('Tank_A'),24) - Outputs the Standard Deviation of the previous 24 data points for the Tank_A sensor.
Examples Reference Chart:
The following example shows measured discharge pressures along with a smoother WMA(12) function, and their respective measures of deviation.
The second panel shows Standard Deviation for the two data streams over the previous 12 points. Obviously, the WMA signal has smaller StdDev values. Large steps in pressure from pumps turning on and off result in spikes in StdDev.
The Variance below shows the same trends as StdDev, since StdDev is the square root of variance. Variance has a greater contrast between spikes and normal noise; for this reason it is more commonly used for deviation alert thresholds rather than StdDev.
For information on setting up custom equations and syntax, please refer to Analytical Functions.