Compression filtering

The compression filtering, called the

swinging door compression

, is used by the snapshot subsystem to determine which events should be archived. The idea behind compression filtering is to store just enough data to accurately reproduce the original signal.

The compression method used by

FactoryTalk Historian ME

allows it to keep more data online than conventional scanned systems. The data is also much more detailed than in an archiving system based on averages or periodic samples.

The following figure shows events that fall on the same straight line. In this case, in order to recreate any point along the line, it is enough to archive just two values - the first and the last one (A and B). The other point values can be interpolated from the values that have been stored.

In practice, the subsequent changes in the point values form curves rather than straight lines, so the compression specifications for each point must be properly configured to achieve a balance between storage efficiency and accuracy.

FactoryTalk Historian ME

uses a compression algorithm to determine which events should be archived in order to provide an accurate data history. The compression attributes allow you to control the granularity of the compression algorithm.

During the compression process, when a new snapshot arrives, the previous one is evaluated according to the compression specifications to verify whether it is a significant event. If it is, the event is sent to the event queue. Otherwise, it is discarded.

An event is sent to the event queue disregarding the compression process, provided that:

The swinging door compression discards values that fall on a line connecting the values that are recorded in the archive. When a new value is received by the snapshot subsystem, the previous value is recorded only if any of the values since the last recorded value do not fall within the compression deviation blanket. The deviation blanket is a parallelogram extending between the last recorded value and the new value with a width equal to twice the compression deviation specification.

Each point has three attributes that comprise the compression specifications:

The compression specifications work similarly to the exception specifications. Just like the exception reporting, the compression is a filter. While the exception specifications determine which events are sent to

FactoryTalk Historian ME

, the compression specifications determine which of the events sent to

FactoryTalk Historian ME

should go archived.

The

Compression Min

and

Compression Max

attributes are limits that refer to the time between events in the archive. A new event is always recorded if the time since the last recorded event is greater than or equal to the compression maximum time.

You can adjust the compression parameters to maintain significant data and keep a reasonable archive space at the same time. The compression maximum time is usually set to one value for all points in the system. It should be large enough that a point that does not change at all uses very little archive space. The compression maximum time of one work shift (for example, 8 hours) is often a good choice.

Use the

Compression Min

attribute with points that produce frequent value changes so as to ensure that they do not occupy too much archive space. Set the attribute value to 0 for any point coming from the data collection that does the exception reporting. For a data acquisition system with a slow scan time, this attribute is not important. There are a few cases where you want to use non-zero compression minimum time.

For the

Compression Deviation

attribute, a reasonable starting point is one or two percent of the span for transmitters and 0.5 to 1.0 degrees for thermocouples. Look at trend displays to find points for which the reproduction of the data is not acceptable. The goal is to filter out instrument and process noise and still record significant process changes. The effect of changing the compression deviation is not predictable.

For digital points, any change is a significant change. That is why only the values of the compression maximum and minimum attributes are taken into account, while the compression deviation value is disregarded.