The Standard For BAS Trending Data Collection And Compilation
Quickly and accurately bridging the
gap between raw trend logs
analysis tools that consume trending data
What Is 'The Gap' Being Bridged?
The trending data saved in building automation system (BAS) trend logs are
used to determine whether HVAC equipment is functioning as
intended, and to evaluate opportunities for energy conservation and cost
savings. Trend logs are often the basis upon which new equipment installations
are certified. Unfortunately, neither the data format nor the file architecture
of trend logs are yet standardized across control vendors.
Before trend data becomes useful for operational evaluation and control as
well as fault detection and diagnostics (FDD), the trend date, time and
point value must be extracted from each trend recording, regardless
of data format or file architecture, and normalized in a spreadsheet to a date and time scale.
The normalization process may require correction of data anomalies as well
as the application of various smoothing and change of value techniques.
The lack of standardization in recording trend logs makes the
collection and compilation of accurate trending data spreadsheets a time
consuming, yet necessary challenge requiring costly engineering skills.
Therein lies 'the gap' ... the manual extraction, normalization, correction
and compilation of BAS trending data. This manual gap in otherwise computerized
and intelligent trending processes is:
- Exceptionally slow,
- Labor intensive,
- A drain on expensive engineering talent, and
- Is guaranteed to introduce errors into the trending data.
Trend Importer bridges this technology gap in the processing of trending data
by offering a quick, cost effective, accurate and easy to use application that
replaces and standardizes manual collection and compilation methods
Collect Trending Data From Any Source
- Vertical files (trend logs recorded one after the other in one file)
- Delimited text files
- Fixed column width text files
- SQL databases
- MySQL databases
- Access databases (both .mdb and .accdb)
- dBase files
- Johnson Controls dBase files
Build Custom Matrices From Any Collected Trending Data Regardless Of Source
- Set the matrix beginning and ending dates and times which do not have to
agree with the start/stop dates and times in the collected trend logs
- Set the sampling interval which does not have to be the same as the intervals in
the collected trend data
- Add and arrange trending data columns in any quantity and sequence with any column name
- Apply "Change Of Value" and "Smoothing" techniques to selected columns
Export Custom Matrices To One, Two Or All Available Analysis Tools
- As spreadsheet files ready to open in Excel
- In formatted files ready to upload to PACRAT
- As UTF files ready to drag and drop into Universal Translator (much
faster than importing with Universal Translator)
Simple To Use
No special training is needed to use Trend Importer. If a person
understands Windows Explorer, they are well on their way to
understanding how to use Trend Importer.
Saves Expensive Engineering Time
Using Trend Importer, trend logs can be collected and prepared
for input to available trend analysis tools in minutes instead of
hours. The saved overhead either flows straight to the bottom
line and/or contributes to more competitive contract bids.
More Accurate Analysis And FDD
With Trend Importer, exported trend matrices are free of the
errors inherent in the process of manually building spreadsheets
from raw trending data. Any analysis based on a matrix created with Trend
Importer can be relied upon to be accurate. In addition,
fault detection and diagnostics (FDD) processes are cleaner with
fewer, if any, faults that are triggered by inaccurately compiled trending data.
Adaptable To Future Needs
As new raw trend log sources are identified in the field, Trend Importer is
designed such that new collection technology can be easily
incorporated into future versions. Likewise on the export side,
adapting Trend Importer to provide immediately consumable trend
data products to new analysis tools can be accomplished quickly.