1. A toolkit for filtering and data processing in WinCC OA
Oliver Holme

2. Filtering needs Current filtering possibilities
Simple procedures provided by WinCC OA
Smoothing (time, deadband, old/new comparison)
Available only in drivers and archiver
Custom code with dpConnect() of timedFunc()
More filtering can be useful sometimes
Smoothing noisy data, reducing update rate
Avoid further load downstream (FSM, dpConnects, alarms)
Beyond filtering - analysis and characterisation of a signal
e.g. RMS calculation or value change detection/counting
Oliver Holme
10/06/2014

3. Toolkit concept Avoid duplicated implementations doing similar things
Eliminate the need to write any filtering code for most cases
Only configuration required (setting up a config DP)
Write and test filters once, then use many times
Hopefully reduce errors
Provide facilities to extend the set of filters if required
Central place for data processing – fewer obscure CTRL scripts
Oliver Holme
10/06/2014

4. Default filter set Provides a standard set of configurable filters
Spike filter(min_spike_size, spike_length)
Low pass filter(time_constant)
Mean(window_length) – sliding window
Median(window_length) – sliding window
Kalman filter(q, r)
Also some examples of analysis and rate reduction
RMS(window_length)
Sample and hold() – to sample a data stream
Value change detection() – can act as a simple watchdog
Limitations:
Single input and single output DPE
But either or both can be dyn_... types
Oliver Holme
10/06/2014

5. Chains can contain any number of filters
How it works
Defining a “filter chain”:
Filter definitions are saved into one or more filter config DPs
A CTRL manager runs a script to perform the filtering
One manager can handle one (-filter_dp_config <DP>) or all config DPs
Load can be shared across many managers
Input DPE
Filter 1
Filter N
Output DPE
Choose polling
or “on change”
Chains can contain any number of filters
Oliver Holme
10/06/2014

6. What exists now The filtering script
The default filter set (See slide 4)
Functions to configure the config DPs:
CMS_ECALfw_Filtering_createFilterDetailString()
CMS_ECALfw_Filtering_createFilterChainObject()
CMS_ECALfw_Filtering_saveFilterChain()
CMS_ECALfw_Filtering_removeFilterChain()
Visualisation panel
Shows configuration (no editing)
Live input/output view with plot
Oliver Holme
10/06/2014

7. Oliver Holme
10/06/2014

8. Adding new filters First three arguments must be:
key – a unique identifier of “this” step in a filter chain
value – the new value of the process variable
timestamp – the timestamp that the value was read*
Additional arguments for filter configuration
Persistent information can be saved for next iteration:
CMS_ECALfw_Filtering_setVariable(key, variable, value)
CMS_ECALfw_Filtering_getVariable(key, variable, value)
Persistent value is of type mixed
Return value is the output of the filter step (type: mixed)
To return “nothing”, return CMS_ECALfw_Filtering_NONE
* time of polling for polled values or DPE timestamp in case of “on change”
Oliver Holme
10/06/2014

9. Example – Sliding Mean Get previous state Manipulate Save state
mixed CMS_ECALfw_Filtering_SlidingMean( string key, mixed value, time timestamp, int window, bool outputAtStartup = false) { mixed output = CMS_ECALfw_Filtering_NONE; //load previous sliding window contents dyn_mixed buffer; CMS_ECALfw_Filtering_getVariable(key, "buffer", buffer); //append latest value and remove the first dynAppend(buffer, value); if(dynlen(buffer) > window) dynRemove(buffer, 1); //save the sliding window contents for next time CMS_ECALfw_Filtering_setVariable(key, "buffer", buffer); //determine and return mean value if(outputAtStartup || (dynlen(buffer) == window)) output = dynAvg((dyn_float)buffer); return output; }
Get previous state
Manipulate
Save state
Generate output
Oliver Holme
10/06/2014

10. Average CPU thread load Average CPU thread load
Performance
Simple filter chain
1 filter, on change, 1Hz input values
Sliding mean (window = 5 samples)
Complex filter chain
3 filters, on change, 1Hz input values Spike filter -> low pass -> RMS (win = 5)
Filter chains
Peak RAM consumption
Average CPU thread load
500
55 MB
22 %
1000
66 MB
44 %
1500
78 MB
67 %
2000
88 MB
75 %
2500
100 MB
87 %
3000
110 MB
93 %
Filter chains
Peak RAM consumption
Average CPU thread load
500
56 MB
52 %
1000
69 MB
80 %
1500
83 MB
95 %
2000 -
2500
3000
Test computer
DELL Blade PowerEdge M610 with Windows 7 SP1 and WinCC OA 3.11 SP1
Dual Intel Xeon X GHz (6 cores / 12 threads per CPU)
Oliver Holme
10/06/2014

11. Current Status Used in production in CMS ECAL DCS
Median and value change detection (watchdog)
Studies ongoing for further applications
Spike filter and mean
No further development anticipated at the moment
Any interest?
Oliver Holme
10/06/2014