Ideas and concepts for diagnosis of performance and evaluation of data reliability based upon ARSA state-of-health (SOH) data Page: 4 of 11
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PNNL-13179 Rev 1
Ideas & Concepts for Diagnosis of Performance and Evaluation of Data
Reliability based upon ARSA State-of-Health (SOH) data
KH Abel, TW Bowyer, JC Hayes, TR Heimbigner, ME Panisko, JI McIntyre, RC Thompson
March 1, 2000
At the current time, the Pacific Northwest National Laboratory (PNNL) prototype for the
Automated Radioxenon Sampler/Analyzer (ARSA) automatically transmits, on a daily basis, a
subset of all state-of-health (SOH) data in an e-mail data file to a limited number of recipients.
These variables represent what were considered the most critical physical parameters for the
ARSA's operation at the beginning of the field demonstration in Freiburg, Germany. Operators at
PNNL perform a daily review of the information in the data file for anomalous operational
conditions as evidenced by sensor readings. The initial review is easily implemented by plotting
the various sensor data versus time and looking for gross deviations in the periodicity of the
variables compared to previous sample sensor data. After viewing the 24-hr graphical plots, if
necessary, a review is conducted of the tabular data of specific sensor anomalies. In most cases,
our experience has been that when there is an ARSA operational problem the data file will have
multiple sensor readings that reflect some aspect of the problem.
For example, there have been a series of short intervals over the period mid-January to mid-
February, 2000, where the flow through the air collection mass-flow controller (MFC-1) drops
below the nominal 100 liters/minute. If the decrease in flow is maintained over more than a few
minutes, there is normally an impact on other components of the ARSA. Specifically, the air
chiller output temperature will increase, as will the post-collection radon trap temperature (when
in cooling mode). Also, the temperature of the final charcoal trap will increase, if it is in a cooling
mode, and after several minutes temperatures in the pre-radon trap and main charcoal trap that
are currently in the sampling mode will increase. Additionally, if there is a period of zero flow due
to valve closure (from power supply failures) and the time interval is long enough (approximately
5 minutes or longer), the pressure in the main charcoal trap in sampling mode will increase due to
warming. Such an event is easily determined from visual inspection of the SOH variables.
Attached are graphical plots, Figures 1 through 4, of the data file that was e-mailed January 26,
2000 where a sizeable "flow anomaly" occurred at more than one period during the day,
illustrating the points discussed. These anomalies were readily noted in a cursory data review
process using graphical plots generated within Microsoft! Excel. Figure 1 contains mass flow
sensor data and clearly shows two intervals where MFC-1 flow dropped from normal flow of 100
liters per minute to near zero. The other two mass flow controllers, with flow rates of 200 to 400
cc/min, follow typical cyclical behavior observed in previous and more recent data files over the
24-hour data file time period.
Figure 2 displays four temperatures, including the chiller output temperature (ts-5), the final
charcoal trap (ts-21), and the two post-radon traps (ts-17 & ts-19). The flow anomaly clearly
impacts the chiller output temperature during both flow anomalies, as evidenced by the increase
in temperature from near -100"C to approximately -50 C in the second instance of flow
disruption. But there are no apparent affects upon the other sensor temperatures shown in this
graph, since neither of the two post radon traps nor the final charcoal trap were in cooling mode
at the time of flow disruption.
Figure 3 displays pressure versus time over the 24-hour length of the data file for the two main
charcoal traps and the pressure after the ascarite trap module (process two pressure in
operational nomenclature). There is an increase in internal pressure in the main charcoal trap
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Abel, KH; Bowyer, TW; Hayes, JC; Heimbigner, TR; Panisko, ME; McIntyre, JI et al. Ideas and concepts for diagnosis of performance and evaluation of data reliability based upon ARSA state-of-health (SOH) data, report, April 27, 2000; Richland, Washington. (https://digital.library.unt.edu/ark:/67531/metadc707638/m1/4/: accessed March 28, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.