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Examination Of Corrosion Sensor Data From Long-Term Field Exposures

Examination of Corrosion Sensor Data from Long-Term Field Exposures
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The current approach to corrosion severity prediction is to use long-term averages of environmental parameters (such as relative humidity, temperature, and pollutants), geographic features (such as coastal proximity), and witness coupon corrosion rates of indicator materials to classify an environment into one of a small number of severity categories. However, recent work has revealed that brief changes in environmental conditions—even those lasting only a few hours—can significantly affect total corrosion damage, and long-term averages of environmental conditions are not sufficient to accurately predict cumulative corrosion damage. To more accurately measure the corrosion damage from these short-term events, corrosion sensors are becoming increasingly popular.  The frequent acquisition of data and increased measurement sensitivity are attractive features, however the data from these corrosion sensors is still difficult to interpret in many cases.   

Product Number: 51322-18198-SG
Author: Erica Macha, Andrew Keller, Shengyen Li, James Dante
Publication Date: 2022
Industry: Corrosion Monitoring and Control
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$20.00
$20.00

This work examines data from a multi-year, large deployment of corrosion sensors in a variety of locations representing a range of corrosivity severities.  The corrosion sensors used in this work measure atmospheric parameters such as relative humidity and temperature in addition to electrochemical parameters such as the conductivity of the electrolyte formed by salt deposition and possible deliquescence, and the polarization resistance measured on a variety of structural materials including 7075 aluminum, 2024 aluminum, and 4310 steel.  Comparisons between sheltered and unsheltered sensor exposures show the difference precipitation and other rinsing events have on sensor measurements.  In particular, the interplay of relative humidity and electrolyte conductivity is discussed in the context of using conductance sensors as a proxy for salt loading.  The tendency for protective corrosion product films to form on the sensing materials, resulting in high initial corrosion rates and subsequent lower corrosion rates is demonstrated and the implications for accurately measuring corrosion rates is discussed.  A comparison between representative conductance values from the field to values from lab testing is presented to provide context for salt loadings in accelerated laboratory corrosion testing.  These discussions can be used to guide the development of models used to predict atmospheric corrosion rates from sensor data.   

This work examines data from a multi-year, large deployment of corrosion sensors in a variety of locations representing a range of corrosivity severities.  The corrosion sensors used in this work measure atmospheric parameters such as relative humidity and temperature in addition to electrochemical parameters such as the conductivity of the electrolyte formed by salt deposition and possible deliquescence, and the polarization resistance measured on a variety of structural materials including 7075 aluminum, 2024 aluminum, and 4310 steel.  Comparisons between sheltered and unsheltered sensor exposures show the difference precipitation and other rinsing events have on sensor measurements.  In particular, the interplay of relative humidity and electrolyte conductivity is discussed in the context of using conductance sensors as a proxy for salt loading.  The tendency for protective corrosion product films to form on the sensing materials, resulting in high initial corrosion rates and subsequent lower corrosion rates is demonstrated and the implications for accurately measuring corrosion rates is discussed.  A comparison between representative conductance values from the field to values from lab testing is presented to provide context for salt loadings in accelerated laboratory corrosion testing.  These discussions can be used to guide the development of models used to predict atmospheric corrosion rates from sensor data.   

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