![]() ![]() Corrosion Sensors to Reduce Aircraft Maintenance. Standard Practice for Conducting Potentiodynamic Polarization Resistance Measurements. ![]() ![]() Standard Practice for Calculation of Corrosion Rates and Related Information from Electrochemical Measurements. A Century of Tafel’s Equation: 1905-2005. Corrosion Monitoring of Concrete-Lined Brine Service Pipelines Using AC andDC Electrochemical Methods. G., Hinkebein, T., Maestas, L., & Montes, L. New York: Kluwer Academic/Plenum Publishers.īuchheit, R. The results demonstrate the effectiveness of the sensor as an efficient and practical approach to measuring pit-depth for aircraft structures, such as AA 7075-T6, and provide feasibility for its use in prognostic applications.īockris, J. RUL estimates were computed a-posteriori for three separate failure thresholds. Simultaneous evaluation of twenty-four μLPR sensors provided a stochastic data set appropriate for prognostics. At the end of the experiment, pit-depth due to corrosion was computed from each μLPR sensor and compared with the control coupons.The paper concludes with a feasibility study for the μLPR sensor in prognostic applications. LPR measurements were sampled once per minute and converted to a corrosion rate using the algorithms presented in this paper. Twelve corrosion coupons were placed alongside twenty-four μLPR sensors in a series of accelerated tests. The μLPR sensor is based on conventional macro-sized Linear Polarization Resistance (LPR) sensors with the additional benefit of a reduced form factor making it a viable and economical candidate for remote corrosion monitoring of high value structures, such as buildings, bridges, or aircraft.A series of experiments were conducted to evaluate the μLPR sensor for AA 7075-T6, a common alloy used in aircraft structures. Corrosion measurements are acquired using a micro-sized Linear Polarization Resistance (μLPR) sensor. This paper presents the theory and experimental validation of a Structural Health Management (SHM) system for monitoring corrosion. ![]()
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