Real-Time Corrosion Monitoring of Aircraft Structures with Prognostic Applications



Douglas Brown Duane Darr Jefferey Morse Bernard Laskowski


This paper presents the theory and experimental validation of a Structural Health Management (SHM) system for monitoring corrosion. Corrosion measurements are acquired using a micro-sized Linear Polarization Resistance (μLPR) sensor. 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. Twelve corrosion coupons were placed alongside twenty-four μLPR sensors in a series of accelerated tests. LPR measurements were sampled once per minute and converted to a corrosion rate using the algorithms presented in this paper. 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. Simultaneous evaluation of twenty-four μLPR sensors provided a stochastic data set appropriate for prognostics. RUL estimates were computed a-posteriori for three separate failure thresholds. 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.

How to Cite

Brown, D., Darr, D. ., Morse, J. ., & Laskowski, B. . (2012). Real-Time Corrosion Monitoring of Aircraft Structures with Prognostic Applications. Annual Conference of the PHM Society, 4(1).
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corrosion, prognostics, structural health monitoring, aircraft

Bockris, J. O., Reddy, A. K. N., & Gambola-Aldeco, M. (2000). Modern Electrochemistry 2A. Fundamentals of Electrodics (2nd ed.). New York: Kluwer Academic/Plenum Publishers.

Buchheit, R. G., Hinkebein, T., Maestas, L., & Montes, L. (1998, March 22-27). Corrosion Monitoring of Concrete-Lined Brine Service Pipelines Using AC andDC Electrochemical Methods. In CORROSION 98.

San Diego, Ca.Burstein, G. T. (2005, December). A Century of Tafel’s Equation: 1905-2005. Corrosion Science, 47(12), 2858-2870.

G102, A. S. (1994). Standard Practice for Calculation of Corrosion Rates and Related Information from Electrochemical Measurements. Annual Book of ASTM Standards, 03.02.

G59, A. S. (1994). Standard Practice for Conducting Potentiodynamic Polarization Resistance Measurements. Annual Book of ASTM Standards, 03.02.

Harris, S. J., Mishon, M., & Hebbron, M. (2006, Octo- ber). Corrosion Sensors to Reduce Aircraft Maintenance. In RTO AVT-144 Workshop on Enhanced Aircraft Platform Availability Through Advanced Maintenance Concepts and Technologies. Vilnius, Lithuania.

Huston, D. (2010). Structural Sensing, Health Monitoring, and Performance Evaluation (B. Jones & W. B. S. J. Jnr., Eds.). Taylor and Francis.

Introduction to Corrosion Monitoring. (2012, August 20). Online. Available from

Twomey, M. (1997). Inspection Techniques for Detecting Corrosion Under Insulation. Material Evaluation, 55(2), 129-133.

Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Hoboken, NJ, USA: John Wiley and Sons.
Wagner, C., & Traud, W. (1938). Elektrochem, 44, 391.
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