Analatom Inc

Santa Clara, CA, United States

Analatom Inc

Santa Clara, CA, United States

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Li H.,Georgia Institute of Technology | Garvan M.R.,Georgia Institute of Technology | Li J.,Georgia Institute of Technology | Echauz J.,JE Research Inc. | And 2 more authors.
PHM 2014 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014 | Year: 2014

It has been established that corrosion is one of the most important factors causing structural deterioration, loss of metal, and ultimately decrease of product performance and reliability. Corrosion monitoring, accurate detection and interpretation are recognized as key enabling technologies to reduce the impact of corrosion on the integrity of critical aircraft and industrial assets. Interest in corrosion measurement covers a broad spectrum of technical approaches including acoustic, electrical and chemical methods. Surface metrology is an alternative approach used to measure corrosive rate and material loss by obtaining surface topography measurement at micrometer levels. This paper reports results from an experimental investigation of pitting corrosion detection and interpretation on aluminum alloy panels using 3D surface metrology methods, image processing and data mining techniques. Sample panels of AA 7075-T6, an aluminum alloy commonly used in aircraft structures, were coated on one side with a corrosion protection coating and assembled in a lap-joint configuration. Then, a series of accelerated corrosion testing of the lap-joint panels were performed in a cyclic corrosion chamber running ASTM G85-A5 salt fog test. Panel surface characterization was evaluated with laser microscopy and stylus-based profilometry to obtain global and local surface images/characterization. Promising imaging and surface features were extracted and compared between the uncoated and coated panel sides, as well as on the uncoated sides under different corrosion exposure times. In the evaluation process, image processing, information processing and other data mining techniques were utilized. Information processing involves the steps of feature or Condition Indicator extraction and selection. The latter step addresses the problem of selecting those features that are maximally correlated with the actual corrosion state, for the purpose of corrosion detection, localization, quantification and state estimation. The results, verified by mass loss data, confirmed the contention that pits at the panel surfaces formed as a result of electrochemical corrosion attack, and showed that deteriorating pitting corrosion attack correlates with increasing corrosion exposure times. This study is a first step in the process of understanding, assessing and responding to the pitting corrosion and ultimately preventing material failure to insure aircraft structural integrity.


Connolly R.J.,Analatom Inc | Brown D.,Analatom Inc | Darr D.,Analatom Inc | Morse J.,Analatom Inc | Laskowski B.,Analatom Inc
Lecture Notes in Mechanical Engineering | Year: 2015

This paper presents an experiment adapting linear polarization resistance- based corrosion sensors, originally developed for aerospace applications, to measure the corrosion rate of API 5L ERW grade-B steel natural gas line pipe using micro-sized linear polarization resistance (μLPR) sensors made from the same alloy and grade steel. Sensors were installed under a 15 mil coating of fusion-bonded epoxy, at various proximities to a 1/8 inch defect introduced at a weld joint and along the pipe seam. After sensor installation the pipe was buried in an controlled environment with soil amended to a pH of five. This environment was held at a temperature above 35 °C while soil moisture content was modulated between wet and dry cycles, each lasting 7 days. LPR and environmental measurements were sampled at 5 min intervals. Post processing was performed to convert the LPR measurements to a surface-loss. Comparisons made in the data showed API 5L ERW grade-B steel natural gas pipelines were highly susceptible to corrosion along the seam, with all sensors showing activity in this region early in the experiment. Sensors adjacent to a weld joint began to display evidence of corrosion more slowly. These results verify the ability of μLPR sensors to measure corrosion activity under protective coatings in underground environments. © Springer International Publishing Switzerland 2015.


Brown D.,Analatom Inc. | Darr D.,Analatom Inc. | Morse J.,Analatom Inc. | Laskowski B.,Analatom Inc. | Betti R.,Columbia University
Proceedings of the 6th European Workshop - Structural Health Monitoring 2012, EWSHM 2012 | Year: 2012

This paper presents the theory and experimental validation of Analatom's 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 validate the μLPR sensor for AA 7075-T3. Test coupons were placed alongside Analatom's μLPR sensors in a series of accelerated tests. LPR measurements were sampled at a rate of once per minute and converted to a corrosion rate using Analatom's SHM system. At the end of the experiment, pit-depth due to corrosion was computed for each sensor from the recorded LPR measurements and compared to the average pit-depth measured on the control coupons. The results demonstrate the effectiveness of the sensor as an efficient means to measure pit-depth for AA 7075-T3.


Waters N.,Analatom Inc. | Connolly R.,Analatom Inc. | Brown D.,Analatom Inc. | Laskowski B.,Analatom Inc.
PHM 2014 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014 | Year: 2014

This paper discusses a micro-linear polarization resistance (ìLPR) sensor modified to perform coating evaluation by means of electrochemical impedance spectroscopy (EIS). A circuit model is used with the EIS data to measure solution resistance, pore resistance, charge transfer resistance, intact coating capacitance, and double layer capacitance. These measurements allow the end user to monitor degradation of protective coatings in real-time, through non-destructive means. This is demonstrated through an accelerated aging test using a coated metal plate with a modified ìLPR sensor. A metal panel made from aluminum alloy 7075-T6 was coated with 2 mils of an epoxy-based polymer coating and 2 mils of high solids polyurethane. The sensor was adhered to the face of the coated panel in a manner that allowed the electrolyte solution consisting of 3.5% NaCl to flow between the sensor and the coated surface of the panel. EIS measurements were acquired every hour for a total of 35 hours and at the conclusion of the test, changes in key parameters within the circuit model identified the initial time and mechanism of coating degradation, in this case, delamination.


Brown D.W.,Analatom Inc. | Connolly R.J.,Analatom Inc. | Laskowski B.,Analatom Inc. | Garvan M.,Georgia Institute of Technology | And 3 more authors.
PHM 2014 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014 | Year: 2014

A direct method of measuring corrosion on a structure using a micro-linear polarization resistance (ìLPR) sensor is presented. The new three-electrode ìLPR sensor design presented in this paper improves on existing LPR sensor technology by using the structure as part of the sensor system, allowing the sensor electrodes to be made from a corrosion resistant or inert metal. This is in contrast to a twoelectrode ìLPR sensor where the electrodes are made from the same material as the structure. A controlled experiment, conducted using an ASTM B117 salt fog, demonstrated the three-electrode ìLPR sensors have a longer lifetime and better performance when compared to the two-electrode ìLPR sensors. Following this evaluation, a controlled experiment using the ASTM G85 Annex 5 standard was performed to evaluate the accuracy and precision of the three-electrode ìLPR sensor when placed between lap joint specimens made from AA7075-T6. The corrosion computed from the ìLPR sensors agreed with the coupon mass loss to within a 95% confidence interval. Following the experiment, the surface morphology of each lap joint was determined using laser microscopy and stylus-based profilometry to obtain local and global surface images of the test panels. Image processing, feature extraction, and selection tools were then employed to identify the corrosion mechanism (e.g. pitting, intergranular).


Brown D.,Analatom Inc. | Darr D.,Analatom Inc. | Morse J.,Analatom Inc. | Laskowski B.,Analatom Inc.
Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012, PHM 2012 | Year: 2012

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.

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