Georgia Institute of Technology PHM17 Conference Corrosion Sensing, Modeling, Detection, Prediction and Decision Support Technologies George Vachtsevanos Georgia Institute of Technology PHM17 Conference Panel Session on Corrosion

Corrosion Technologies: Gaps, Challenges Corrosion Sensing: Need for new on-board sensors monitoring accurately long-term local and global corrosion Corrosion Modeling: Methods to address high fidelity corrosion modeling Corrosion Detection/Prediction Corrosion Mitigation: Emphasis on coating

The Integrated Methodology Al Alloy Panels Corrosion Monitoring/ Sensing Corrosion Detection/Prediction Assessment/ Decision Support Data Mining Corrosion Modeling Sensors Pre-Processing Global Diagnostics Reasoning Data Acquisition Feature Extraction Local Prognostics Action Optimum Aircraft Maintenance

The Database Sensing, Temperature, Relative Humidity, Salinity, Mass Loss measurements Images of coupons from submersion test and Lap Joint Chamber tests Images of cracks and pits found in the literature Pictures from field inspection Need for on-platform long-term data

Types of Corrosion Micro-structure corrosion Pitting Common denominator in almost all types of corrosion attack May assume different shapes Chlorides (Cl—) Inter-granular corrosion Grain-boundaries Stress induced cracking http://www.nace.org/Pitting-Corrosion/

Data Mining (Extracting Useful Information from Raw Data) Pre-processing Feature Extraction Mass Loss Data Wavelets Statistical Features Morphological Features Laser Confocal Microscope Feature Selection Classification and Prediction Particle Filtering Neural Networks Clustering Algorithms Prediction

Image Processing: Profile of Pit using Laser Confocal Microscope Surface Plot: 2D Image: µm µm

Corrosion Detection and Prediction Sensor Data Pre-processing Operating conditions and input stresses Diagnostic Model Features & performance Feature Extraction Fault diagnosis Prognostic Model Failure Prognosis Sensor Data Pre-processing Fault Detection Operating conditions and inputs Diagnostic Model Features & performance Feature Extraction Prognostic Model Failure Prognosis

Assessment/Decision Support/The Dynamic Case Based Reasoning Paradigm Assessing the severity of the corrosion state Severity Index-> determines the critical state of the aircraft/estimated from current corrosion state and prognostic information Exploits “smart” reasoning tools/methods Provides accurate and verifiable maintenance advisories A “smart” reasoning paradigm: The Dynamic Case Based Reasoning (DCBR) Architecture for Data Storage, Adaptation and Learning Dynamic Case Based reasoning-The “smart” knowledge base The Q-Learning Paradigm Current state Learning rate Immediate reward Discount factor Expected reward: “cost-to-go” function Current action Next state Next action

Corrosion parameter estimation From the Laboratory Environment to On-Board The Aircraft-Moving Forward Sensor Corrosion parameter estimation Sensing/ Data Acquisition Feature Extraction Current Signal Stress Crack Corrosion Models Corrosion Detection/ Prediction Assessment A/C Maintenance Reasoning: DCBR Severity Index

From the Laboratory Environment to On-Board The Aircraft-Moving Forward Damage detection provides a trigger for maintenance action Knowledge of damage and corrosion tolerance of the structure in adverse environments Parametric modeling allows for inclusion of stress crack profiles Moving forward: Accurate sensing; better corrosion assessment strategies; effective mitigation methods Living with corrosion?