Autonomous Coating Condition Monitoring System
Kevin Farinholt, Fritz Friedersdorf
Introduction: Corrosion of tanks and enclosures is a top expense for ships, recently accounting for more than $250M in direct annual costs to the US Navy. A combination of coatings and cathodic protection systems are used to protect these tanks; however, traditional inspection methods often require direct physical access to monitor the condition of these systems during scheduled dockside maintenance. With inspection costs that can range from $8k – $15k per tank, there is a desire to develop autonomous health monitoring systems that can identify the presence of defects within coatings, and track the growth and location of these defects with time. Presently, a coating health monitoring technology that relies on electrochemical sensors and stochastic models is being developed to quantify the location and extent of coating damage. This technology is based on a low power, low cost smart sensor platform designed for structural health and equipment monitoring and diagnostics. The coating condition monitoring system is composed of a network of sensors that measure environmental conditions and electrochemical parameters to provide an evaluation of the health of coating, substrate, and cathodic protection systems. Previous results have demonstrated the successful performance of this system using one- and two-dimensional geometries of coated steel plates. The present work extends this development to three-dimensional structures representative of ballast water tanks, and to length scales (> 5 meters through 1D experiments) that could be expected in shipboard applications.
Key Points: Electrochemical Impedance Spectroscopy (EIS) Measurements / Neural Network Models
- EIS measurements are widely used in material and system characterization, and can be used to monitor coating health and potential degradation over time. • The EIS technique can be applied in 2-wire or 3-wire (with reference) configurations.
- Transmission line models can relate EIS measurements to individual components (fluid properties, coatings, substrate, etc.) within complex systems.
- Neural network models using complex EIS measurements at different frequencies can offer insight into the physical properties of a complex electrochemical system.
- Low frequency EIS measurements have strong correlation with defect size.
- High frequency EIS measurements are directly related to separation distances. Embedded Coating Condition Monitoring (CCM) System
- A first generation prototype has been developed to perform swept frequency EIS measurements.
- The CCM system uses a flexible design that supports wired and wireless network connectivity.
- The system is compatible with the IEEE-1451 family of open standards that are designed to bring cross platform interoperability and plug-and-play functionality to distributed sensor networks. Physical Tank Testing
- Tests are performed using steel substrates and relevant protective coatings.
- Previous tests have demonstrated performance in 1D and 2D configurations.
- Neural network models have been extended to consider 3D test cells, with highly accurate (R2 > 0.99) predictions of damage size and location within the laboratory environment.
- Tests at length scales > 5m demonstrate that the technique is viable for shipboard applications. Integration of the Embedded System into Ship Structures
- The final design for the second autonomous embedded system will be presented along with methods for mounting and data processing / prediction.
- Future qualification testing and shipboard demonstration will be discussed. Conclusions An embedded Coating Condition Monitoring (CCM) system is in development, which will be capable of autonomously evaluating the health of coatings and cathodic protection systems within Navy ballast tanks.
This system will provide assessments of damage size and location, and support the transition from time-based to condition-based maintenance strategies. Demonstrations in 1D, 2D, and 3D test cells have shown the effectiveness of this technique, and the next generation is being designed for testing in relevant scale environments.