ABSTRACT

Inspection of ballast tanks and enclosures is generally performed using traditional methods such as visual inspection and non-destructive evaluation (NDE) techniques. However, it is common for these methods to often be labor intensive and limited by physical restrictions that prohibit access to certain areas. It is also challenging to plan for maintenance activities before the vessel has entered drydock due to limitations for inspections at sea or during short port calls. Further, the evaluation of the coating condition is heavily dependent on the inspector, and the quality of the data gathered is varying. It is the asset owner or the class society responsible for the inspection that owns the inspection reports, hence the assessment of the in-service performance of the ballast tank coating is not readily available for the supplier of the coating. Accordingly, there is a desire to transition to embedded technologies that provide monitoring capabilities that can capture the onset, and monitor the evolution of damage in an autonomous manner and preferably to detect coating degradations before extensive corrosion has developed.

This paper presents the capabilities and limitations of a candidate sensor system to identify and characterize the location and severity of defects and degradation of water ballast tank coatings.

INTRODUCTION

Inspection of ballast tanks and enclosures is generally performed using traditional methods such as visual inspection and non-destructive evaluation (NDE) techniques. However, it is common for these methods to often be labor intensive and limited by physical restrictions that prohibit access to certain areas. Further, the evaluation of the coating condition is heavily dependent on the inspector, and the quality of the data gathered is varying.

Accordingly, there is a desire to transition to embedded technologies that provide monitoring capabilities that can capture the onset and evolution of a damage in an autonomous manner. Several technologies have been developed for corrosion monitoring in tanks, structures, pipelines, and other industrial applications 1,2,3,4. Recent advances in the development of corrosion sensors have resulted in a number of systems capable of measuring coating performance in the field. Most corrosion sensor systems are based on electrochemical impedance spectroscopy (EIS) or electrochemical noise measurements (ENM). As with all coating monitoring systems, however, its recent development and limited usage by industry has slowed the development of universal data algorithms that relate sensor output to specific coating failure mechanisms.

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