Intelligent defect detection and predictive structural integrity assessment tools for safe topside piping system

The most critical structural features in topside piping systems are the welded joints connecting the tubes, pipes and valves. The lack of acceptance criteria in current standards and practices for topside piping do not allow any detectable weld defects and imposes costly and unnecessary repair operations in the manufacturing phase. Also, if defects are detected during operation it often results in high costs of repair and downtime. In addition, these requirements make lifetime extension more challenging. Experience from pipelines indicates that repairing all indications of weld defects is overly conservative. There is a strong need for tools and methods to evaluate the criticality of defects based on acceptance criteria. The IDDEA project aims to develop intelligent defect detection and predictive structural integrity assessment tools to support decision-making in topside piping production, maintenance and life extension. The key R&D challenge is to establish the relationship between defects, material and loading in order to develop robust acceptance criteria for topside piping. The planned innovations in the project include: 1) methods for accurate detection and classification of defects; 2) robust acceptance criteria based on deep understanding of materials failure mechanisms; 3) data-driven and model-based tools for engineering critical assessment. Successful implementation of the innovation will support the quality assurance and safety during the whole lifetime of topside piping, resulting in substantial cost reduction, increased productivity as well as reduced carbon footprint. In an increasingly competitive market situation, the planned innovations are believed to be important and critical for the participating companies to keep or increase their market share. The results are directly transferable to other industries and sectors such as marine and renewable energy, where lifetime extension of piping system is deemed necessary. In 2021, several test welds have been made with known defects (type, size, shape and location). These will form the basis of a database where different methods for detecting defects are used. Each method shall be performed by several inspectors. This will give an indication of the accuracy of the classification of the defects. The use of several methods will also give an indication of the difference between the methods. After the scan is completed, some of the defects will be sliced up to verify the exact size of the defect. The work with scanning and slicing is mainly expected to be completed in 2022. In 2021, modeling of piping and fracture mechanical test specimens has also been carried out to ensure that the fracture mechanical testing provides accuratet, but still conservative, fracture mechanical values that can be used in analyzes of the piping systems. Modeling of cracks in pipes has been performed using Abaqus. These simulations are used to evaluate and calibrate analyzes in LINKpipe. LINKpipe is a modeling tool developed by SINTEF that will be used in further parameter studies. Fracture mechanical testing of relevant materials and welding methods has been performed, which will be included in further analyzes to establish acceptance criteria for welding defects in piping systems.

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The IDDEA project aims to establish the tools and methods needed to perform safer and cost-efficient production, operation and life extension on topside piping. The project will develop intelligent defect detection and predictive structural integrity assessment tools to support decision-making during various phases of a piping lifetime. The corrosion resistance alloys (CRA) topside piping will be focused. The most critical R&D challenges include the development of 1) acceptance criteria for topside CRA piping, 2) defect detection and classification methods, 3) predictive structural integrity assessment method and 4) engineering tools for engineering critical assessment (ECA). The project will investigate the damage mechanisms of materials under different conditions (e.g., loading and environment), and thus establishing acceptance criteria for assessment of defects. The PAUT method will be further developed for defect detection especially in CRA piping. The digital solutions like machine learning, database and models will be applied to develop engineering tools to support decision-making related to predictive life extension. Finally, the innovation will be demonstrated by relevant industrial case studies such as decision-making regarding repair during piping production. In today's piping production, weld defects are not acceptable, which often leads to unnecessary and expensive repair. The knowledge-based tools developed in the IDDEA project will help the operators to make rational decisions regarding repair and future inspection planning.