#Sweden #AI Inspection #Trueflaw #Ringhals

Introduction

In a groundbreaking advancement for nuclear safety, the Ringhals power plant in Sweden has integrated artificial intelligence (AI) and machine learning (ML) into its ultrasonic inspection processes. This innovative move not only represents a first within the nuclear sector but also showcases how technology can enhance the reliability and efficiency of inspections critical for operational safety.

The Technology Behind the Inspection

This pioneering inspection was developed through a collaboration between Trueflaw, a Finnish firm specializing in defect detection systems, and the Electric Power Research Institute (EPRI) from the USA. The system was qualified for use in ultrasonic inspection related to non-destructive evaluations (NDE) of pressure vessel head penetrations. With this advancement, Ringhals aims to strengthen its safety measures during scheduled outages by efficiently monitoring the condition of critical components.

The vendor responsible for conducting the inspections is Wesdyne Sweden. They were able to utilize the automated capabilities granted by Trueflaw’s AI system, which was recently granted qualification by the Swedish Qualification Centre (SQC). This collaboration represents a significant step towards incorporating modern technology into a field that relies heavily on traditional inspection techniques.

Importance of Non-Destructive Testing

Non-destructive testing (NDT) methods like ultrasonic inspections are vital for identifying potential service-induced decay in nuclear power plants. Traditionally, these inspections produce vast amounts of data that require skilled human analysts to interpret. The tasks are often lengthy and labor-intensive, leading to possible delays and increased costs. However, with the integration of AI in the inspection workflow, these challenges are being addressed effectively.

How AI Revolutionizes Inspections

AI’s role in this process involves pre-screening the data collected, allowing it to identify any areas of potential defect before they are reviewed by human experts. This automated preliminary analysis dramatically reduces the time required for inspections, allowing the team to focus on the critical aspects of their assessments.

Instead of spending hours analyzing data manually, experts can now pinpoint areas of concern within minutes. This shift not only enhances the efficiency of inspections but also leads to greater consistency in evaluations and reduces the risk of human error.

The CEO of Trueflaw, Iikka Virkkunen, noted, “AI/ML has proven itself as a reliable and valuable tool for non-destructive evaluation. The first use in qualified nuclear inspection is an important milestone and shows AI can meet the highest of reliability and regulatory requirements.” This statement underscores the importance of integrating AI into the fabric of nuclear safety inspections, where precision is paramount.

Impact on Safety and Operations

The adoption of AI-powered ultrasonic inspections marks a vital step forward in the ongoing effort to enhance safety protocols in nuclear energy. By bolstering the reliability and expediency of inspections, power plants like Ringhals can operate more safely and efficiently. The eventual outcome will be not just safer plants but also reduced operational costs.

Moreover, as more nuclear facilities explore similar technological advancements, the industry as a whole will benefit from improved safety standards and operational efficiencies. As energy demands grow, the need for safe and efficient energy production is becoming increasingly critical, and innovations like AI in inspection processes are likely to play a key role in the future of energy production.

Conclusion

The introduction of AI and ML in ultrasonic inspections at the Ringhals nuclear plant highlights a significant leap forward in the integration of technology within safety inspections. By enhancing the efficiency and reliability of data analysis, this advancement promises to revolutionize the nuclear industry. As Trueflaw continues to lead in defect detection systems and NDE reliability, the potential for further advancements remains promising, paving the way for a safer future in energy production.