Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning. (February 2023)

• Record Type: Journal Article
• Authors: Sandhu, Harleen Kaur; Bodda, Saran Srikanth; Gupta, Abhinav
• Is Part Of: International journal of pressure vessels and piping. Volume 201(2023)
• Publication Date: 2023-02
• Subjects: Condition assessment -- Deep learning -- Nuclear piping -- Flow-assisted corrosion/erosion -- Degradation detection; Pressure vessels -- Periodicals; Pipe -- Periodicals; Récipients sous pression -- Périodiques; Tuyaux -- Périodiques; Pipe; Pressure vessels; Periodicals; 681.76041
• DOI: 10.1016/j.ijpvp.2022.104849
• Languages: English
• ISSNs: 0308-0161

Abstract

Abstract: Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. In this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy. Highlights: Novel approach for feature extraction of degradation-sensitive quantities. Prediction of degradation severity in nuclear piping systems under uncertainty. Design and architecture of deep learning for post-hazard condition assessment. Illustration of condition assessment methodology on real-life nuclear piping systems.