@article{sandhu_bodda_yan_sabharwall_gupta_2024, title={A comparative study on deep learning models for condition monitoring of advanced reactor piping systems}, volume={209}, ISSN={["1096-1216"]}, DOI={10.1016/j.ymssp.2023.111091}, abstractNote={Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system's health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant's sensor data. This research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.}, journal={MECHANICAL SYSTEMS AND SIGNAL PROCESSING}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Yan, Erin and Sabharwall, Piyush and Gupta, Abhinav}, year={2024}, month={Mar} }
 @misc{sandhu_bodda_gupta_2023, title={A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities}, volume={16}, ISSN={["1996-1073"]}, DOI={10.3390/en16062628}, abstractNote={The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali–silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of existing techniques to nuclear systems is somewhat limited because its response requires characterization of high and low-frequency vibration modes, whereas previous studies focus on systems where a single vibration mode can define the degraded state. Data assimilation and storage is another challenging aspect of autonomous control. Advances in AI and data mining world can help to address these challenges.}, number={6}, journal={ENERGIES}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Gupta, Abhinav}, year={2023}, month={Mar} }
 @article{sandhu_bodda_sauers_gupta_2023, title={Condition Monitoring of Nuclear Equipment-Piping Systems Subjected to Normal Operating Loads Using Deep Neural Networks}, volume={145}, ISSN={["1528-8978"]}, DOI={10.1115/1.4062462}, abstractNote={Abstract}, number={4}, journal={JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Sauers, Serena and Gupta, Abhinav}, year={2023}, month={Aug} }
 @article{sandhu_bodda_gupta_2023, title={Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning}, volume={201}, ISSN={["1879-3541"]}, DOI={10.1016/j.ijpvp.2022.104849}, abstractNote={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.}, journal={INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING}, author={Sandhu, Harleen Kaur and Bodda, Saran Srikanth and Gupta, Abhinav}, year={2023}, month={Feb} }
 @article{nie_bodda_sandhu_han_gupta_2022, title={Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method}, volume={22}, ISSN={["1424-8220"]}, url={https://doi.org/10.3390/s22186869}, DOI={10.3390/s22186869}, abstractNote={Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.}, number={18}, journal={SENSORS}, author={Nie, Guang-Yu and Bodda, Saran Srikanth and Sandhu, Harleen Kaur and Han, Kevin and Gupta, Abhinav}, year={2022}, month={Sep} }