Further Development in Nondestructive Methods to Gauge Life Expectancy in Ferromagnetic Components

Manufactured nuclear components under stresses induced through normal operations cause mechanical fatigue and strain. Depending on their magnitude and distribution they can contribute to increasing the expected life of a component or for its premature failure. Using Barkhausen noise we can analyze the microstructural characteristics without damaging the sample through magnetization or acoustics. The samples in our case are ferromagnetic metals, also known as ferrous metals, from heat treated and rolled steel. A Rollscan 300 instrument and Microscan 600 software were used to acquire Barkhausen noise data from fatigued steel samples. MATLAB software and R software were used to evaluate results of the Microscan 600 to better understand the signal processing algorithms. In order to find a correlation we used a two random variable probability distribution function (PDF). plot We found the difference between the three positions taken on the given sample at each strain level, and with a 95% confidence level we created a plot of data points that found a loose correlation in the data results between both perpendicular and parallel testing. Using these results we can compare older sets of data and create an accurate prediction of stress levels induced upon nuclear components. We hope to create more precise predictions in the near future using alternative methods, such as statistical calibration techniques to find closer one‐to‐one correlations

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 fault prognosis experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the prognosis phase once they got exposed to real-world data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.Comment: 25 Pages, 13 Figures, 5 Table

A Review of Prognostics and Health Management Applications in Nuclear Power Plants

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40-year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation.Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many SMR and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities

Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop the accelerator before damage occurs. We describe the acceleratoroperation, related Machine Learning research, the prediction performance required to abort beamwhile maintaining operations, the monitoring device and its data, and the Siamese method andits results. These results show that the researched method can be applied to improve acceleratoroperations.Comment: 11 pages, 15 figures, for PR-A

A Review of Prognostics and Health Management Applications in Nuclear Power Plants

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40- year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation. Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many small modular reactor (SMR) and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities

Multi-module based CVAE to predict HVCM faults in the SNS accelerator

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several neural network (NN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptim

Research gaps and technology needs in development of PHM for passive AdvSMR components

Advanced small modular reactors (AdvSMRs), which are based on modularization of advanced reactor concepts, may provide a longer-term alternative to traditional light-water reactors and near-term small modular reactors (SMRs), which are based on integral pressurized water reactor (iPWR) concepts. SMRs are challenged economically because of losses in economy of scale; thus, there is increased motivation to reduce the controllable operations and maintenance costs through automation technologies including prognostics health management (PHM) systems. In this regard, PHM systems have the potential to play a vital role in supporting the deployment of AdvSMRs and face several unique challenges with respect to implementation for passive AdvSMR components. This paper presents a summary of a research gaps and technical needs assessment performed for implementation of PHM for passive AdvSMR components

Neural network based iterative algorithms for solving electromagnetic NDE inverse problems

The solution of inverse problems is of interest in a variety of applications ranging from geophysical exploration to medical diagnosis and non-destructive evaluation (NDE). Electromagnetic methods are often used in the nondestructive inspection of conducting and ferromagnetic materials. A crucial problem in electromagnetic NDE is signal inversion wherein the defect parameters must be recovered from the measured signals. Iterative algorithms are commonly used to solve this inverse problem. Typical iterative inversion approaches use a numerical forward model to predict the measurement signal for a given defect profile. The desired defect profile can then be found by iteratively minimizing a cost function. The use of numerical models is computationally expensive, and therefore, alternative forward models need to be explored. This thesis proposes neural network based forward models in iterative inversion algorithms for solving inverse problems in NDE.;This study proposes two different neural network based iterative inverse problem solutions. In addition, specialized neural networks forward models that closely model the physical processes in electromagnetic NDE are proposed and used in place of numerical forward models. The first approach uses basis function networks (radial basis function (RBFNN) and wavelet basis function (WBFNN)) to approximate the mapping from the defect space to the signal space. The trained networks are then used in an iterative algorithm to estimate the profile given the measurement signal. The second approach proposes the use of two networks in a feedback configuration. This approach stabilizes the solution process and provides a confidence measure of the inversion result. Furthermore, specialized finite element model based neural networks (FENN) are proposed to model the forward problem. These networks are derived from conventional finite element models and offer several advantages over conventional numerical models as well as neural network based forward models. These neural networks are then applied in an iterative algorithm to solve the inverse problem. Results of applying these algorithms to several examples including synthetic magnetic flux leakage (MFL) data are presented.</p