Development of a Data Driven Multiple Observer and Causal Graph Approach for Fault Diagnosis of Nuclear Power Plant Sensors and Field Devices

Data driven multiple observer and causal graph approach to fault detection and isolation is developed for nuclear power plant sensors and actuators. It can be integrated into the advanced instrumentation and control system for the next generation nuclear power plants. The developed approach is based on analytical redundancy principle of fault diagnosis. Some analytical models are built to generate the residuals between measured values and expected values. Any significant residuals are used for fault detection and the residual patterns are analyzed for fault isolation. Advanced data driven modeling methods such as Principal Component Analysis and Adaptive Network Fuzzy Inference System are used to achieve on-line accurate and consistent models. As compared with most current data-driven modeling, it is emphasized that the best choice of model structure should be obtained from physical study on a system. Multiple observer approach realizes strong fault isolation through designing appropriate residual structures. Even if one of the residuals is corrupted, the approach is able to indicate an unknown fault instead of a misleading fault. Multiple observers are designed through making full use of the redundant relationships implied in a process when predicting one variable. Data-driven causal graph is developed as a generic approach to fault diagnosis for nuclear power plants where limited fault information is available. It has the potential of combining the reasoning capability of qualitative diagnostic method and the strength of quantitative diagnostic method in fault resolution. A data-driven causal graph consists of individual nodes representing plant variables connected with adaptive quantitative models. With the causal graph, fault detection is fulfilled by monitoring the residual of each model. Fault isolation is achieved by testing the possible assumptions involved in each model. Conservatism is implied in the approach since a faulty sensor or a fault actuator signal is isolated only when their reconstructions can fully explain all the abnormal behavior of the system. The developed approaches have been applied to nuclear steam generator system of a pressurized water reactor and a simulation code has been developed to show its performance. The results show that both single and dual sensor faults and actuator faults can be detected and isolated correctly independent of fault magnitudes and initial power level during early fault transient

Levels and Determinants of DDT and DDE Exposure in the VHEMBE Cohort.

BackgroundAlthough indoor residual spraying (IRS) is an effective tool for malaria control, its use contributes to high insecticide exposure in sprayed communities and raises concerns about possible unintended health effects.ObjectiveThe Venda Health Examination of Mothers, Babies and their Environment (VHEMBE) is a birth cohort study initiated in 2012 to characterize prenatal exposure to IRS insecticides and exposures' impacts on child health and development in rural South Africa.MethodsIn this report, we describe the VHEMBE cohort and dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethylene (DDE) serum concentrations measured in VHEMBE mothers when they presented for delivery. In addition, we applied a causal inference framework to estimate the potential reduction in population-level p,p'-DDT and p,p'-DDE serum concentrations under five hypothetical interventions. A total of 751 mothers were enrolled.ResultsSerum concentrations of p,p' isomers of DDT and DDE were above the limit of detection (LOD) in ≥98% of the samples, whereas the o,p' isomers were above the LOD in at least 80% of the samples. Median (interquartile range) p,p'-DDT and p,p'-DDE serum concentrations for VHEMBE cohort participants were 55.3 (19.0-259.3) and 242.2 (91.8-878.7) ng/g-lipid, respectively. Mothers reporting to have lived in a home sprayed with DDT for malaria control had ~5-7 times higher p,p'-DDT and p,p'-DDE serum concentrations than those who never lived in a home sprayed with DDT. Of the five potential interventions tested, we found increasing access to water significantly reduced p,p'-DDT exposure and increasing the frequency of household wet mopping significantly reduced p,p'-DDT and p,p'-DDE exposure.ConclusionOur findings suggest that several intervention approaches may reduce DDT/DDE exposure in pregnant women living in IRS communities. https://doi.org/10.1289/EHP353

Classification-based prediction of effective connectivity between timeseries with a realistic cortical network model

Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

Supervised estimation of Granger-based causality between time series

Brain effective connectivity aims to detect causal interactions between distinct brain units and it is typically studied through the analysis of direct measurements of the neural activity, e.g., magneto/electroencephalography (M/EEG) signals. The literature on methods for causal inference is vast. It includes model-based methods in which a generative model of the data is assumed and model-free methods that directly infer causality from the probability distribution of the underlying stochastic process. Here, we firstly focus on the model-based methods developed from the Granger criterion of causality, which assumes the autoregressive model of the data. Secondly, we introduce a new perspective, that looks at the problem in a way that is typical of the machine learning literature. Then, we formulate the problem of causality detection as a supervised learning task, by proposing a classification-based approach. A classifier is trained to identify causal interactions between time series for the chosen model and by means of a proposed feature space. In this paper, we are interested in comparing this classification-based approach with the standard Geweke measure of causality in the time domain, through simulation study. Thus, we customized our approach to the case of a MAR model and designed a feature space which contains causality measures based on the idea of precedence and predictability in time. Two variations of the supervised method are proposed and compared to a standard Granger causal analysis method. The results of the simulations show that the supervised method outperforms the standard approach, in particular it is more robust to noise. As evidence of the efficacy of the proposed method, we report the details of our submission to the causality detection competition of Biomag2014, where the proposed method reached the 2nd place. Moreover, as empirical application, we applied the supervised approach on a dataset of neural recordings of rats obtaining an important reduction in the false positive rate