[PDF] Signal Validation Methods For Power Plant Performance Monitoring eBook

Author : Anjali Muraleedharan Nair
Publisher :
Page : 55 pages
File Size : 27,1 MB
Release : 2016
Category :
ISBN :

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Online Monitoring systems may offer an effective alternative to the current intrusive calibration assessment procedure used in the nuclear industry. Apart from optimizing the economic and human resource aspects of the currently utilized technique, OLM increases the opportunities for performance assessment and fault detection for nuclear instrumentation. This can lead to possibly extend or ultimately remove the current time based assessment process. Irrespective of its plausible benefits, OLM sees limited applicability in today's US fleet. Regulatory constraints that limits the large scale implementation of OLM can be addressed by developing highly sensitive signal validation technique and thereby structurally quantify its associated predictive uncertainty. A multi-tier Bayesian Inference model is developed to fit the high accuracy signal validation requirements set on OLM systems that are developed for instrumentation calibration applications in NPPs. The technique utilizes OLM predictions and original process data as inputs to learn the statistical characteristics of various errors of interest. Here, the implementation focuses on utilizing the uncertainty quantification capacities of this framework to graduate and possibly minimize model based error in OLM systems. This is achieved by a balance between ideal OLM model architecture and sensitivity of hyper parameter selection process for the Bayesian framework. Current implementation of this technique limits the iterative learning process to fewer cycles by marginalizing the hyper parameter distribution based on knowledgeable priors specific to the data set. Mathematically, this eases the number and complexity of the operations (example: integration of posteriors distributions to obtain closed form solutions for parameters of interest). In terms of applications, an extension of this technique is envisioned for performance based calibration status inspection by identifying deviations from calibration bounds using a fault flag system. This model can also be used for fault detection, virtual sensor development, and is suitable for various sensor types and operational modes. The developed framework provides promising results in isolating model inadequacy error for normal data for both stationary and transient ranges. However, currently the model inadequacy error tend to follow the drift, thereby limiting anomaly detection capacities. This can be countered by explicitly modeling the non-stationary error using Gaussian Process.

Author : Chenggang Yu
Publisher :
Page : pages
File Size : 33,23 MB
Release : 2002
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ISBN :

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On-line signal validation is essential for safe and economic operations of a complicated industrial system such as a nuclear power plant. Various signal validation methods based on empirical signal estimation have been developed and successfully used. The first part of the thesis addresses a common and unavoidable problem for these methods -fault propagation, which causes false identification of healthy signals as faulty ones because of the faults existing in other signals. This effect is especially serious when faults occur in multiple signals and/or during system transient. A sub-grouping technique is presented in the thesis to prevent the effect of fault propagation in general signal validation methods. Specifically, two methods, Subgroups Consistency Check (SCC) and Subgroups Voting (SV), are developed. Their effectiveness is demonstrated by using a well-known Multivariate State Estimation technique (MSET) as a general method of signal estimation. To further improve the performance of MSET estimation, a procedure called Feedback Once (FBO) is also developed. All these new methods are tested and compared with MSET by using real transient data from a reactor startup process in a nuclear power plant. The results show that false identification of signals caused by fault propagation is significantly reduced by the two sub-grouping methods and the FBO method is able to improve the performance of MSET estimation to some extent. The results demonstrate that implementation of these new methods can lead to an improved signal validation technique that remains effective even when faults occur in multiple signals during system transients. The other major contribution is on the improvement of statistical test used for signal validation. Sequential Probability Ratio Test (SPRT) is a popular method that has been widely used in many signal validation methods. However, the assumption of SPRT is too stringent to satisfy in practice, which may cause the false identification rate exceeding the preset tolerance. In this thesis, a Sequential Rank-sum Probability Ratio Test (SRPRT) method is developed. This method is similar to SPRT in procedure but is based on a much weaker assumption that can be easily satisfied. The demonstrations show that SRPRT yields a smaller false identification rate than SPRT and is always below the preset tolerance.

Author : Hosameldin Ahmed
Publisher : John Wiley & Sons
Page : 475 pages
File Size : 35,30 MB
Release : 2019-12-03
Category : Technology & Engineering
ISBN : 1119544645

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Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Author : Jonghyun Kim
Publisher : Springer Nature
Page : 280 pages
File Size : 13,60 MB
Release : 2023-02-20
Category : Technology & Engineering
ISBN : 3031223861

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This book introduces novel approaches and practical examples of autonomous nuclear power plants that minimize operator intervention. Autonomous nuclear power plants with artificial intelligence presents a framework to enable nuclear power plants to autonomously operate and introduces artificial intelligence (AI) techniques to implement its functions. Although nuclear power plants are already highly automated to reduce human errors and guarantee the reliability of system operations, the term “autonomous” is still not popular because AI techniques are regarded as less proven technologies. However, the use of AI techniques and the autonomous operation seems unavoidable because of their great advantages, especially, in advanced reactors and small modular reactors. The book includes the following topics: Monitoring, diagnosis, and prediction. Intelligent control. Operator support systems. Operator-autonomous system interaction. Integration into the autonomous operation system. This book will provides useful information for researchers and students who are interested in applying AI techniques in the fields of nuclear as well as other industries. This book covers broad practical applications of AI techniques from the classical fault diagnosis to more recent autonomous control. In addition, specific techniques and modelling examples are expected to be very informative to the beginners in the AI studies.