Fault monitoring of nuclear power plant sensors and field devices

Upadhyaya, B.R.; Zhao, Ke; Lu, Baofu

doi:10.1016/s0149-1970(03)00046-5

Cited by 55 publications

(23 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What can be observed once the failure has been initiated is the PWR control system responding to the failure. The control system attempts to compensate for the malfunction and maintain steady output, producing a complex response [17]. If no operator action is taken, the effect of the initiated leak is a unit trip, where the plant stops supplying power to the grid.…”

Section: B Degradation Model Implementationmentioning

confidence: 99%

Prognostic Modeling Utilizing a High-Fidelity Pressurized Water Reactor Simulator

McGhee

Catterson

Brown³

2018

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

Abstract-Within power generation, ageing assets and an emphasis on more efficient operation of power systems and improved maintenance decision methods has led to a growing focus on asset prognostics. The main challenge facing the implementation of successful asset prognostics in power generation is the lack of available run-to-failure data. This paper proposes to overcome this issue by use of full scope high fidelity simulators to generate the run-to-failure data required. From this simulated failure data a similarity based prognostic approach is developed for estimating the Remaining Useful Life of a valve asset. Case study data is generated by initializing prebuilt industrial failure models within a 970MW Pressurized Water Reactor simulation. Such full scope high fidelity simulators are mainly operated for training purposes, allowing personnel to gain experience of standard operation as well as failures within a safe, simulated operating environment. This research repurposes such a high fidelity simulator to generate the type of data and affects that would be produced in the event of a fault. The fault scenario is then run multiple times to generate a library of failure events. This library of events was then split into training and test batches for building the prognostic model. Results are presented and conclusions drawn about the success of the technique and the use of high fidelity simulators in this manner.Index Terms-Model-based prognostics, remaining useful life, high fidelity simulation, power generation,

show abstract

Section: B Degradation Model Implementationmentioning

confidence: 99%

Prognostic Modeling Utilizing a High-Fidelity Pressurized Water Reactor Simulator

McGhee

Catterson

Brown³

2018

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

show abstract

“…However, the results indicate that active power is closely related to internal mechanical phenomena in the MOV; this indicator may be useful for monitoring additional faults and/or components. Upadhyaya et al (2003) give an example of an advanced surveillance, fault detection, and diagnostic system applied to a turbine control valve during transient operation. This approach involves a data-driven model for predicting nominal variable values and a decision-making module for anomaly detection and diagnosis.…”

Section: Valvesmentioning

confidence: 99%

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

Coble¹,

Ramuhalli²,

Bond³

et al. 2012

View full text Add to dashboard Cite

Recent years have seen a resurgence of nuclear power worldwide, with interest in extending the operating life of the approximately 436 reactors currently in service (as of March, 2012), 61 new reactors being constructed, and as many as 162 under consideration. Renewed worldwide interest in nuclear power has been somewhat tempered by the March 2011 incident at Fukushima Dai-ichi in Japan. However, nuclear power is still considered a key element in meeting future worldwide sustainable energy, energy security, and emissions goals. Currently, three separate thrusts to safe and economical nuclear power development for energy security are being pursued in the United States: (i) longer term operation for the legacy fleet, from 40-60 and possibly 60-80 years; (ii) near-term new nuclear plants with a 60-year design life; and (iii) small modular reactors, which are expected to employ light water reactor technology at least in the medium term. Within these activities, attention is turning to enhanced methods for plant component and structural health management.The operating U.S. fleet includes 104 light water reactors. In addition, there are now (as of May 2012) four new nuclear power plants (AP-1000 plants) under construction in the United States, and two delayed plants are being completed by the Tennessee Valley Authority. There is also interest in the United States in small modular reactors (SMRs), which could be easier to match to existing grid infrastructure and which could replace aging coal fired plants. The current low price for natural gas presents a challenge to the economics of nuclear power, at least in the short term; however, some recent studies have demonstrated that nuclear generation will be competitive in the longer term (at least in some markets) when anticipated escalation in gas prices and the cost of building, operating, and maintaining gas-fired plants are considered over those same time periods.This report reviews the current state of the art of prognostics and health management (PHM) for nuclear power systems and related technology currently applied in field or under development in other technological application areas, as well as key research needs and technical gaps for increased use of PHM in nuclear power systems. The historical approach to monitoring and maintenance in nuclear power plants (NPPs), including the Maintenance Rule for active components and Aging Management Plans for passive components, are reviewed. An outline is given for the technical and economic challenges that make PHM attractive for both legacy plants through Light Water Reactor Sustainability (LWRS) and new plant designs. There is a general introduction to PHM systems for monitoring, fault detection and diagnostics, and prognostics in other, non-nuclear fields. The state of the art for health monitoring in nuclear power systems is reviewed. A discussion of related technologies that support the application of PHM systems in NPPs, including digital instrumentation and control systems, wired and wireless sensor techno...

show abstract

“…Techniques for process monitoring and FDD are based on a variety of paradigms including signal‐based techniques8–11 that are mainly applied to mechanical systems, model‐based techniques4, 12–14 which address a wider spectrum of applications, rule‐based techniques,15–18 and more recently, knowledge‐based techniques19–22 that blend heuristic knowledge into monitoring application. Such methodologies have shown their potential whenever cost‐benefit economics have justified the required effort in developing applications.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of process faults in chemical systems using a local partial least squares approach

Krüger¹,

Dimitriadis

2008

AIChE Journal

View full text Add to dashboard Cite

This article discusses the application of partial least squares (PLS) for monitoring complex chemical systems. In relation to existing work, this article proposes the integration of the statistical local approach into the PLS framework to monitor changes in the underlying model rather than analyzing the recorded input/output data directly. As discussed in the literature, monitoring changes in model parameters addresses the problems of nonstationary behavior and presents an analogy to model-based approaches. The benefits of the proposed technique are that (i) a detailed mechanistic plant model is not required, (ii) nonstationary process behavior does not produce false alarms, (iii) parameter changes can be non-Gaussian, (iv) Gaussian monitoring statistics can be established to simplify the monitoring task, and (v) fault magnitude and signatures can be estimated. This is demonstrated by a simulation example and the analysis of recorded data from two chemical processes. 2008 American Institute of Chemical Engineers AIChE J, 54: 2581-2596, 2008 Keywords: condition monitoring, fault detection, fault diagnosis, partial least squares, local approach IntroductionThe condition monitoring of industrial processes is standard practice in many industries, particularly the chemical industry. Based on the large body of work in the area of fault detection and diagnosis (FDD), the goals of condition monitoring are threefold: (i) to detect anomalous behavior in a process from the measured process variables, (ii) to locate and identify the source of such behavior, and (iii) to identify the magnitude of the fault. An ideal condition monitoring procedure can prevent a major malfunction in processing units by identifying incipient faults before they actually cause significant damage. Detailed discussions of condition monitoring and fault detection aspects can be found in many survey papers, for example, Refs. 1-4, or research texts. 5-7Techniques for process monitoring and FDD are based on a variety of paradigms including signal-based techniques [8][9][10][11] that are mainly applied to mechanical systems, model-based techniques 4,12-14 which address a wider spectrum of applications, rule-based techniques, [15][16][17][18] With advances in instrumentation and computer technology, the number of recorded process variables has increased to the point that some or many of the measured variables can be highly correlated. 27 In such cases, the creation of condition monitoring models is hindered by multicollinearity and suitably derived methods must be used. The most popular of these methods are statistical-based techniques, such as principal component analysis (PCA), partial least squares (PLS), and their extensions.In a previous paper, 28 a statistical theory was noted which can be readily used for the early detection of incipient faults, known as the local approach. The theory can be used to transform the problem of detecting a fault in a stochastic process to that of monitoring the mean of a Gaussian vector that is constructed from the...

show abstract

Fault monitoring of nuclear power plant sensors and field devices

Cited by 55 publications

References 2 publications

Prognostic Modeling Utilizing a High-Fidelity Pressurized Water Reactor Simulator

Prognostic Modeling Utilizing a High-Fidelity Pressurized Water Reactor Simulator

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

Diagnosis of process faults in chemical systems using a local partial least squares approach

Contact Info

Product

Resources

About