Prognostics and life beyond 60 years for nuclear power plants

Bond, Leonard J.; Ramuhalli, Pradeep; Tawfik, Magdy S.; Lybeck, Nancy J.

doi:10.1109/icphm.2011.6024316

Cited by 29 publications

(20 citation statements)

References 24 publications

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“…Prognostics and health management which includes SHM is the latest strategies and has been recognized in high technology community to maintain the assets (Bond et al, 2011). SHM can be defined as the monitoring system for detecting the changes in structural properties which can effects the performance and safety of the main system, building or plant.…”

Section: Introductionmentioning

confidence: 99%

Damage identification using wireless structural health monitoring system through smart sensor application

Shirazi¹,

Mustapha²,

Ahmad

2017

Int. j. adv. appl. sci

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Delamination, disbanding, void, low impact resistance and visible internal damage are some of damages uniquely found in the structure. Any damage found on the structure require repair. The inspection need to be done to detect the damage on structure before the structure can be repaired. NonDestructive Inspection (NDI) and Structural health monitoring (SHM) concept were used to detect flaws from structures. But the SHM differs from NDT which the system used to monitor the integrity of mechanical structures in a continuous and independent way. SHM helps to reduce financial cost for maintenance. SHM can monitor this situation in active and passive states, either by online or offline monitoring. The use of SHM is to augment the NDI application and not to replace it. This paper presents the damage identification technique by using the improved design of wireless structural health monitoring system. Smart PZT sensors were used as an actuator and receiver, coupled with two XBee's and two Ardiuno as signal generator and signal receiver. Program execution on transmitting and receiving the ultrasonic guided wave via the PZT sensor had been written in Makerplot. The acquired results showed that the wave is more even in non-defected area and disrupted in affected area.

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Section: Introductionmentioning

confidence: 99%

Damage identification using wireless structural health monitoring system through smart sensor application

Shirazi¹,

Mustapha²,

Ahmad

2017

Int. j. adv. appl. sci

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“…No formalized CBA for applying PHM in a specific NPP has been found; however, analyses by Bond et al (2011b) and Wacker et al (2007) suggests that fleet-wide savings of over $1 billion per year are possible in the United States when PHM is applied to all key equipment in legacy power plants. Analyses of PHM in other industries and in general suggest significant potential savings.…”

Section: Business Case For Phmmentioning

confidence: 99%

“…The technical gaps in available ISI and NDE methods were identified in Bond et al (2009a) and Bond et al (2009b). Some emerging NDE techniques may be applied for continuous online monitoring of components susceptible to SCC Bond 2010;Doctor et al 2010;Bond et al 2011a;Bond and Meyer 2011;Bond et al 2011b). The combination of new (and possibly currently unknown) degradation mechanisms and the increase in the number of components that become susceptible to aging-related degradation as plants transition to longterm operations are likely to challenge the capabilities of available ISI technology (Doctor 1988;Dobmann 2006;Bond et al 2009c).…”

Section: Passive Componentsmentioning

confidence: 99%

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

Coble¹,

Ramuhalli²,

Bond³

et al. 2012

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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...

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“…This interest is mainly originated by the following three reasons:  many nuclear SSCs are becoming old due to the extension of the life of the existing reactors beyond the initial 30 or 40 years. This aging may cause degradation and even failures if SSC maintenance and replacement is not properly planned [8];  many utilities are considering performing power up-rates in their plants. This typically requires increased coolant flow which may cause faster degradation of the SSCs;  there is a strong economic interest towards having longer fuel cycles and decreased outage times.…”

Section: Introductionmentioning

confidence: 99%

“…failures if SSC maintenance and replacement is not properly planned [8];  many utilities are considering performing power up-rates in their plants. This typically requires increased coolant flow which may cause faster degradation of the SSCs;  there is a strong economic interest towards having longer fuel cycles and decreased outage times.…”

Section: Introductionmentioning

confidence: 99%

A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression

Baraldi

Mangili

Zio

2015

Progress in Nuclear Energy

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Abstract-Advanced diagnostics and prognostics tools are expected to play an important role in ensuring safe and long term operation in nuclear power plants. In this context, we use Gaussian Process Regression (GPR) to build a stochastic model of the equipment degradation evolution and apply it for prognostics.GPR is a probabilistic technique for non-linear nonparametric regression that estimates the distribution of the future equipment degradation states by constraining a prior distribution to fit the available training data, based on Bayesian inference. Training data are taken from sequences of degradation measures collected from a set of similar historical equipments which have undergone a similar degradation process. Given new degradation measures from a currently degrading equipment (test trajectory), the distribution of the Remaining Useful Life (RUL) before failure is estimated by comparing with a failure criterion the distribution of the future degradation states predicted by GPR.Applications are shown on simulated data concerning the evolution of creep damage in ferritic steel exposed to high stress and on real data concerning the clogging of sea water filters placed upstream the heat exchangers of a BWR condenser.Index Terms-Remaining Useful Life, Prediction, Prognostics, Bayesian Inference, Gaussian Process Regression, Degradation, Ccreep, Ffilter Cclogging I. INTRODUCTIONUCLEAR INDUSTRY is considering the development and use of advanced diagnostic and prognostic tools to enable longer term safe operation of nuclear structures, systems and components (SSCs) [1][2][3][4][5][6][7]. This interest is mainly originated by the following three reasons:  many nuclear SSCs are becoming old due to the extension of the life of the existing reactors beyond the initial 30 or 40 years. This aging may cause degradation and even failures if SSC maintenance and replacement is not properly planned [8];  many utilities are considering performing power up-rates in their plants. This typically requires increased coolant flow which may cause faster degradation of the SSCs;  there is a strong economic interest towards having longer fuel cycles and decreased outage times. This may pose constraints on the frequency and extent of in-service inspections which may become not able to detect in a timely manner all SSC degradation modes. In this context, in the present paper, we consider the development of prognostic methods for estimating the remaining useful life of nuclear components, structures and systems. Data-driven and model-based methods can be used for predicting the Remaining Useful Life (RUL) of degrading equipment [9-10], i.e., the remaining time during which the equipment can continue performing its function in a safe and efficient way. This allows the implementation of predictive maintenance strategies which have the potential of increasing safety and lowering costs [11]. Model-based methods assume that a mathematical model of the degradation process is available. In practice, the detailed knowledge necessary for b...

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Prognostics and life beyond 60 years for nuclear power plants

Cited by 29 publications

References 24 publications

Damage identification using wireless structural health monitoring system through smart sensor application

Damage identification using wireless structural health monitoring system through smart sensor application

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

A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression

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