Fatigue Monitoring Approaches for Power Plants

Jouan, Benoît; Bergholz, Steffen; Rudolph, Jürgen

doi:10.1115/pvp2014-28100

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…This presents valuable information that needs to be further developed to support structural analysis. For example, similar field data analysis has previously been reported for wind turbines where the fatigue cycle was measured and implemented in computational model to assess possible improvements and design modification [12], [13].…”

Section: Discussionmentioning

confidence: 99%

Development of industrial process characterisation through data analysis

Josifovic

Corney

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Recently the world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the exploitation of large datasets for insights into machine behaviour. In the case of optimisation, where usually a simulation model is provided for the exploration of the parameter space, the new data-driven approach translates into the use of data for quantifying problems and delivering an optimal solution

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Section: Discussionmentioning

confidence: 99%

Development of industrial process characterisation through data analysis

Josifovic

Corney

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…This will have to be accounted for when performing predictions in defect growth. Potential approaches for a more realistic representation of the loading conditions such as using on-load data 4,34,35 can be substituted as needed.…”

Section: Description Of the Structural Problem And Damage Growth Modelmentioning

confidence: 99%

A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

Leung

Corcoran

2021

Structural Health Monitoring

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The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.

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Fatigue Assessment of NPP Piping and Components Using Realistic Thermal-Mechanical Load Histories

Rudolph¹,

Maneschy

Cisternas

et al. 2014

Volume 1: Codes and Standards

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Modern state-of-the-art fatigue monitoring approaches gain in importance as part of the ageing management of conventional and nuclear power plant components (NPP) and particularly in the context of lifetime extension projects and new plants. A key feature of qualified fatigue monitoring is the measurement of realistic loads based on plant instrumentation and/or local temperature measurement. The major prerequisites for any subsequent fatigue assessment are the accurate component stress analysis and the identification of relevant cycles, using highly qualified and efficient cycle counting methods. component). Conventional and more advanced methodologies are subsequently applied for comparison purpose. The influence of the cycle counting method applied on the resulting partial fatigue usage factors are elaborated based on the given realistic plant data.

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Fatigue Monitoring Approaches for Power Plants

Cited by 3 publications

References 3 publications

Development of industrial process characterisation through data analysis

Development of industrial process characterisation through data analysis

A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

Fatigue Assessment of NPP Piping and Components Using Realistic Thermal-Mechanical Load Histories

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