<title>Fully quantitative predictive maintenance/inspection planning optimization for the utility/plant components</title>

This study focuses on the probabilistic analysis method to the determination of low cycle fatigue life for power plant components. The analysis incorporates standard life assessment modeling techniques used in the determination analysis of the low cycle fatigue. The probabilistic life assessment is developed to increase the reliability of life assessment. A probabilistic life assessment procedure can provide the engineer with the probability of structural failure as a function of operating time given the uncertainties in the input data. The probabilistic life assessment involves some uncertainties, for example, initial crack size, aspect ratio, crack initiation time, crack location, structural geometry, material properties, and loading condition, and a triangle distribution function is used for random variable generation. The resulting information provides the engineer with an assessment of the probability of structural failure. This information can form the basis of inspection planning and retirement-for-cause decisions. This study forms basis of the probabilistic life assessment technique and will be extended to other damage mechanisms.

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Reliability-Based Methods for Inspection Planning and Predictive Maintenance in Fixed Offshore Structures

Reliability-Based Analysis and Design of Structures and Infrastructure ◽

10.1201/9781003194613-27 ◽

2021 ◽

pp. 405-424

Author(s):

Tiago P. Ribeiro ◽

Ana M.L. Sousa

Keyword(s):

Offshore Structures ◽

Predictive Maintenance ◽

Inspection Planning

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Human reliability in non-destructive inspections of nuclear power plant components: modeling and analysis

Brazilian Journal of Radiation Sciences ◽

10.15392/bjrs.v7i2b.368 ◽

2019 ◽

Vol 7 (2B) ◽

Author(s):

Vanderley Vasconcelos ◽

Wellington Antonio Soares ◽

Raissa Oliveira Marques ◽

Silvério Ferreira Silva Jr ◽

Amanda Laureano Raso

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Human Factors ◽

Nuclear Power ◽

Power Plants ◽

Failure Modes ◽

Cooling System ◽

Human Reliability ◽

Non Destructive ◽

Plant Components

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. This inspection is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI is reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components, such as FMEA (Failure Modes and Effects Analysis) and THERP (Technique for Human Error Rate Prediction). An example by using qualitative and quantitative assessesments with these two techniques to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues, is presented.

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The Convergence between Predictive Maintenance and Augmented Reality to Aid Renewable Energy Equipment Availability

International Journal on Engineering Applications (IREA) ◽

10.15866/irea.v2i5.1916 ◽

2014 ◽

Vol 2 (5) ◽

pp. 152

Author(s):

José Manuel Torres Farinha ◽

Inácio Adelino Fonseca ◽

Rúben Silva Oliveira ◽

Fernando Maciel Barbosa

Keyword(s):

Renewable Energy ◽

Augmented Reality ◽

Predictive Maintenance ◽

Equipment Availability ◽

Energy Equipment

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Nonlinear Trend Analysis of Mill Fan System Vibrations for Predictive Maintenance and Diagnostics

International Journal of Electronics and Telecommunications ◽

10.2478/v10177-012-0048-9 ◽

2012 ◽

Vol 58 (4) ◽

pp. 351-356

Author(s):

Mincho B. Hadjiski ◽

Lyubka A. Doukovska ◽

Stefan L. Kojnov

Keyword(s):

Power Plant ◽

Trend Analysis ◽

Thermal Power Plant ◽

Power Plants ◽

Thermal Power ◽

Balkan Peninsula ◽

Predictive Maintenance ◽

Nonlinear Trend ◽

The Subject ◽

The Given

Abstract Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.

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