183 research outputs found

    Preliminary Failure Modes and Effects Analysis of the US DCLL Test Blanket Module

    Full text link
    This report presents the results of a preliminary failure modes and effects analysis (FMEA) of a small tritium-breeding test blanket module design for the International Thermonuclear Experimental Reactor. The FMEA was quantified with โ€œgenericโ€ component failure rate data, and the failure events are binned into postulated initiating event families and frequency categories for safety assessment. An appendix to this report contains repair time data to support an occupational radiation exposure assessment for test blanket module maintenance

    Preliminary Failure Modes and Effects Analysis of the US DCLL Test Blanket Module

    Full text link

    Guideline for Selection of Systems, Structures and Components to be considered in Ageing PSA

    Get PDF
    The guideline intends to provide a practical approach and to recommend the methods to be used in selection/prioritization of components, systems and structures (SSC) sensitive to ageing and important from risk point of view in operating nuclear power plants. The approach intends to ensure that the selection process will be carried out and documented in a uniform and consistent manner. The methods suitable for selection are briefly presented, and their advantages and disadvantages are specified. A list of generic ageing mechanisms, the factors favorable for their occurrence and some sensitive materials are provided in appendices. In the appendices are presented also the specific approaches and criteria used for SSC prioritization and selection in case studies performed in the frame of Ageing PSA task 3 activities. The guideline was developed in the frame of EC JRC Ageing PSA Network (APSA) activities.JRC.DDG.F.5-Safety of present nuclear reactor

    Development of maintenance framework for modern manufacturing systems

    Get PDF
    Modern manufacturing organizations are designing, building and operating large, complex and often โ€˜one of a kindโ€™ assets, which incorporate the integration of various systems under modern control systems. Due to such complexity, machines failures became more difficult to interpret and rectify and the existing maintenance strategies became obsolete without development and enhancement. As a result, the need for more advanced strategies to ensure effective maintenance applications that ensures high operation efficiency arise. The current research aims to investigate the existing maintenance strategies, the levels of machines complexity and automation within manufacturing companies from different sectors and sizes including, oil and gas, food and beverages, automotive, aerospace, and Original Equipment Manufacturer. Results analysis supports in the development of a modern maintenance framework that overcome the highlighted results and suits modern manufacturing assets using systematic approaches and utilisation of pillars from Total productive maintenance (TPM, Reliability Centred Maintenance (RCM) and Industry 4.0

    Maintenance models applied to wind turbines. A comprehensive overview

    Get PDF
    Producciรณn CientรญficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models

    A Framework for Remaining Useful Life Prediction of Steam Turbines Applicable to Various Data Types

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2018. 8. ์œค๋ณ‘๋™.์ตœ๊ทผ ๋ฐœ์ „์‚ฌ๊ฐ„ ๊ฒฝ์Ÿ์ด ์น˜์—ดํ•ด์ง์— ๋”ฐ๋ผ ๋ฐœ์ „ ์‚ฐ์—…์—์„œ๋Š” ์šด์ „ ๋น„์šฉ์„ ์ ˆ๊ฐํ•˜๊ณ  ํ•ต์‹ฌ ์„ค๋น„์˜ ์ˆ˜๋ช…์„ ์—ฐ์žฅํ•˜๋Š”๋ฐ ๋งŽ์€ ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์ด๊ณ  ์žˆ๋‹ค. ํ•œํŽธ ์šด์ „ ์‹œ๊ฐ„์ด ์„ค๊ณ„ ์ˆ˜๋ช…์— ๊ทผ์ ‘ํ•จ์— ๋”ฐ๋ผ ์ฆ๊ธฐํ„ฐ๋นˆ๊ณผ ๊ฐ™์€ ํ•ต์‹ฌ ์„ค๋น„์˜ ์—ดํ™”๊ฐ€ ๊ฐ€์†๋˜๊ณ  ํฌ๊ณ  ์ž‘์€ ๊ณ ์žฅ์ด ๋งŽ์ด ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ๊ฐ€์†ํ™”๋œ ์—ดํ™”๋‚˜ ์˜ˆ๊ธฐ์น˜ ๋ชปํ•œ ์†์ƒ์œผ๋กœ ๋ฐœ์ „์†Œ๊ฐ€ ์ •์ง€๋˜๋ฉด ๋ง‰๋Œ€ํ•œ ๊ฒฝ์ œ์  ์†์‹ค๊ณผ ๊ตญ๊ฐ€์ ์ธ ์žฌํ•ด๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์•ˆ์ •์ ์ธ ์„ค๋น„์˜ ์šด์ „์„ ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๋‹ค์–‘ํ•œ ๊ธฐ์ˆ ๋“ค์ด ๊ฐœ๋ฐœ๋˜๊ณ  ์žˆ์œผ๋ฉฐ ์ตœ๊ทผ ๋“ค์–ด ๋”์šฑ ๋งŽ์€ ๊ฐ๊ด‘์„ ๋ฐ›๊ณ  ์žˆ๋Š” ์‹œ์Šคํ…œ ๊ฑด์ „์„ฑ ๊ด€๋ฆฌ ๊ธฐ์ˆ ์€ ํšจ๊ณผ์ ์œผ๋กœ ์‹œ์Šคํ…œ์˜ ์ƒํƒœ๋ฅผ ๊ฐ์ง€, ์ง„๋‹จ, ๊ทธ๋ฆฌ๊ณ  ์˜ˆ์ง€ํ•˜์—ฌ ๊ด€๋ฆฌ์ž๊ฐ€ ์œ ์ง€ ๋ณด์ˆ˜์— ์žˆ์–ด ํ•„์š”ํ•œ ๊ฒฐ์ •์„ ๋‚ด๋ฆด ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ค€๋‹ค. ํŠนํžˆ ์ตœ์  ์œ ์ง€์ •๋น„ ๊ด€์ ์—์„œ ์ ํ•ฉํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ์˜ˆ์ธก๋œ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์€ ์„ค๋น„ ์ˆ˜๋ช…์— ์ •ํ™•ํ•œ ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํšจ๊ณผ์ ์ธ ์œ ์ง€ ์ •๋น„๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ์ฆ๊ธฐ ํ„ฐ๋นˆ์€ ๋ฐœ์ „์†Œ ์ˆ˜๋ช…์„ ๊ฒฐ์ •ํ•˜๋Š” ํ•ต์‹ฌ ์„ค๋น„์ด๊ธฐ ๋•Œ๋ฌธ์— ๋ฐœ์ „์†Œ์˜ ์ตœ์  ์šด์˜์„ ์œ„ํ•ด ํ™œ์šฉ ๊ฐ€๋Šฅํ•œ ์ •๋ณด๋ฅผ ์ตœ๋Œ€ํ•œ ํ™œ์šฉํ•˜์—ฌ ์šด์ „ ์ค‘์ธ ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฐœ๋ฐœ์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ด์— ๋ณธ ๋ฐ•์‚ฌํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” (1) ์ฆ๊ธฐ ํ„ฐ๋นˆ์— ๋Œ€ํ•œ ๊ณ ์žฅ๋ชจ๋“œ์˜ํ–ฅ๋ถ„์„๊ณผ ์—ฐ๊ณ„ํ•œ ์ž”์กด์œ ํšจ์ˆ˜๋ช… ์˜ˆ์ธก ํ”„๋ ˆ์ž„์›Œํฌ, ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ (2) ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ (๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ), (3) ํฌ๋ฆฌํ”„-ํ”ผ๋กœ ์†์ƒ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ณ ๋ คํ•œ ๋ชจ๋“œ ์˜์กด ์†์ƒ ๋ชจ๋ธ (๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ) ๋“ฑ์˜ ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ ์žฅ๋ชจ๋“œ์˜ํ–ฅ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ์˜ˆ์ธกํ•˜๋Š” ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ์ธก์ •๋œ ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•œ ๋ฐฉ๋ฒ•๋ก ๊ณผ ์†์ƒ ๋ชจ๋ธ์— ๊ธฐ๋ฐ˜ํ•œ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์˜คํ”„๋ผ์ธ์ด๋‚˜ ์˜จ๋ผ์ธ๊ณผ ๊ฐ™์ด ๋‹ค๋ฅธ ๋ชฉ์ ์œผ๋กœ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ์˜ˆ์ธกํ•  ๋•Œ ๋ถˆํ™•์‹ค๋„๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ๋ถˆํ™•์‹ค๋„๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜๋Š” ์ ˆ์ฐจ๋ฅผ ํฌํ•จํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ์„ ์ด์šฉํ•ด ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ž”์กด์œ ํšจ์ˆ˜๋ช…์„ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋Š” ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ์˜ ๊ฐœ๋ฐœ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ž”์กด์œ ํšจ์ˆ˜๋ช…์€ ์†์ƒ ์ธ์ž๋กœ๋ถ€ํ„ฐ ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ์„ ์—ฐ๊ณ„ํ•˜์—ฌ ์˜ˆ์ธกํ•œ๋‹ค. ํ˜„์žฅ์—์„œ ์ธก์ •๋œ ๊ฒฝ๋„๊ฐ’์œผ๋กœ๋ถ€ํ„ฐ ์†์ƒ์ธ์ž์˜ ํ™•๋ฅ ๋ถ„ํฌ๋ฅผ ์ถ”์ •ํ•˜๊ณ  ์†์ƒ์˜ ์„ฑ์žฅ์„ ํ‰๊ฐ€ํ•  ๋•Œ ๋ถˆํ™•์‹ค๋„๋ฅผ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด ๋ฒ ์ด์ง€์•ˆ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์†์ƒ ์„ฑ์žฅ ๋ชจ๋ธ์„ ํ†ตํ•ด ๊ธฐ์ €๋ถ€ํ•˜๋‚˜ ์ฒจ๋‘๋ถ€ํ•˜์— ์‚ฌ์šฉ๋˜๋Š” ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ์ข…๋ฅ˜์— ์ƒ๊ด€์—†์ด ์ •ํ™•ํ•œ ์ž”์กด์œ ํšจ์ˆ˜๋ช… ์˜ˆ์ธก์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•๋ก ์„ ์ด์šฉํ•ด ํฌ๋ฆฌํ”„์™€ ํ”ผ๋กœ ์ƒํ˜ธ์ž‘์šฉ์ด ๊ณ ๋ ค๋œ ๋ชจ๋“œ ๊ธฐ๋ฐ˜ ์†์ƒ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์†์ƒ๊ธฐ๊ตฌ์— ๋”ฐ๋ฅธ ์žฌ๋ฃŒ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ต๊ณ„์  ๊ธฐ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜๊ณ  ์‹ค ์ฆ๊ธฐํ„ฐ๋นˆ์˜ ํ˜•์ƒ ์ •๋ณด์™€ ์šด์ „์ •๋ณด๋ฅผ ์ด์šฉํ•ด ๊ธฐ์ €๋ถ€ํ•˜์™€ ์ฒจ๋‘๋ถ€ํ•˜ ํ„ฐ๋นˆ์„ ๋Œ€์ƒ์œผ๋กœ ํฌ๋ฆฌํ”„ ๋ฐ ํ”ผ๋กœ ์†์ƒ์œจ์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ฐ๊ฐ ๊ณ„์‚ฐ๋œ ์†์ƒ์œจ ๊ฒฐ๊ณผ์™€ ํฌ๋ฆฌํ”„-ํ”ผ๋กœ ์ƒํ˜ธ์ž‘์šฉ ๋ชจ๋ธ์„ ํ†ตํ•ด ์šด์ „๋ชจ๋“œ ๋˜๋Š” ์†์ƒ๋ชจ๋“œ์— ๋”ฐ๋ฅธ ์ฆ๊ธฐํ„ฐ๋นˆ์—์„œ์˜ ํฌ๋ฆฌํ”„์™€ ํ”ผ๋กœ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. Abstract i List of Tables viii List of Figures x Nomenclatures xiv Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 4 1.3 Dissertation Layout 7 Chapter 2 Literature Review 8 2.1 Life Prediction Methodologies of Steam Turbine 8 2.1.1 Destructive Method 11 2.1.2 Non-destructive Method 11 2.1.3 Analytical Method 13 2.1.4 Summary and Discussion 13 2.2 Data-driven and Model-based Life Prediction 15 2.2.1 Data-driven Approach 21 2.2.2 Model-based Approach 21 2.3 Empirical Model-based Life Prediction 15 2.3.1 On-site Data Measurement 18 2.3.2 Bayesian Inference 19 2.3.3 Summary and Discussion 20 2.4 Damage Model-based Life Prediction 21 2.4.1 Creep or Fatigue Damage Model Analysis 22 2.4.2 Creep-Fatigue Damage Summation Model analysis 23 2.4.3 Summary and Discussion 27 Chapter 3 A Practical RUL Prediction Framework of Steam Turbine with FMEA Analysis 28 3.1 Overview of Steam Turbines 28 3.2 FMEA for Steam Turbines 31 3.3 A Framework for RUL Prediction of Steam Turbine 34 3.4 Summay and Discussion 38 Chapter 4 A Bayesian Approach for RUL Prediction of Steam Turbines with Damage Growth Model 39 4.1 Characteristics of On-site Measurement Data 40 4.2 Measured Data based Damage Indices 46 4.3 Damage Growth Model using Sporadically Measured and Heterogeneous On-site Data 51 4.3.1 Proposed Damage Growth Model 51 4.3.2 Bayesian Updating Scheme of the Damage Growth Model 58 4.3.3 Damage Growth Model Updating 60 4.4 Predicting the Remaining Useful Life(RUL) of Steam Turbines 68 4.4.1 Damage Threshold 68 4.4.2 Validation of the Proposed Damage Growth Model 72 4.4.3 RUL Prediction 74 4.5 Summary and Discussion 78 Chapter 5 Mode-Dependent Damage Assessment for Steam Turbines with Creep-Fatigue Interaction Model 80 5.1 Dominant Damage Mechanisms of Steam Turbine 82 5.2 Typical Opeation Data of Steam Turbine 83 5.3 Dominant Damage Model of Steam Turbine 86 5.3.1 Creep Damage Model 86 5.3.2 Fatigue Damage Model 88 5.3.3 Creep-Fatigue Damage Model 90 5.4 Statiatical Damage Calculation for Steam Turbine 91 5.4.1 Statistical Characterization of Creep-Fatigue Damage Data 91 5.4.2 Creep Damage Calculation with Steady State Stress 94 5.4.3 Fatigue Damage Calculation with Transient Strain 95 5.5 Mode-Dependent Multiple Damage Interaction Model 100 5.5.1 Estimation of Damage Interaction Parameters 100 5.5.2 Validation of Mode-Dependent Model 101 5.5.3 Effect of Mode-dependence Effects on Multiple Damage 104 5.5.4 Case Study : Risk Assessment 108 5.6 Summary and Discussion 111 Chapter 6 Conclusions 113 6.1 Contributions and Impacts 113 6.2 Suggestions for Future Research 116 References 119 ๊ตญ๋ฌธ ์ดˆ๋ก 142Docto

    Methods and Systems for Fault Diagnosis in Nuclear Power Plants

    Get PDF
    This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

    Reliability estimates for selected sensors in fusion applications

    Full text link
    • โ€ฆ
    corecore