Unavailability model for demand-caused failures of safety components addressing degradation by demand-induced stress, maintenance effectiveness and test efficiency

[EN] The reliability, availability and maintainability (RAM) modelling of safety equipment has long been a topic of major concern. Some RAM models have focused on explicitly addressing the effect of component degradation and surveillance and maintenance policies, searching for an optimum level of the safety component RAM by adjusting surveillance and maintenance related parameters. As regards the reliability contribution, these components normally have two main types of failure mode that contribute to the probability of failure on demand (PFD): (1) by demand-caused and (2) standby-related failures. The former is normally associated with a demand failure probability, which is affected by the degradation caused by demand-related stress. Surveillance testing therefore not only introduces a positive effect, but also an adverse one, which it compensates by performing maintenance activities to eliminate or reduce the accumulated degradation. This paper proposes a new model for the demand failure probability that explicitly addresses all aspects of the effect of demand-induced stress (mostly test-induced stress), maintenance effectiveness (PAS or PAR model) and test efficiency. A case study is included on an application to a typical motor-operated valve in a nuclear power plant.The authors are grateful to the Spanish Ministry of Science and Innovation for the financial support received (Research Projects ENE2013-45540-R and ENE2016-80401-R) and the doctoral scholarship awarded (BES-2014-067602). The study also received financial support from the Spanish Research Agency and the European Regional Development Fund.Martorell-Aygues, P.; Martón Lluch, I.; Sánchez Galdón, AI.; Martorell Alsina, SS. (2017). Unavailability model for demand-caused failures of safety components addressing degradation by demand-induced stress, maintenance effectiveness and test efficiency. Reliability Engineering & System Safety. 168:18-27. https://doi.org/10.1016/j.ress.2017.05.044S182716

Parameter Estimation of a Reliability Model of Demand-Caused and Standby-Related Failures of Safety Components Exposed to Degradation by Demand Stress and Ageing That Undergo Imperfect Maintenance

[EN] One can find many reliability, availability, and maintainability (RAM) models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD) of safety equipment: (1) by demand-caused and (2) standby-related failures. It proposes a maximum likelihood estimation (MLE) approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.The authors are grateful to the Spanish Ministry of Science and Innovation for the financial support received (Research Project ENE2016-80401-R) and the doctoral scholarship awarded (BES-2014-067602). The study also received financial support from the Spanish Research Agency and the European Regional Development Fund.Martorell Alsina, SS.; Martorell-Aygues, P.; Sánchez Galdón, AI.; Mullor, R.; Martón Lluch, I. (2017). Parameter Estimation of a Reliability Model of Demand-Caused and Standby-Related Failures of Safety Components Exposed to Degradation by Demand Stress and Ageing That Undergo Imperfect Maintenance. Mathematical Problems in Engineering. (7042453):1-11. https://doi.org/10.1155/2017/7042453S111704245

Prognosis of wear-out effect on of safety equipment reliability for nuclear power plants long-term safe operation

To reach a Net Zero Emission scenario, on 1st January 2022 the European Union (EU) declared nuclear and gas as transitional activities under strict safety conditions. A central challenge is that many nuclear reactors in operation are close to or have reached their design life, so that, it is required to demonstrate the influence of equipment ageing on plant reliability will be kept under control in the plan extended lifetime. In this work a three-step methodology is proposed to obtain the time instants at which the failure rate behaviour changes (break points) and the most appropriate age-dependant reliability model to explicitly include the effects of ageing and maintenance. The methodology requires the reliability parameters estimation at each phase of the plant equipment lifetime, what is carried out by using the available Nuclear Power Plant (NPP) historical data, which is quite scarce. The methodology is applied to a motor operated valve of a NPP safety system. The results demonstrate the capability of the approach proposed to estimate and predict the component reliability in the plant extended lifetime depending on the maintenance policy implemented, being necessary to estimate an accurate age-dependant reliability model to support the decision-making process on equipment ageing management.Grant PID2019-110590RB-I00 funded by MCIN/AEI/10.13039/501100011033 “ERDF A way of making Europe”

Development of an ontology supporting failure analysis of surface safety valves used in Oil & Gas applications

Treball desenvolupat dins el marc del programa 'European Project Semester'.The project describes how to apply Root Cause Analysis (RCA) in the form of a Failure Mode Effect and Criticality Analysis (FMECA) on hydraulically actuated Surface Safety Valves (SSVs) of Xmas trees in oil and gas applications, in order to be able to predict the occurrence of failures and implement preventive measures such as Condition and Performance Monitoring (CPM) to improve the life-span of a valve and decrease maintenance downtime. In the oil and gas industry, valves account for 52% of failures in the system. If these failures happen unexpectedly it can cause a lot of problems. Downtime of the oil well quickly becomes an expensive problem, unscheduled maintenance takes a lot of extra time and the lead-time for replacement parts can be up to 6 months. This is why being able to predict these failures beforehand is something that can bring a lot of benefits to a company. To determine the best course of action to take in order to be able to predict failures, a FMECA report is created. This is an analysis where all possible failures of all components are catalogued and given a Risk Priority Number (RPN), which has three variables: severity, detectability and occurrence. Each of these is given a rating between 0 and 10 and then the variables are multiplied with each other, resulting in the RPN. The components with an RPN above an acceptable risk level are then further investigated to see how to be able to detect them beforehand and how to mitigate the risk that they pose. Applying FMECA to the SSV mean breaking the system down into its components and determining the function, dependency and possible failures. To this end, the SSV is broken up into three sub-systems: the valve, the actuator and the hydraulic system. The hydraulic system is the sub-system of the SSV responsible for containing, transporting and pressurizing of the hydraulic fluid and in turn, the actuator. It also contains all the safety features, such as pressure pilots, and a trip system in case a problem is detected in the oil line. The actuator is, as the name implies, the sub-system which opens and closes the valve. It is made up of a number of parts such as a cylinder, a piston and a spring. These parts are interconnected in a number of ways to allow the actuator to successfully perform its function. The valve is the actual part of the system which interacts with the oil line by opening and closing. Like the actuator, this sub-system is broken down into a number of parts which work together to perform its function. After breaking down and defining each subsystem on a functional level, a model was created using a functional block diagram. Each component also allows for the defining of dependencies and interactions between the different components and a failure diagram for each component. This model integrates the three sub-systems back into one, creating a complete picture of the entire system which can then be used to determine the effects of different failures in components to the rest of the system. With this model completed we created a comprehensive FMECA report and test the different possible CPM solutions to mitigate the largest risks

Operating and maintenance cost reduction using probabilistic risk assessment (PRA)

"January 1992."Includes bibliographical references (pages 129-132)Final report, "Operating and maintenance cost reduction using probabilistic risk assessment (PRA)"This study quantifies the change in one measure of plant risk, the frequency of loss of long-term decay heat removal, due to changes in maintenance at the James A. Fitzpatrick (JAF) plant. Quantification is accomplished in two steps. First, the effects of maintenance are quantified in terms of changes in: a) the frequency of common cause failure of residual heat removal (RHR) pumps and b) the frequency with which operators fail to correctly restore the RHR system following maintenance. These parameters are selected as the result of an importance analysis for the plant. Second, the changes in these two parameters are propagated through a simple plant model to obtain the associated change in plant risk. Based on this study's assessment of the current maintenance program at JAF, it appears that the potential for significant risk reduction due to improved maintenance is not extremely large; an optimal program might lead to an 80% reduction. The optimal program would place a stronger emphasis on predictive maintenance, and would employ improved procedures for RHR pump maintenance. There is potential for significant risk increase (around a factor of 70) if the maintenance program is significantly degraded (e.g., if post-maintenance is deemphasized). This study shows how, at a simple level, maintenance program changes can be quantified without explicit modeling of the details of a plant's management and organizational structure. However, such modeling may be required: a) to more strongly justify the quantitative factors used in the analysis and b) to quantify the effect of other program changes not yet treated (e.g., the strengthening of program elements ensuring feedback of information to organization). In addition, failure data specific to the JAF plant are also needed to increase the confidence in the quantitative results of this study.Sponsored by New York Power Authority, White Plains, NY under contract no. S-90-0019

Strategies for maintenance management of railway track assets

M.Ing. (Engineering Management)Abstract: Population growth and environmental issues are revitalizing the railway sector in a tremendous way. An increase in frequency of passenger traffic and rising loads of freight trains has an impact on dynamic railway track properties and components thereof. The challenge from the railway fraternity is to rise to the challenge by ensuring a safe, reliable and affordable mode of transport. The purpose of this research is to investigate the capacity needed to meet demand by maintaining the track components of the railway infrastructure cost effectively. The railway track is the most critical in terms of safety, influence on maintenance costs, availability and reliability of the train service. Profillidis (2012) highlights the fact that track maintenance expenses represent a significant percentage of total railway infrastructure expenses. In literature, different maintenance strategies, approaches and concepts are discussed in light with arguments raised by different scholars and researchers. The main research methodology utilised was the case study on maintenance strategies from different countries where data was mostly available. The reason for the chosen method was to standardise the research method across different countries as this made it easy to obtain the findings and arrive at recommendations of the research. The broader findings from different maintenance strategies were that the track maintenance approach still has to evolve from working in silos to working in a system that acknowledges that decisions taken from other departments can affect the quality of maintenance in future. The deterioration of the track system is mostly affected by the initial quality of the railway track after commissioning due to workmanship and track design, maintenance approach, type of rolling stock tonnages, speed of rolling stock, and environmental related issues. Design phase of the track acknowledges the systems thinking approach for quality and structural integrity. However, more can still be done to adopt approaches that foster inter-departmental coordination in the maintenance phase of the railway track asset lifecycle. Transnet faces a challenge of fulfilling its obligation by providing quality and cost effective maintenance to increase the reliability, affordability, availability and safety of its infrastructure with the ever-increasing freight volumes. The traditional approach of maintaining railway track assets does not bring in required outcomes that ensure high quality and cost effective maintenance as required by high intensity asset utilisation. Data collected from the..

Advances in Reliability, Risk and Safety Analysis with Big Data: Proceedings of the 57th ESReDA Seminar: Hosted by the Technical University of Valencia, 23-24 October, 2019, Valencia, Spain

The publication presents 57th Seminar organized by ESReDA that took place at the Polytechnic University of Valencia/Universitat Politècnica de Valencia, Spain. The Seminar was jointly organized by ESReDA and CMT Motores Termicos, a research unit at the Polytechnic University of Valencia. In accordance with the theme proposed for the Seminar, communications were presented that made it possible to discuss and better understand the role of the latest big data, machine learning and artificial intelligence technologies in the development of reliability, risk and safety analyses for industrial systems. The world is moving fast towards wide applications of big data techniques and artificial intelligence is considered to be the future of our societies. Rapid development of 5G telecommunications infrastructure would only speed up deployment of big data analytic tools. However, despite the recent advances in the these fields, there is still a long way to go for integrated applications of big data, machine learning and artificial intelligence tools in business practice. We would like to express our gratitude to the authors and key note speakers in particular and to all those who shared with us these moments of discussion on subjects of great importance and topicality for the members of ESReDA. The editorial work for this volume was supported by the Joint Research Centre of the European Commission in the frame of JRC support to ESReDA activities.JRC.C.3-Energy Security, Distribution and Market

Critical Infrastructures: Enhancing Preparedness & Resilience for the Security of Citizens and Services Supply Continuity: Proceedings of the 52nd ESReDA Seminar Hosted by the Lithuanian Energy Institute & Vytautas Magnus University

Critical Infrastructures Preparedness and Resilience is a major societal security issue in modern society. Critical Infrastructures (CIs) provide vital services to modern societies. Some CIs’ disruptions may endanger the security of the citizen, the safety of the strategic assets and even the governance continuity. The European Safety, Reliability and Data Association (ESReDA) as one of the most active EU networks in the field has initiated a project group on the “Critical Infrastructure/Modelling, Simulation and Analysis – Data”. The main focus of the project group is to report on the state of progress in MS&A of the CIs preparedness & resilience with a specific focus on the corresponding data availability and relevance. In order to report on the most recent developments in the field of the CIs preparedness & resilience MS&A and the availability of the relevant data, ESReDA held its 52nd Seminar on the following thematic: “Critical Infrastructures: Enhancing Preparedness & Resilience for the security of citizens and services supply continuity”. The 52nd ESReDA Seminar was a very successful event, which attracted about 50 participants from industry, authorities, operators, research centres, academia and consultancy companies.JRC.G.10-Knowledge for Nuclear Security and Safet