Multi-State System Reliability: A New and Systematic Review

AbstractReliability analysis considering multiple possible states is known as multi-state (MS) reliability analysis. Multi-state system reliability models allow both the system and its components to assume more than two levels of performance. Through multi-state reliability models provide more realistic and more precise representations of engineering systems, they are much more complex and present major difficulties in system definition and performance evaluation. MSS reliability has received a substantial amount of attention in the past four decades. This article presents a new and systematic review about multi-state system reliability. A timely review is an effective work related to improving the development of MSS theory. The review about the latest studies and advances about multi-state system reliability evaluation, multi-state systems optimization and multi-state systems maintenance is summarized in this paper

AVAILABILITY MODEL FOR A COG EN ERA TION SYSTEM SUBJECTED TO REDUNDANCY

The main emphasis of cogeneration system is to provide electrical energy, steam, hot and chilled water to their customers. The failure of this system could lead to the disruption of the supply of these items. If failure occurs, it will result in reduction of availability as well as economic loss. In order to mitigate such effects, it is required to study availability of the cogeneration system together with associated economic loss. However, there are factors which affect the availability assessment of the cogeneration system. These factors are system redundancy and limitation of maintenance data. Use of redundancy in cogeneration helps to achieve higher availability but the operation cost of redundancy is expensive due to maximum demand charge cost. Thus, it is important to consider the economic effect of redundancy

Multi-State Reliability Analysis of Nuclear Power Plant Systems

The probabilistic safety assessment of engineering systems involving high-consequence low-probability events is stochastic in nature due to uncertainties inherent in time to an event. The event could be a failure, repair, maintenance or degradation associated with system ageing. Accurate reliability prediction accounting for these uncertainties is a precursor to considerably good risk assessment model. Stochastic Markov reliability models have been constructed to quantify basic events in a static fault tree analysis as part of the safety assessment process. The models assume that a system transits through various states and that the time spent in a state is statistically random. The system failure probability estimates of these models assuming constant transition rate are extensively utilized in the industry to obtain failure frequency of catastrophic events. An example is core damage frequency in a nuclear power plant where the initiating event is loss of cooling system. However, the assumption of constant state transition rates for analysis of safety critical systems is debatable due to the fact that these rates do not properly account for variability in the time to an event. An ill-consequence of such an assumption is conservative reliability prediction leading to addition of unnecessary redundancies in modified versions of prototype designs, excess spare inventory and an expensive maintenance policy with shorter maintenance intervals. The reason for this discrepancy is that a constant transition rate is always associated with an exponential distribution for the time spent in a state. The subject matter of this thesis is to develop sophisticated mathematical models to improve predictive capabilities that accurately represent reliability of an engineering system. The generalization of the Markov process called the semi-Markov process is a well known stochastic process, yet it is not well explored in the reliability analysis of nuclear power plant systems. The continuous-time, discrete-state semi-Markov process model is a stochastic process model that describes the state transitions through a system of integral equations which can be solved using the trapezoidal rule. The primary objective is to determine the probability of being in each state. This process model ensures that time spent in the states can be represented by a suitable non-exponential distribution thus capturing the variability in the time to event. When exponential distribution is assumed for all the state transitions, the model reduces to the standard Markov model. This thesis illustrates the proposed concepts using basic examples and then develops advanced case studies for nuclear cooling systems, piping systems, digital instrumentation and control (I&C) systems, fire modelling and system maintenance. The first case study on nuclear component cooling water system (NCCW) shows that the proposed technique can be used to solve a fault tree involving redundant repairable components to yield initiating event probability quantifying the loss of cooling system. The time-to-failure of the pump train is assumed to be a Weibull distribution and the resulting system failure probability is validated using a Monte Carlo simulation of the corresponding reliability block diagram. Nuclear piping systems develop flaws, leaks and ruptures due to various underlying damage mechanisms. This thesis presents a general model for evaluating rupture frequencies of such repairable piping systems. The proposed model is able to incorporate the effect of aging related degradation of piping systems. Time dependent rupture frequencies are computed and the influence of inspection intervals on the piping rupture probability is investigated. There is an increasing interest worldwide in the installation of digital instrumentation and control systems in nuclear power plants. The main feedwater valve (MFV) controller system is used for regulating the water level in a steam generator. An existing Markov model in the literature is extended to a semi-Markov model to accurately predict the controller system reliability. The proposed model considers variability in the time to output from the computer to the controller with intrinsic software and mechanical failures. State-of-the-art time-to-flashover fire models used in the nuclear industry are either based on conservative analytical equations or computationally intensive simulation models. The proposed semi-Markov based case study describes an innovative fire growth model that allows prediction of fire development and containment including time to flashover. The model considers variability in time when transiting from one stage of the fire to the other. The proposed model is a reusable framework that can be of importance to product design engineers and fire safety regulators. Operational unavailability is at risk of being over-estimated because of assuming a constant degradation rate in a slowly ageing system. In the last case study, it is justified that variability in time to degradation has a remarkable effect on the choice of an effective maintenance policy. The proposed model is able to accurately predict the optimal maintenance interval assuming a non-exponential time to degradation. Further, the model reduces to a binary state Markov model equivalent to a classic probabilistic risk assessment model if the degradation and maintenance states are eliminated. In summary, variability in time to an event is not properly captured in existing Markov type reliability models though they are stochastic and account for uncertainties. The proposed semi-Markov process models are easy to implement, faster than intensive simulations and accurately model the reliability of engineering systems

A hybrid load flow and event driven simulation approach to multi-state system reliability evaluation

Structural complexity of systems, coupled with their multi-state characteristics, renders their reliability and availability evaluation difficult. Notwithstanding the emergence of various techniques dedicated to complex multi-state system analysis, simulation remains the only approach applicable to realistic systems. However, most simulation algorithms are either system specific or limited to simple systems since they require enumerating all possible system states, defining the cut-sets associated with each state and monitoring their occurrence. In addition to being extremely tedious for large complex systems, state enumeration and cut-set definition require a detailed understanding of the system׳s failure mechanism. In this paper, a simple and generally applicable simulation approach, enhanced for multi-state systems of any topology is presented. Here, each component is defined as a Semi-Markov stochastic process and via discrete-event simulation, the operation of the system is mimicked. The principles of flow conservation are invoked to determine flow across the system for every performance level change of its components using the interior-point algorithm. This eliminates the need for cut-set definition and overcomes the limitations of existing techniques. The methodology can also be exploited to account for effects of transmission efficiency and loading restrictions of components on system reliability and performance. The principles and algorithms developed are applied to two numerical examples to demonstrate their applicability

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

Reliability monitoring techniques applied to a hot strip steel mill

Reliability engineering techniques have been used in the manufacturing environment for many years. However the reliability analysis of repairable systems is not so widely practised in the steel manufacturing environment. Many different analysis methods have been proposed for the modelling of repairable systems, most of these have had limited application in the manufacturing environment. The current reliability analysis techniques are predominantly used by engineers to construct a “snapshot” in time of a manufacturing system’s reliability status. There are no readily identifiable applications of reliability modelling techniques being applied to repairable systems over a long time period within the manufacturing environment The aim of this work is to construct a method which can analyse and monitor the reliability status of multiple repairable systems within the steel plant over an extended operating period. The developed analysis method is predominantly automated and is facilitated by applying standard reliability analysis techniques to all of the repairable systems failure data sets under review. This Thesis illuminates the methodology used to fulfil the remit of this research by the following sequential steps: Developing a new methodology for the application of reliability analysis techniques to repairable systems within a steel manufacturing facility Utilised an innovative step of combining three reliability analysis methods as complimentary activities Constructed an automated reliability analysis model which fulfils the project remit. In addition the model is capable of the long term monitoring of repairable system reliability The new reliability analysis method has been delivered to Tata Steel and is installed in the Port Talbot Technology Group with a direct link to the Hot Strip Mill (HSM) monitoring database. This reliability analysis method has been tested with four years operational data from the Hot Strip Mill manufacturing area and the analysis has shown that changes and trends in all systems reliability status can be easily identified.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Reliability monitoring techniques applied to a hot strip steel mill

Reliability engineering techniques have been used in the manufacturing environment for many years. However the reliability analysis of repairable systems is not so widely practised in the steel manufacturing environment. Many different analysis methods have been proposed for the modelling of repairable systems, most of these have had limited application in the manufacturing environment. The current reliability analysis techniques are predominantly used by engineers to construct a “snapshot” in time of a manufacturing system’s reliability status. There are no readily identifiable applications of reliability modelling techniques being applied to repairable systems over a long time period within the manufacturing environment The aim of this work is to construct a method which can analyse and monitor the reliability status of multiple repairable systems within the steel plant over an extended operating period. The developed analysis method is predominantly automated and is facilitated by applying standard reliability analysis techniques to all of the repairable systems failure data sets under review. This Thesis illuminates the methodology used to fulfil the remit of this research by the following sequential steps: Developing a new methodology for the application of reliability analysis techniques to repairable systems within a steel manufacturing facility Utilised an innovative step of combining three reliability analysis methods as complimentary activities Constructed an automated reliability analysis model which fulfils the project remit. In addition the model is capable of the long term monitoring of repairable system reliability The new reliability analysis method has been delivered to Tata Steel and is installed in the Port Talbot Technology Group with a direct link to the Hot Strip Mill (HSM) monitoring database. This reliability analysis method has been tested with four years operational data from the Hot Strip Mill manufacturing area and the analysis has shown that changes and trends in all systems reliability status can be easily identified