Complex System Reliability Analysis Method: Goal‐Oriented Methodology

Goal‐oriented (GO) methodology is a success‐oriented method for complex system reliability analysis based on modeling the normal operating sequence of a system and all possible system states. Recently, GO method has been applied in reliability and safety analysis of a number of systems, spanning defense, transportation, and power systems. This chapter provides a new approach for reliability analysis of complex systems, first, by providing its development history, its engineering applications, and the future directions. Then, the basic theory of GO method is expounded. Finally, the comparison of GO method, fault tree analysis and Monte‐Carlo simulation is discussed

Availability modeling and evaluation on high performance cluster computing systems

Cluster computing has been attracting more and more attention from both the industrial and the academic world for its enormous computing power, cost effective, and scalability. Beowulf type cluster, for example, is a typical High Performance Computing (HPC) cluster system. Availability, as a key attribute of the system, needs to be considered at the system design stage and monitored at mission time. Moreover, system monitoring is a must to help identify the defects and ensure the system\u27s availability requirement. In this study, novel solutions which provide availability modeling, model evaluation, and data analysis as a single framework have been investigated. Three key components in the investigation are availability modeling, model evaluation, and data analysis. The general availability concepts and modeling techniques are briefly reviewed. The system\u27s availability model is divided into submodels based upon their functionalities. Furthermore, an object oriented Markov model specification to facilitate availability modeling and runtime configuration has been developed. Numerical solutions for Markov models are examined, especially on the uniformization method. Alternative implementations of the method are discussed; particularly on analyzing the cost of an alternative solution for small state space model, and different ways for solving large sparse Markov models. The dissertation also presents a monitoring and data analysis framework, which is responsible for failure analysis and availability reconfiguration. In addition, the event logs provided from the Lawrence Livermore National Laboratory have been studied and applied to validate the proposed techniques

Reliability and fault tolerance in the European ADS project

After an introduction to the theory of reliability, this paper focuses on a description of the linear proton accelerator proposed for the European ADS demonstration project. Design issues are discussed and examples of cases of fault tolerance are given.Comment: 14 pages, contribution to the CAS - CERN Accelerator School: Course on High Power Hadron Machines; 24 May - 2 Jun 2011, Bilbao, Spai

FRAMEWORK FOR RELIABILITY, MAINTAINABILITY AND AVAILABILITY ANALYSIS OF GAS PROCESSING SYSTEM DURING OPERATION PHASE

In facing many operation challenges such as increased expectation in bottom line performances and escalating overhead costs, petrochemical plants nowadays need to continually strive for higher reliability and availability by means of effective improvement tools. Reliability, maintainability and availability (RAM) analysis has been recognised as one of the strategic tools to improve plant's reliability at operation phase. Nevertheless, the application of RAM among industrial practitioners is still limited generally due to the impracticality and complexity of existing approaches. Hence, it is important to enhance the approaches so that they can be practically applied by companies to assist them in achieving their operational goals. The objectives of this research are to develop frameworks for applying reliability, maintainability and availability analysis of gas processing system at operation phase to improve system operational and maintenance performances. In addition, the study focuses on ways to apply existing statistical approach and incorporate inputs from field experts for prediction of reliability related measures. Furthermore, it explores and highlights major issues involved in implementing RAM analysis in oil and gas industry and offers viable solutions. In this study, systematic analysis on each RAM components are proposed and their roles as strategic improvement and decision making tools are discussed and demonstrated using case studies of two plant systems. In reliability and maintainability (R&M) analysis, two main steps; exploratory and inferential are proposed. Tools such as Pareto, trend plot and hazard functions; Kaplan Meier (KM) and proportional hazard model (PHM), are used in exploratory phase to identify critical elements to system's R&M performances. In inferential analysis, a systematic methodology is presented to assess R&M related measures

Automatic phased mission system reliability model generation

There are many methods for modelling the reliability of systems based on component failure data. This task becomes more complex as systems increase in size, or undertake missions that comprise multiple discrete modes of operation, or phases. Existing techniques require certain levels of expertise in the model generation and calculation processes, meaning that risk and reliability assessments of systems can often be expensive and time-consuming. This is exacerbated as system complexity increases. This thesis presents a novel method which generates reliability models for phasedmission systems, based on Petri nets, from simple input files. The process has been automated with a piece of software designed for engineers with little or no experience in the field of risk and reliability. The software can generate models for both repairable and non-repairable systems, allowing redundant components and maintenance cycles to be included in the model. Further, the software includes a simulator for the generated models. This allows a user with simple input files to perform automatic model generation and simulation with a single piece of software, yielding detailed failure data on components, phases, missions and the overall system. A system can also be simulated across multiple consecutive missions. To assess performance, the software is compared with an analytical approach and found to match within 5% in both the repairable and non-repairable cases. The software documented in this thesis could serve as an aid to engineers designing new systems to validate the reliability of the system. This would not require specialist consultants or additional software, ensuring that the analysis provides results in a timely and cost-effective manner

Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library

Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three di erent case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

Fault-tolerant computer study

A set of building block circuits is described which can be used with commercially available microprocessors and memories to implement fault tolerant distributed computer systems. Each building block circuit is intended for VLSI implementation as a single chip. Several building blocks and associated processor and memory chips form a self checking computer module with self contained input output and interfaces to redundant communications buses. Fault tolerance is achieved by connecting self checking computer modules into a redundant network in which backup buses and computer modules are provided to circumvent failures. The requirements and design methodology which led to the definition of the building block circuits are discussed

Availability estimation and management for complex processing systems

“Availability” is the terminology used in asset intensive industries such as petrochemical and hydrocarbons processing to describe the readiness of equipment, systems or plants to perform their designed functions. It is a measure to suggest a facility’s capability of meeting targeted production in a safe working environment. Availability is also vital as it encompasses reliability and maintainability, allowing engineers to manage and operate facilities by focusing on one performance indicator. These benefits make availability a very demanding and highly desired area of interest and research for both industry and academia. In this dissertation, new models, approaches and algorithms have been explored to estimate and manage the availability of complex hydrocarbon processing systems. The risk of equipment failure and its effect on availability is vital in the hydrocarbon industry, and is also explored in this research. The importance of availability encouraged companies to invest in this domain by putting efforts and resources to develop novel techniques for system availability enhancement. Most of the work in this area is focused on individual equipment compared to facility or system level availability assessment and management. This research is focused on developing an new systematic methods to estimate system availability. The main focus areas in this research are to address availability estimation and management through physical asset management, risk-based availability estimation strategies, availability and safety using a failure assessment framework, and availability enhancement using early equipment fault detection and maintenance scheduling optimization

FRAMEWORK FOR RELIABILITY, MAINTAINABILITY AND AVAILABILITY ANALYSIS OF GAS PROCESSING SYSTEM DURING OPERATION PHASE

In facing many operation challenges such as increased expectation in bottom line performances and escalating overhead costs, petrochemical plants nowadays need to continually strive for higher reliability and availability by means of effective improvement tools. Reliability, maintainability and availability (RAM) analysis has been recognised as one of the strategic tools to improve plant's reliability at operation phase. Nevertheless, the application of RAM among industrial practitioners is still limited generally due to the impracticality and complexity of existing approaches. Hence, it is important to enhance the approaches so that they can be practically applied by companies to assist them in achieving their operational goals. The objectives of this research are to develop frameworks for applying reliability, maintainability and availability analysis of gas processing system at operation phase to improve system operational and maintenance performances. In addition, the study focuses on ways to apply existing statistical approach and incorporate inputs from field experts for prediction of reliability related measures. Furthermore, it explores and highlights major issues involved in implementing RAM analysis in oil and gas industry and offers viable solutions. In this study, systematic analysis on each RAM components are proposed and their roles as strategic improvement and decision making tools are discussed and demonstrated using case studies of two plant systems. In reliability and maintainability (R&M) analysis, two main steps; exploratory and inferential are proposed. Tools such as Pareto, trend plot and hazard functions; Kaplan Meier (KM) and proportional hazard model (PHM), are used in exploratory phase to identify critical elements to system's R&M performances. In inferential analysis, a systematic methodology is presented to assess R&M related measures