Reliability, Availability and Maintainability (RAM) Analysis for Offshore High Pressure Compressor

Reliability, Availability and Maintainability (RAM) helps in optimizing performance of equipment. The availability can be improved by the enhancement of the reliability and maintainability. Equipment failure in offshore facilities are difficult to be predicted hence sudden failure of an equipment lead to reduction in output, loss of production and high maintenance cost due to unplanned maintenance. This study examined and analysed the failure mode of high pressure compressor at offshore platform in order to identify its critical failure mode. Failure and repair data are utilized to determine reliability and maintainability of the high pressure compressor. Reliability and maintainability analysis was carried out with the aid of Reliasoft Weibull++ software to obtain the required parameters while ReliaSoft BlockSim software was used for reliability block diagram (RBD) construction and simulation to obtain the availability of the high pressure compressor. The developed model can improve the performance of the high pressure compressor since it is validated with the actual model. From this RAM analysis, the overall performance of high pressure compressor can be increase by conducting Root Cause Failure Analysis (RCFA) which focusing on the most critical failure mode. The optimization of maintenance schedule can lead to the reduction of maintenance cost

A STRUCTURED FRAMEWORK FOR RELIABILITY AND RISK EVALUATION IN THE MILK PROCESS INDUSTRY UNDER FUZZY ENVIRONMENT

This paper aims at proposing a novel integrated framework for studying reliability and risk issues of the curd unit in a milk process industry under uncertain environment. The considered plant’s complex series-parallel configuration was presented using the Petri Net (PN) modeling. The Fuzzy Lambda-Tau (λ-τ) approach was applied to study and analyze the reliability aspects of the considered plant. Failure dynamics of the curd unit has been analyzed with respect to increasing/ decreasing trends of the tabulated reliability indices. Availability of the considered plant shows a decreasing trend with an increase in spread values. For improving the system’s availability, a risk analysis was done to identify the most critical failure causes. Using the traditional FMEA approach, the FMEA sheet was generated on the basis of expert’s knowledge/experience. The Fuzzy-Complex Proportional Assessment (FCOPRAS) approach was applied within FMEA approach for identification of critical failure causes associated with different subsystem/components of the considered plant. In order to check the consistency of the ranking results, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) was applied within the FCOPRAS approach. Ranking results are compared for checking consistency and robustness of critical failure causes related decision making which would be useful in designing the finest maintenance schedule for the considered curd unit.  Overheating/moisture lead to winding failure (MSCP5), visible sediment of milk jam in filter (MBFP3), improper quality of oil (H4), blade breakage (CTK4), wearing in gears (PFM11), and cylinder leakage (CFM7) were recognized as the most critical failure causes contributing to system unavailability. The analysis results were supplied to the maintenance manager for framing a suitable time-based maintenance intervals policy for the considered unit

Reliability, Availability, Maintainability (RAM) study, on reciprocating compressors API 618

Abstract The Oil & Gas industry has continuously increased its requirements and together with the high complexity of technological systems and the higher competitiveness of markets, has compelled providers to implement adequate management strategies for these systems in order to improve their availability and productivity to meet those more demanding criteria. In this context, the complex of RAM factors constitute a strategic approach for integrating reliability, availability and maintainability, by using methods, tools and engineering techniques (Mean Time to Failure, Equipment down Time and System Availability values) to identify and quantify equipment and system failures that prevent the achievement of the productive objectives. The application of such methodologies requires a deep experience and know-how together with the possibility of acquiring and processing data in operating conditions. This paper presents the most relevant aspects and findings of a study conducted for assessing the operational performance of a reciprocating compressor system package installed and used in the oil and gas' industries. The study was based on the analysis of the behaviour of states defined for each individual parts and component of reciprocating compressor and also aimed to identify and evaluate the effects of RAM-type factors and was conducted in collaboration with a private company that, for privacy reasons, will be named RC company. The Methodologies procedures used in this descriptive study were the bibliographical research, documentary and content analysis of the main literature. Adopting the most suitable maintenance strategy is one of the main challenges that maintenance managers face. The main purpose of this work is to propose a new approach to evaluate maintenance strategies. In this study, three criteria called reliability, availability and maintainability (RAM) have been employed to compare to future maintenance strategies

RAM factors in the operation and maintenance phase of wind turbines

The high complexity of technological systems and the increasing requirement and competitiveness of markets request the implementation of adequate management strategies for these systems in order to improve their availability and productivity. In this context, RAM factors constitute a strategic approach for integrating reliability, availability and maintainability, by using methods, tools and engineering techniques to identify and quantify equipment and system failures that prevent the achievement of its objectives. This paper presents the most relevant aspects and findings of a study conducted for assessing the operational performance of a wind turbine system installed in a wind farm in Portugal. The study was based on the analysis of the behavior of states defined for each individual wind turbine over a period of two years, and was aimed to identify and evaluate the effects of RAM-type factors. Given the structure and nature of the data, a Markov Chain approach was adopted for this evaluation. The main finding was that the usage of a particular technique (the frequency and duration technique) is adequate to effectively evaluate the overall performance of the wind farm and find opportunities for improvements.This work is financed with FEDER Funds by Programa Operacional Fatores de Competitividade – COMPETE and by National Funds by FCT – Fundação para a Ciência e Tecnologia, Project: FCOMP-01-0124-FEDER- 02267

Reliability, Availability and Maintainability (RAM) Analysis for Offshore High Pressure Compressor

Reliability, Availability and Maintainability (RAM) helps in optimizing performance of equipment. The availability can be improved by the enhancement of the reliability and maintainability. Equipment failure in offshore facilities are difficult to be predicted hence sudden failure of an equipment lead to reduction in output, loss of production and high maintenance cost due to unplanned maintenance. This study examined and analysed the failure mode of high pressure compressor at offshore platform in order to identify its critical failure mode. Failure and repair data are utilized to determine reliability and maintainability of the high pressure compressor. Reliability and maintainability analysis was carried out with the aid of Reliasoft Weibull++ software to obtain the required parameters while ReliaSoft BlockSim software was used for reliability block diagram (RBD) construction and simulation to obtain the availability of the high pressure compressor. The developed model can improve the performance of the high pressure compressor since it is validated with the actual model. From this RAM analysis, the overall performance of high pressure compressor can be increase by conducting Root Cause Failure Analysis (RCFA) which focusing on the most critical failure mode. The optimization of maintenance schedule can lead to the reduction of maintenance cost

Resilience, Reliability, and Recoverability (3Rs)

Recent natural and human-made disasters, mortgage derivatives crises, and the need for stable systems in different areas have renewed interest in the concept of resilience, especially as it relates to complex industrial systems with mechanical failures. This concept in the engineering systems (infrastructure) domain could be interpreted as the probability that system conditions exceed an irrevocable tipping point. But the probability in this subject covers the different areas that different approaches and indicators can evaluate. In this context, reliability engineering is used the reliability (uptime) and recoverability (downtime) indicators (or performance indicators) as the most useful probabilistic tools for performance measurement. Therefore, our research penalty area is the resilience concept in combination with reliability and recoverability. It must be said that the resilience evaluators must be considering a diversity of knowledge sources. In this thesis, the literature review points to several important implications for understanding and applying resilience in the engineering area and The Arctic condition. Indeed, we try to understand the application and interaction of different performance-based resilience concepts. In this way, a collection of the most popular performance-based resilience analysis methods with an engineering perspective is added as a state-of-the-art review. The performance indicators studies reveal that operational conditions significantly affect the components, industry activities, and infrastructures performance in various ways. These influential factors (or heterogeneity) can broadly be studied into two groups: observable and unobservable risk factors in probability analysis of system performance. The covariate-based models (regression), such as proportional hazard models (PHM), and their extent are the most popular methods for quantifying observable and unobservable risk factors. The report is organized as follows: After a brief introduction of resilience, chapters 2,3 priorly provide a comprehensive statistical overview of the reliability and recoverability domain research by using large scientific databases such as Scopus and Web of Science. As the first subsection, a detailed review of publications in the reliability and recoverability assessment of the engineering systems in recent years (since 2015) is provided. The second subsection of these chapters focuses on research done in the Arctic region. The last subsection presents covariate-based reliability and recoverability models. Finally, in chapter 4, the first part presents the concept and definitions of resilience. The literature reviews four main perspectives: resilience in engineering systems, resilience in the Arctic area, the integration of “Resilience, Reliability, and Recoverability (3Rs)”, and performance-based resilience models

Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Reliability assessment refers to the process of evaluating reliability of components or systems during their lifespan or prior to their implementation. In the manufacturing industry, the reliability of systems is directly linked to production efficiency, product quality, energy consumption, and other crucial performance indicators. Therefore, reliability plays a critical role in every aspect of manufacturing. In this review, we provide a comprehensive overview of the most significant advancements and trends in the assessment of manufacturing system reliability. For this, we also consider the three main facets of reliability analysis of cyber–physical systems, i.e., hardware, software, and human-related reliability. Beyond the overview of literature, we derive challenges and opportunities for reliability assessment of manufacturing systems based on the reviewed literature. Identified challenges encompass aspects like failure data availability and quality, fast-paced technological advancements, and the increasing complexity of manufacturing systems. In turn, the opportunities include the potential for integrating various assessment methods, and leveraging data to automate the assessment process and to increase accuracy of derived reliability models

Prognostics-Based Two-Operator Competition for Maintenance and Service Part Logistics

Prognostics and timely maintenance of components are critical to the continuing operation of a system. By implementing prognostics, it is possible for the operator to maintain the system in the right place at the right time. However, the complexity in the real world makes near-zero downtime difficult to achieve partly because of a possible shortage of required service parts. This is realistic and quite important in maintenance practice. To coordinate with a prognostics-based maintenance schedule, the operator must decide when to order service parts and how to compete with other operators who also need the same parts. This research addresses a joint decision-making approach that assists two operators in making proactive maintenance decisions and strategically competing for a service part that both operators rely on for their individual operations. To this end, a maintenance policy involving competition in service part procurement is developed based on the Stackelberg game-theoretic model. Variations of the policy are formulated for three different scenarios and solved via either backward induction or genetic algorithm methods. Unlike the first two scenarios, the possibility for either of the operators being the leader in such competitions is considered in the third scenario. A numerical study on wind turbine operation is provided to demonstrate the use of the joint decision-making approach in maintenance and service part logistics

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