Performance modeling in critical engineering systems using RAM analysis

Reliability, Availability and Maintainability (RAM) models were first developed in Enbridge Liquids Pipelines in 2006 and in the last 3 years have contributed over $200 Million in capital cost avoidance while maintaining or improving the reliable design and operation of the pipeline system. These models constitute a very effective, factual and dependable tool to assess the throughput performance of a pipeline system over its entire life cycle or a specified time of interest. In addition, the RAM model cost is a small fraction of the overall monetary benefit, in the order of 1%, hence making RAM models a highly leveraged activity. The concept of a RAM model stems from Reliability Block Diagram methods (also known as Dependent Diagrams). Interaction of large, complex and multi layered systems can then be analyzed using the Monte Carlo simulation methods (or Stochastic Discrete Event Simulation) hence quantifying the output of the entire system with greater accuracy than other estimating tools or methods. Over 10 years, Enbridge Liquids Pipelines has developed its own failure database for equipment and operational events consequently almost all model inputs are based on in-house data rather than industry generic data, making the model output more robust, accurate and appropriate for internal use. Initially, in Enbridge Liquids Pipelines, RAM models were mainly built to assess and confirm the design capabilities of future pipelines designs and assist in the optimal selection of specific design options. Because of the effectiveness of the tool combined with the current cost constrained business environment, the company is moving towards building RAM models for assets already in operation in order to optimize their performance. This is proving to be an extremely cost effective addition to internal decision making processes. The approach has been used in risk based budgeting, asset maintenance, design modifications and throughput optimization initiatives. In various industries including Oil and Gas, RAM models have proven their worth over time as an effective cost avoidance tool. This approach has now been successfully deployed in the Pipeline Industry at Enbridge. As an example, a RAM model conducted during the design phase of a pipeline project saved $28 million in capital by proving that an additional storage tank and significant new infrastructure was not required. Another benefit of RAM models has been their ability to confirm or counter stated assumptions by internal stakeholders. Equipment upgrades and equipment sparing decisions are often seriously debated and costly decisions may not always be based on complete economical foundations but rather on avoiding past negative experiences or by following basic guidelines that are less than optimal. When a project or operational team needs to find an alternative, a RAM model is a tool of choice to evaluate, and justify the best option. Because of its proven value, RAM models are now an integral part of Enbridge Liquids Pipelines Design Standards and are used on all large projects. Models recently built for operational pipelines delivered similar value so in the near future the work will be expanded to encompass the entire existing network of integrated pipelines to improve on performance and operational costs.

PurposeThe purpose of this research is to calculate and enhance the cheese cheddar manufacturing plant efficiency under actual workplace conditions by measuring reliability, availability and maintainability (RAM) indices. The authors highlight how RAM analysis is important in determining periodic maintenance and in scheduling and managing the appropriate maintenance policy.Design/methodology/approachThe current work is conducted using statistical approaches to evaluate failure and repair statistics. The RAM estimation was calculated on the basis of quantitative data obtained over a span of 32 months. Descriptive statistics, Pareto analysis, as well as the presumption of independence were ensured through trend and serial correlation tests. In addition, the reliability and maintainability of the cheddar cheese processing plant and its machines were calculated at various mission periods.FindingsThe primary goal of the implementation approach is to understand the fault patterns and the accurate quantitative assessment of the reliability and maintainability of the cheddar production plant. The findings revealed the essential aspects of the line, which need improvement by an appropriate maintenance program.Originality/valueThis study is intended to serve to highlight the RAM assessment and its impact on the performance of the real-time system. The benefit of the technique is the continual control of the manufacturing process by means of acceptable indexes, whose use corresponds to a continuous improvement process.

PurposeThe purpose of this paper is to provide results for a complete reliability, availability, and maintainability (RAM) analysis utilizing data sets from a production system in a wine packaging line. Through the illustrated case study, the author demonstrates how RAM analysis is very useful for deciding maintenance intervals, and for planning and organizing the adequate maintenance strategy.Design/methodology/approachRAM analysis has been done for each machine by using failures data. The parameters of some common probability distributions, such as Weibull, exponential, lognormal, and normal distributions, have been estimated by using the Minitab software package. An investigation to determine which of these distributions provide the best fit for characterizing the failure pattern at machine and line level has been made. Reliability and maintainability of both wine packaging and its machines has been estimated at different mission times with their best fit distribution. High maintainability issues and potential factors with their potential failure modes were presented, through failure mode and effect analysis process.FindingsAnalysis of the total downtime, breakdown frequency, reliability, and maintainability characteristics of different machines shows that: first, the availability for the wine packaging line was 91.80 percent, and for the remaining 8.2 percent the line is under repair. Second, about two failures per shift are displayed on the line, whereas for the mean time-to-repair (TTR) a failure is 24 minutes. Third, there is no correlation between the time-between-failures and the TTRs for the wine packaging line. Fourth, the main three factors affecting the maintainability process in the production line are: resources availability, manpower management, and maintenance planning procedures.Originality/valueThis study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis in the much advertised field of wine packaging production line which on the other hand so little has been published on operational availability and equipment effectiveness. It can also be useful to serve as a valid data source for winery product manufacturers, who wish to improve the design and operation of their production lines.

The High voltage static converter systems are deployed at different military and DoD assets in which power electronics is used for the isolated and stabilized AC or DC voltage/frequency conversion process and the Insulated Gate Bipolar Transistor (IGBT) triggering system plays crucial role in such systems. Such type of systems and equipments are being operated by the combat deployed personnel and they rely upon in order to complete their critical missions. Therefore, it is evident that reliability and availability during the mission and maintainability of the system before and after the mission is paramount. The objective of this paper is to present the predicted Reliability, Maintainability, Availability and Failure rate analysis of the IGBT triggering system. The analysis is made in the marine environment by defining specific parameters. The method used to calculate the reliability prediction of the IGBT triggering system is based on the calculation of Mean Time Between Failure (MTBF), which is obtained from the calculation of Failure rate of individual components used in the system. The maintainability analysis is done based on the length of the time required by the repairable components, repair through replaceable components or replacing the whole system. Therefore, this analysis will use the Mean Time To Repair (MTTR). MTBF and MTTR are used as key elements in the availability analysis of the system. Pareto analysis is presented in order to show the individual components failure rate contribution used in the system. The presented analysis shows the relationship between reliability, maintainability and availability of a system and also shows how the reliability and maintainability analysis improves the availability of the designed system. The presented analysis can be viewed with reference to system lifetime calculation as it will help in investment plan of designed system in marine environment.

A new fuzzy methodology-based structured framework for RAM and risk analysis

A model for evaluation the reliability, availability and maintainability of the electronic equipment condition monitoring system of oil platforms, which is designed to predict the parameters values of the RAM (Reliability, Availability and Maintainability) system during the operation of oil platform equipment, is proposed. An integration module WMCS (Well Monitoring and Communication System) based on LabVIEW software, which processes the maintenance parameters, is implemented. Raptor software for the model is implemented. Studies have shown that the proposed model allows you to use maintenance parameters for evaluation reliability, availability and maintainability, providing results in the form of reports. Keywords reliability, availability, maintainability, RAM, WMCS, LabVIEW, Raptor

The purpose of this paper is to present a methodology for analyzing the system performance of an industrial system by utilizing uncertain data. Although there have been tremendous advances in the art and science of system evaluation, yet it is very difficult to assess their performance with a very high accuracy or precision. For handling of these uncertainties, fuzzy set theory has been used in the analysis while their corresponding membership functions are generated by solving a nonlinear optimization problem with particle swarm optimization. For finding the critical component of the system which affects the system performance mostly, a composite measure of reliability, availability and maintainability (RAM) named as the RAM-index has been introduced which influences the effects of failure and repair rate parameters on its performance. A time varying failure and repair rate parameters are used in the analysis instead of constant rate models. Finally, the computed results are finally compared with existing methodologies. The suggested framework has been illustrated with the help of a case.

Integrated framework to optimize RAM and cost decisions in a process plant

The state-of-the-art related to reliability, availability and maintainability (RAM), provides indices that allow to effectively measuring different attributes on single complex assets. However, those methodologies usually present limitations when the aim is to analyse grouping maintenance or the remaining useful life in fleets of assets. The purpose of this contribution is to suggest the advantages of measures based on the notion of Shannon entropy, applied to fleet RAM analysis. Particularly, this research presents a derivation from well-known concepts such as the Gini, Hoover, and Theil indices.

In this article, Reliability analysis of a three-unit identical system is discussed. The units of system may affect by two types of failures namely, Lethal Common Cause Shock (LCCS) and Non-Lethal Common Cause Shock (NCCS) failures. Using stochastic process, the set of differential equations of the current model are derived to obtain reliability measures such as reliability of the system and Mean Time to Failure (MTTF) in the case of series and parallel. Also, the Maximum Likelihood Estimates (MLE) of the above said measures are discussed and presented in numerical illustration by using simulation. Tables display the findings and suggest that LCCS and NCCS are the most dominant causes of failures while studying the performance of the systems in reliability theory.

In the production department of the D&K Building plant which serves as a place to assemble tire building materials to become a green tire, there is a jointless machine which functions to install jointless material to be assembled with other materials. The frequency of damage in jointless machine components is still high that causes the high losstime. The high losstime indicates a small value of reliability of the machine. The data needed in this study are damaged engine components, engine failure period, and engine component replacement time. In this study, critical components were analysed. The methods used in this analysis were FMEA, LTA and MTTF. The result of the analysis showed that the bearing is jammed with an average reliability of 49% with an MTTF value of 30 days. Broken bearings value an average reliability of 8.5% to an MTTF value of 56 days. Piping accessories (jointless stopper) the average value of reliability is 52% to an MTTF value of 145 days. The stopper bolt has an average reliability of 50% to an MTTF value of 385 days. The results showed that the determination of the critical component replacement time interval can determine the appropriate maintenance activities on the Jointless machine so that the right RCM decision for the company can be achieved.

High-performance computing (HPC) storage systems provide data availability and reliability using various hardware and software fault tolerance techniques. Usually, reliability and availability are calculated at the subsystem or component level using limited metrics such as, mean time to failure (MTTF) or mean time to data loss (MTTDL). This often means settling on simple and disconnected failure models (such as exponential failure rate) to achieve tractable and close-formed solutions. However, such models have been shown to be insufficient in assessing end-to-end storage system reliability and availability. We propose a generic simulation framework aimed at analyzing the reliability and availability of storage systems at scale, and investigating what-if scenarios. The framework is designed for an end-to-end storage system, accommodating the various components and subsystems, their interconnections, failure patterns and propagation, and performs dependency analysis to capture a wide-range of failure cases. We evaluate the framework against a large-scale storage system that is in production and analyze its failure projections toward and beyond the end of lifecycle. We also examine the potential operational impact by studying how different types of components affect the overall system reliability and availability, and present the preliminary results

The ability of executing assigned function of repairable system under certain condition is quite different from that of single unit due to redundant configuration. It is well understood that proper management strategy can greatly prolong the mean time to failure (MTTF) of repairable system. By introducing operating cycle or partial test interval, repair time and maintenance efficiency, the MTTF of classic 1oo1, 1oo2 and 2oo3 redundant systems is studied in this paper. It is found that periodical maintenance can dramatically improve the availability of repairable system, while the efficiency of maintenance has great influence on the MTTF of repairable system.

The system consists of two heat production plants (for a total of 621 MW). a branched closed circuit transportation network, a distribution network and heat exchange substations. AEM wants to modify the district heating system anticipating a growth of user connection; for this reason different system solutions were studied. The object of this study is to evaluate the better solution according to a cost-benefit analysis. The costs are represented by the necessary investments to realise each change; the benefits are represented in terms of reliability growth for Torino Sud district heating system and the decrease of the consequences (user out of service) in case of faults in the system. Then availability and expected number of failures of the system were determined via a fault tree analysis related to the top event "no heat to user for t>1 hour". The consequences of faults for each macro-component of the system were defined as a function of the users out of service and a risk-map of whole system was made. Finally new availability analysis were made analysing four different solutions for the system in order to find the best one. The results of the reliability analysis of the system in the present configuration are satisfactory: the availability is in a range of 97.36% and 99.73%. The mission time is 1 year (8760 hours of continuous operation) and the expected number of failures for a year is in a range of 0.98 and 9.81.

Reliability analysis of the feeding system in the paper industry