High plant reliability is critical for every successful company, and it has never been more important than it is in the present economic climate.
The costs associated with equipment downtime and reduced production can be significant, and engineers must ensure that you are using every possible means of maximizing plant reliability and performance. Of the five fundamental ways in which engineers can approach the maintenance of plant, one of the least commonly used (because it is least commonly understood) is Reliability Centred Maintenance (RCM).
The heart of an RCM approach is the creation and exploitation of reliability models that use previous failure data to predict future plant performance and hence permit the selection of a maintenance strategy and frequency optimization of planned maintenance activities. Reliability modeling as part of an integrated maintenance strategy is an approach that can no longer be sidelined or ignored by high performing companies.
This conference is a combination of instructor-led topic areas and extensive computer-based analysis and modeling. You will learn in detail about, and practice using, best-of-breed approaches to statistical failure data analysis and reliability modeling. Furthermore, throughout the conference, you will have the opportunity to analyze your own data and to ask lots of questions about how best to apply reliability analysis and modeling techniques in your organization.
The conference delivers many practically-based technical solutions to reliability improvement, and delegates will discuss these concepts and practice using them via a range of practical tools applied to real-world case studies and data.
At the end of this conference the participants will be able to:
Explore and understand the power contained in maintenance history records (failure data), and how this can be harnessed using statistical approaches to improve maintenance (and hence overall plant) performance
Analyze failure data using a range of first principles and industry-standard methods, all implemented in Microsoft Excel
Understand failure mode shape analysis and thereafter to extract failure mode shapes from history record data and use this to optimize Planned Maintenance (PM) activities
Understand the theory and application of reliability modeling
Apply the theory of reliability modeling to a range of practical case studies, using the teaching version of an industry-standard reliability modeling software package
Develop from first principles a practical and comprehensive reliability modeling and statistical analysis toolbox in Microsoft Excel, and use this to analyze numerous practical case studies
Use reliability models to predict future spare parts requirements and the proportions of maintenance time that will be spent in reactive (breakdown) and proactive (PM/PPM) maintenance activities
Explore the implementation of a Reliability Centred Maintenance approach as part of a modern maintenance management strategy, including a detailed cost-benefit analysis of a real application
Planners
Supervisors
Engineers
Reliability Engineers
Maintenance Team Leaders and Managers
Operations Team Leaders and Managers
Fundamental approaches to maintenance
Formulating a maintenance strategy
The importance of maintenance history records
Understanding plant performance
An introduction to the statistical analysis of failure data
The principles of failure data analysis
Industry-standard measures of reliability (Availability, MTBF, MTTR, etc)
Extensive hands-on experience
Open discussion
Pareto analysis, rank order charts, and standard deviation
Linear regression models and determining model accuracy
Failure mode analysis
Interpreting failure mode shapes
Extracting failure mode shapes from real data
Optimizing PM activity using mode shape analysis
Knowing when to use a breakdown maintenance approach
Extensive hands-on experience
Open discussion
The principles of RCM and reliability modeling
Developing a reliability model
Weibull statistics and the range of Weibull models (2 parameters, 3 parameters, maximum likelihood, maximum accuracy)
The Weibull curve and plotting data on a Weibull scale
Defining parameters: shape, scale, mean life, minimum life, characteristic life, standard deviation
Model accuracy assessment (observed model accuracy and hypothesis rejection)
Interpreting model results
Confidence levels and Weibull critical values
Key graphical functions:
The reliability function: survival probability
The cumulative distribution function
The failure probability density function
The failure rate function
Extensive hands-on experience
Open discussion
Converting reliability model data into cost-based maintenance decisions
Optimizing PM activity based on cost and by using reliability predictions (note that the program will NOT cover the costing of maintenance activities, but will assume that this information is already known)
Calculating the cheapest PM interval for age-based replacement policies
Graphing costs versus PM interval
Predicting future failures
Predicting spares utilization
Development of the key components of a reliability toolbox
Extensive hands-on experience
Open discussion
The cost of maintenance convenience and making informed maintenance optimization decisions
Incorporating real-world effects within reliability models
Specifying the PM interval and understanding the implications of doing this
Completing the reliability toolbox
Graphing toolbox results
Toolbox testing and comparison of results with best-of-breed modeling software
Extensive hands-on experience
Overall review of concepts learned and how they can be applied in practice
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Large capital-intensive projects in the oil and gas industries require substantial - and mostly risky - investments in the acquisition, exploration, and subsequent operation and maintenance of new organizational assets.
The decision of whether or not to invest in new capital projects in the oil and gas industry starts with critical decisions during the exploration phase of new development or the expansion of an existing field. The decision-making tools used to analyze project risk under conditions of uncertainty will help companies to determine the probability of success or loss and will drive the decision to develop or abandon the well.
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