Optimal create and use RAM models for use at any stage in the asset lifecycle
But what are they?
Two common forms are described below:
Reliability Block Diagrams
A reliability block diagram (RBD) is a diagrammatic method for showing how component reliability contributes to the success or failure of a complex system. An RBD is also known as a Dependence Diagram (DD). An RBD or DD is drawn as a series of blocks connected in parallel or series configuration. The RBD shows the logical connections of components within a piece of equipment. It is not necessarily the schematic diagram of the equipment, but the functional components of the system and their relationships. The equipment is made up of multiple components / systems in series, parallel and a combination of the two. These components / systems and the configuration of them provides us with the inherent reliability of the equipment. The RBD analysis consists of reducing the system to simple series and parallel blocks which can be analysed using the appropriate reliability formula. In some cases specific to process flow systems the RBD is called Process Dependence Diagram (PDD).
RAM Availability Models – Monte Carlo
To determine the estimated availability of a complex operating plant an availability simulation is used. The model contains either RBDs or PDDs to represent the constituent items of the plant. This may be at the system, sub-system, equipment or component level.
The elements are combined into groups and the groups are further combined (to any depth) to produce the PDD of the system. The PDD is similar to a normal Reliability or Availability Block Diagram (RBD / ABD) used by reliability engineers, but it allows complex logical relationships between groups and elements and this permits more accurate representation of the process or plant being modelled. The PDD should not be confused with a flow diagram since it describes dependency, not flow. An element may appear in more than one position in the PDD if this is required to represent the true dependency of the process on that element. For each element in the model reliability data such as failure rate, failure characteristics, failure distribution (exponential, log-normal etc), repair times are added. A more sophisticated model can include constraints such as limited spares, limited resource pools which will force items to queue waiting on repair, or be repaired according to a pre-defined criticality precedence.
This is too complex to do in a static analysis (i.e. by spreadsheets) so a dynamic simulation is used. The Monte Carlo simulation is a computational algorithm that relies on repeated random sampling from user defined input data distributions to compute output parameters of interest. This method is often used when the model is complex, nonlinear, infeasible, or impossible to compute an exact result with a deterministic algorithm. The benefits of this type of simulation is that it allows the operational capability of complex plants to be examined, including the interaction of many complicating factors such as queuing for repairs and spares, common mode failures, the effects of preventive maintenance, changes in plant configuration, changes in plant loading, etc. It is simulated thousands of times over to produce a distribution of the parameters of interest.
Monte Carlo simulation offers the most versatile of all the system analysis methods available. Systems can be modelled in whatever level of detail is required being very useful for the simulation of different maintenance strategies. The trade-off for the versatility of the technique is the demand that this method makes in terms of computer power or the need for software applications.
When the simulation finishes, the program totals the time spent by each component in its running, standby, and failed states, and the time spent by the overall system in all its possible states, from which the overall system availability can be calculated. Other important information can also be obtained, such as the number of times each component failed, the number of times the standby component was called to start, the number of times components were exchanged for spares, and whether there would have been advantage in having more spares available.
At Optimal, RAM modelling forms part of the Strategise phase of ARaaS – Asset Reliability as a Service suite of solutions through which the reliability and availability of an asset are assessed. We have undertaken RAM modelling exercises for a variety of clients on systems such as power generation and gas compression and understand the importance of RAM models in the different stages of the asset lifecycle, whether that would be for determining the best equipment installation configuration or identifying most effective maintenance regimes at late life decommissioning stages.
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