on Maintenance Reliability

Definition of Reliability

Reliability, in its academic root, is defined as the probability that a system will perform its intended function in a specified mission time and within specific process conditions. Reliability (R) is related to the Probability of Success as opposed to the Probability of Failure (F), and the relation between R and F is:

R = 1 – F (for mission time t)

In the above context, a reliability example would be: What is the probability that a centrifugal pump in a sheltered enclosure will push 3,000m3/day of sweet crude oil without unplanned failures for a period of 8,760 running hours?

Definition of repairable versus non-repairable assets

A common error when performing a life analysis for an asset is to confuse repairable and non-repairable assets. The mathematical determination of the life characteristics for each model is different, so throwing a simple “Weibull analysis” at them might lead to the wrong results but also the loss of valuable information.

Reliability, Availability, and Maintainability (RAM) modeling overview

The fundamental purpose of Reliability, Availability, and Maintainability (RAM) modeling is to quantify system performance, typically in a future time interval. A system is a collection of items that operate together to produce an output, often a production value. These items can include subsystems, components, software, human operations, and more. For example, an automobile can be viewed as a system with subcomponents like the drive train, engine, gearbox, etc. In RAM models, it is crucial to consider the relationships between these items to determine the system’s final output. They have proven effective in various industries as tools for cost avoidance, decision-making, and validating assumptions made by internal stakeholders.

The fundamental purpose of Reliability, Availability, and Maintainability (RAM) modeling is quantifying system performance, typically in a future interval of time. A system is a collection of items whose coordinated operation leads to the output, generally a production value. The collection of items includes subsystems, components, software, human operations, etc. For example, an automobile can be considered as a system with sub-components being the drivetrain, engine, gearbox, etc. In RAM models, it is crucial to account for relationships between items to determine the final output of the system. In various industries, RAM models have proven to be effective as cost avoidance or decision-making tools, as well as their ability to confirm or counter stated assumptions by internal stakeholders. 

This paper highlights a non-exhaustive list of seven diverse solutions that a RAM model can bring to the organization in terms of decision-making advantages.