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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 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.
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.
Sometimes we have an asset running for some accumulated time and need some indication of what the future performance. This is where conditional reliability comes in. Conditional reliability is the probability of a system successfully completing another mission following the successful completion of a previous mission.
In the Reliability and Maintenance world, we often refer to what is known as the “bathtub” curve. The bathtub curve can be useful in various circumstances and help an operator better manage their assets over time. However, it is important to understand where it comes from and what it means so we can avoid misusing or misinterpreting it.
Reliability, Availability, and Maintainability (RAM) models are commonly used to establish maintenance strategies or quantify operational outputs. However, they are not the primary tool of choice when drafting a contract between two parties.
Many assets have more than one degradation variable. In this case, it is important to define which of the multiple variables is the dominant one and will subsequently provide the Reliability Engineer with the most precise life model.
Confidence boundaries can be confusing to reliability engineering practitioners and their audience. Yet, they can play an important role in the risk-based decision-making process.
Maintenance and Reliability professionals deal with equipment failures all the time. However, the word “failure” could have different definitions or thresholds. In order to take adequate and effective action, it is important to have a clear understanding of what we are measuring as a failure.
Reliability Engineers are forward and “out of the box” thinkers. They tend to bring creative solutions to customers and help them optimize asset performance. Creativity implies offering ideas and possibilities that a customer did not know of or even request.
One of the most valuable tools for a Reliability Engineering team is an asset life model repository or Library. This is also known as the Weibull Library. It contains the life models for all the critical assets in the organization.
Operators need to estimate when in the future their equipment will attain their end-of-life state. Obsolescence is another word for equipment end-of life. Once they reach this stage, equipment replacement often leads to significant budgetary expenditures.
Barringer Process Reliability (BPR) was developed by Paul Barringer. BPR highlights operational issues. Not addressed and mitigated, those could have significant revenue impacts. A BPR analysis uses the Weibull probability plot which happens to be a very well-known tool in the field of Reliability Engineering. On one side of a sheet of paper only, the BPR plot can tell the true “story” on the operation.
Maintenance and Reliability practitioners often need to find quick methods to estimate life distributions in order to get some urgent answers to a customer. The tempting solution and easy way out to this is to refer to a handbook or publication out there. However, this can come with multiple pitfalls highlighted in this article.
Overhauling equipment is expected to bring it to an “as good as new” state. But is this really the case in reality? Equipment will deteriorate over time and progressively lose its ability to function. No matter how extensive the overhaul, the equipment will unlikely be up to the level of “newness” as when it rolled out of the assembly line.
There is a definite distinction between Reliability Engineers (RE) and Maintenance Engineers (ME). Although they are highly dependant on each other. REs rely a lot on MEs and vice versa. However, those roles are often confused let also not well understood by recruiters, managers or regrettably, even professionals in the roles.
A proper CMMS setup can make a world of difference in an organization’s asset management journey. Conversely, a substandard setup can be a living hell for Reliability Engineers like myself.
Building a Reliability, Availability and Maintainability model can provide numerous benefits to an Asset Management program. This includes conducting a Criticality Analysis.
Ever wanted to make your Reliability Analysts more productive and engaged? If yes, then this article highlights 10 highly recommended set-up requirements.
In line with the RAM acronym sequence, we often start and go no further than the “R” in Reliability. In doing so, we forget about the “M”. The question often asked is: “what is the reliability of the system?” But rarely asked is: “what is the maintainability of the same system?”
Maintenance Optimization is a Reliability Engineering process which helps organizations avoid unnecessary spend whilst minimizing the risk of a costly failure. There are multiple ways to get there. Find out more in this article!
Traditional spare parts or inventory calculation methods involving Min-Max levels lack rigor especially for critical equipment. RAM models provide a better approach to this calculation. Find out more in this article!
Using Barringer Process Reliability techniques, one can estimate the total “prize” recoverable using RCAs. As such, the company can justify investing in the resources required to conduct those exercises. Find out more in this article!
When adding new equipment, it is cheaper to evaluate the benefits, or lack of thereof, on paper, before implementing the change. A RAM Model is a tool of choice for this exercise. Find out more in this article!
Operational Data typically stored in the CMMS or historical records repositories can be a formidable revenue generator. Find out more in this article!
In an operation, assets can be dependent on each other. A RAM model helps account for the complexities including dependencies. Find out more in this article!
Concurrent or simultaneous failures can happen with redundant or spared systems. A RAM Model is an excellent tool to analyze this.
Find out more in this article!
Failure Modes and Effect Analysis (FMEAs) are an excellent foundation for Reliability Programs. It is also a steppingstone to build Reliability, Availability and Maintainability (RAM) models.
In probability theory and statistics, the Weibull distribution is a continuous probability distribution. It is very useful in Reliability Engineering calculations. Unbeknown to a lot of analysts, the Weibull distribution can also be used to model Production Output.
A PF Curve is a graphical tool used in the field of maintenance and reliability. It illustrates a component’s health degradation over its lifetime. A PF Curve is best used as a maintenance planning tool. This article demonstrates how Life Analysis is a rigorous approach to building a PF Curve.
In his book based on Reliability Centered Maintenance, John Moubray highlights 6 patterns of failure. However, one needs to be careful about how those patters are interpreted and used. Find out more in this article!
You might not have lots of data or a robust CMMS but you can still initiate a Reliability Improvement Program. All you need is daily production records. And apply the Barringer Process Reliability (BPR) Methodology using those records. Find out more in this article!
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