André-Michel Ferrari — Accendo Reliability New Contributor

The Equipment Overhaul Paradigm

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.  Sure, if you have this vintage automobile from the 1930s, and spend an absolute fortune on its overhaul, it might come close to being new again. But somewhere in the structure of this automobile, there will an element that is “as good as old” and vulnerable to imminent failure. Also remember that this vintage automobile is not out there generating revenue. There is no accountant running after you with regards to budget constraints or revenue increases. Where there is passion, cost might not matter.  

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The need for life distributions

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. Also known as “Reliability Data” handbooks. These publications would have “ready to go” life distributions.  However, this can come with multiple pitfalls listed as follows.

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Barringer Process Reliability – Your factory performance on a single page!

Barringer Process Reliability (BPR) was developed by Paul H. Barringer, a fellow reliability engineer “extraordinaire” and an outstanding mentor for myself and countless others in this field of practice.  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.

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End-of-life definition

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. In addition, if the operation has a large number of a particular equipment types, they could all reach their end-of life at the same time leading to even greater financial impact. In industry jargon, this is known as the “Tsunami” effect. Operators need to aware and prepared in order to avoid this “wave” of consequences. Managing future cash inflow and outflows happens to be crucial financial exercise in any organization. Therefore, end-of-life management in an important financial accounting exercise. However, estimating end of life this is not always as straightforward as it seems.

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A crucial knowledge repository for Reliability professionals

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. This information is crucial for failure prediction and ultimately decision making in the domain of asset management. It is the company’s own and true version of a “failure database”. It avoids the use other generic external databases that might not reflect the true behavior of the assets in the organization.

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Reliability Marketing

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. Ones that will be of great benefit to them. The role has a lot to do with marketing. It is actually marketing a better “operating” future for an organization. In this article, I share some of the approaches I have had as a Reliability Engineer on bringing the biggest reliability “bang” for the customer’s “buck”.

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The reliability definition in relation to asset failures

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 clear specifications for what a “failure” truly is.

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Introducing confidence boundaries

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. When building statistical models, there is always uncertainty around the model because it is usually based on a smaller sample of the studied population. The confidence interval is the range of values you expect your model to fall between a certain percentage of the time if you run your experiment again or re-sample the population similarly. For example, using a 90% confidence boundary, one would expect 90% of the records to fall between the upper and lower confidence boundaries. As a rule of thumb, the more data you have, the more precise the model and the narrower the confidence boundaries.  In essence, if we have an infinite amount of data, we will end up with a perfect model. However, this is never the case. Confidence boundaries help establish the accuracy of the model and also provide some information on the validity of the data.

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The concept of degradation variables

Assets typically age over time, leading to degraded performance and loss of function. Asset life models are built in order to predict future degradation patterns. Those models are based on asset degradation variables such as time or usage. Those variables could be for example, time between failures or distance covered between failures. 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. 

Reliability is a probability. Specifically, the probability that a system will perform its intended function within a specified mission time and under specific process conditions. Therefore, most reliability calculations incorporate a time element as a degradation variable. Generally, when building life models, we default to using calendar time as it is more straightforward. We have had tools to easily measure elapsed calendar time for centuries now. [Read more…]

Reliability, Availability, and Maintainability (RAM) modeling overview

The concept of Reliability Block Diagrams (RBD) is also known as Reliability Modeling or Reliability, Availability, Maintainability (RAM) analysis. With RAM models, the interaction of large, complex, and multi-layered systems can be analyzed using Monte Carlo simulation methods. This help quantify the output of the entire system with greater accuracy than other estimating tools or methods. [Read more…]

Introducing the “Bath Tub” curve concept

In the Reliability and Maintenance world, we often refer to what is known as the “bathtub” curve and ask the question: “What is the bathtub curve for this equipment?” The name “bathtub” comes from the equipment failure rate curve resembling a sanitary bathtub’s longitudinal section. In reality, it is rarely symmetrical and looks more like a distorted “u” or “v” shaped figure. 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.

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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:

$$ \displaystyle \large R = 1 – F (t) \ \:\:\:\: \:\:\:\: (for \:mission\: time\: t) $$

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?

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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.

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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.

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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 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.

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