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André-Michel Ferrari — Accendo Reliability New Contributor

André-Michel's author archives list his articles published on Accendo Reliability (in reverse chronological order).

About André-Michel Ferrari

André-Michel Ferrari is a Reliability Engineer who specializes in Reliability Analytics and Modeling which are fundamental to improving asset performance and output in industrial operations.

André-Michel has approximately 30 years of industrial experience mainly in Reliability Engineering, Maintenance Engineering, and Quality Systems Implementation. His experience includes world-class companies in the Brewing, Semiconductor, and Oil & Gas industries in Africa, Europe and North America.

by André-Michel Ferrari Leave a Comment

Defining a failure? – it is actually up to you!

Defining a failure? – it is actually up to you!

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.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari 1 Comment

How Conservative and Prudent can a Risk Decision be? – Practical uses of Confidence Boundaries

How Conservative and Prudent can a Risk Decision be? – Practical uses of Confidence Boundaries

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.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari Leave a Comment

Age Related Degradation Variables – Which is the Dominant One?

Age Related Degradation Variables – Which is the Dominant One?

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…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari Leave a Comment

Using RAM Models in Contracts

Using RAM Models in Contracts

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…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari Leave a Comment

The “Bath Tub” Curve Explained

The “Bath Tub” Curve Explained

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.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari Leave a Comment

Conditional Reliability and Boardroom Conversations

Conditional Reliability and Boardroom Conversations

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?

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari 1 Comment

Life Models for Repairable Versus Non-repairable Assets

Life Models for Repairable Versus Non-repairable Assets

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.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari Leave a Comment

Building a RAM model? How granular do you need to go?

Building a RAM model? How granular do you need to go?

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.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

by André-Michel Ferrari Leave a Comment

7 Benefits a RAM Model can Bring to an Organization

7 Benefits a RAM Model can Bring to an Organization

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.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset

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