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Home » Articles » on Maintenance Reliability » The Reliability Mindset

The Reliability Mindset: Practical Applications in Industry

These articles offer practical and effective aspects of Reliability Engineering in an operating environment. Through short and easy to read articles, the author shares his experiences, the different tips and techniques he has learnt over the years illustrating the vast and sometimes untapped potential of this specialty.

by André-Michel Ferrari Leave a Comment

Reliability Engineering – Science of Pessimists?

Reliability Engineering – Science of Pessimists?

Introduction

This is a rather depressing title for an article specifically on the topic of Reliability. After all, Reliability relates to the Probability of Success hence the contradiction.

However, the contradiction is not so much one as Reliability Analysis depends on having access to failure data. In other words, if the equipment does not fail, it might be difficult or even impossible to perform an analysis. Of course, we can source other types of data that are not failure records, but this is not the common approach.

[Read more…]

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

by André-Michel Ferrari 1 Comment

Reliability Software – Avoiding White Elephants

Reliability Software – Avoiding White Elephants

Introduction

Software packages and algorithms help speed up calculations in Reliability Engineering Analysis. And obviously in other specialties. They are known as Commercial Off-The-Shelf (COTS) Software. Preprogrammed formulas can be readily applied by the analyst to provide modeling results. Additionally, graphics and other informative visual representations can be obtained at a mouse click. This helps showcase issues in an operating environment. And lead to relevant action to address those issues.  Our human brain has cognitive limitations when analyzing highly complex systems such as Reliability, Availability and Maintainability (RAM) Models. Software helps to overcome those limitations.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Evaluating Equipment Repair Effectiveness

Evaluating Equipment Repair Effectiveness

Introduction

How effective are the repair tasks you conduct on your equipment?

This article looks at quantifying the effectiveness of corrective maintenance, typically an unplanned repair on an asset. Unplanned repairs are undesirable because the asset loses its function and cannot produce what is required often leading to loss of revenue. Yet, this advent is inevitable because assets are subject to degradation over time.

The effectiveness of a repair relates to the condition in which the asset is restored to after a repair. But also how long it will operate until the next repair, Evaluating the effectiveness of the repair will provide the asset operator with a decision making tool. Can we do something different if the repair effectiveness is unsatisfactory? Or do we have to replace the asset as it is failing too frequently?

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Reliability Engineering – It’s all about Models!

Reliability Engineering – It’s all about Models!

Definition of Reliability

The concept of Reliability is often misused, misunderstood, and misinterpreted. 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. So, it is in essence a probability.

The ageing variable is crucial in calculating the reliability of a system. Reliability is the Probability of Success. And 1 minus the Probability of Failure. In the equation below , the ageing variable is time (t).

$$ \displaystyle \large R\left(t\right) = 1 – F\left(t\right) \:\:\:\: (where \: F = Failure \: Probability) $$

Reliability engineering is based on building data models to predict future system performance accurately. Based on past performance records. The model is more or less precise depending on how many past records we have. The more data, the more precise the model. At the end of the day, a well defined model, even with a little data, is better than no model. At least we can proceed in the right direction. That is, make the best possible decision for our assets.

This article looks at a non exhaustive list of models in the Reliability Engineering realm.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Linking Reliability Concepts and Safety Performance

Linking Reliability Concepts and Safety Performance

Definition of Reliability

The concept of Reliability is often misused, misunderstood, and misinterpreted. 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. So, it is in essence a probability.

The time variable is crucial in calculating the reliability of a system. Reliability is the Probability of Success. And 1 minus the Probability of Failure.

$$ \displaystyle \large R\left(t\right) = 1 – F\left(t\right) \:\:\:\: (where \: F = Failure \: Probability) $$

[Read more…]

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

by André-Michel Ferrari Leave a Comment

5 Decisions Reliability Analysis Can Help With

5 Decisions Reliability Analysis Can Help With

Introduction to Reliability or Life Analysis

Reliability Analysis or Life Analysis is a process where asset records are used to build a statistical model for the asset. The model will in turn provide information of its future performance. It becomes an indispensable tool for asset managers in terms of making life cycle decisions with regards to the asset preservation and eventually replacement. Let alone the safety of employees. Typically, though not necessarily always, those records are obtained from the Computer Maintenance Management System (CMMS).

The basis of the model is a statistical distribution. An example of the process is illustrated in Diagram 1 where a number of “n” centrifugal pumps are run on a test bench until they fail. Each time interval to failure is recorded and once all the failure records are collected, a normalized frequency graph can be constructed. This frequency graph helps define the statistical distribution that best represents the life cycle of a typical component in the population. Using specific mathematical transformations applied to the distribution, the probability of failure after a defined mission time can be derived. And much more information as we will discover in this article.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Graphical Representations in Reliability Analytics

Graphical Representations in Reliability Analytics

Introduction

Reliability Engineering analytics come with graphs and visual representations providing the analyst with various types of key information. Some graphs are more popular than others. This article highlights the 4 main graphs used in Reliability Engineering Analysis.

They are Reliability, Probability of Failure, Probability Density and Failure Rate. Less common types are not covered here. Such as Contour Plots. The basis of the graphs in this article is the Weibull Distribution. In other words, all the graphs derive from this very popular distribution. The same concepts applies to other distributions.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Reliability Analysis and Sound Engineering Judgment

Reliability Analysis and Sound Engineering Judgment

Introduction

Sometimes, non reliability practitioners assume that a Reliability Analysis is just about “tossing” a distribution at a set of data. It is a little bit more complex than this. There needs to be some sort of engineering validation on the final outcome. The question to be asked is: “does this life model used make engineering sense?” This article highlights the importance of choosing the correct statistical distribution. The alternative leads to flawed decision making and can be detrimental especially when dealing with critical equipment or million-dollar decisions. A worked example is also provided.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Equipment Maintenance Terminology Simplified

Equipment Maintenance Terminology Simplified

Introduction

This article is a highly simplified overview of Equipment Maintenance Strategies. Its objective is to categorize the types of maintenance approaches for the lay person. Sometimes, explaining the basics using simple terminologies, initiates dialogues with others that are outside of our field of expertise. And as such improve collaboration between teams. How about making this accountant who somewhat controls the maintenance budget, understand what maintenance is really about? On the other hand, that same accountant can educate us on their work practices.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Using Crow-AMSAA Plots in Disaster Predictions

Using Crow-AMSAA Plots in Disaster Predictions

Crow-AMSAA Analysis Overview

Crow-AMSAA Plots have a variety of names, such as Reliability Growth Plots or Duane Plots. The term “Crow” comes from Dr Larry H. Crow, who enhanced James T. Duane’s pioneering launch of this methodology, which was developed in the early 1960s (1). Crow successfully applied the method in the US Army Materials System Analysis Activity (AMSAA). The technique has blossomed into large amounts of new applications in industry such as but not limited to:

  • Making reliability more visible and manageable hence contributing to Reliability Improvement Programs,
  • Monitoring Design Optimization and Quality Performance,
  • Catastrophic or adverse event occurrence predictions and trends which in essence are the objective of this article.

In practical terms, The Crow-AMSAA technique involves plotting, most commonly, cumulative failures vs cumulative time on a log-log scale resulting in straight line plots (2). The line slope value (or Beta value) indicates improving, deteriorating, or constant failure occurrences. Due to the straight-line nature of the plots, future failure forecasts can be estimated. In plain words, based on the current trend, when is the next failure expected to occur? This method handles mixed failure modes, so it is, therefore, suitable for the complex nature of the generating units. [Read more…]

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

by André-Michel Ferrari Leave a Comment

Using Contour Plots in RCAs

Root Cause Analysis Overview

A Root Cause Analysis (RCA) is a structured approach to identifying the underlying factors that result in the unwanted/unexpected outcomes of chronic or sporadic events. It is a highly methodic and rigorous process. And highlights what assets, systems or behaviors need to be modified in order to to limit or eliminate the recurrence of similar outcomes. The fundamental driver is to address, correct or mitigate the root causes that lead to the unwanted event rather than addressing the symptoms. Bob Latino, a renowned RCA expert, summarizes this concept as “the establishing of logically complete, evidence based, tightly coupled chains of factors from the least acceptable consequences to the deepest significant underlying causes.”

[Read more…]

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

by André-Michel Ferrari 2 Comments

No data = No Reliability Program? Fallacy!

No data = No Reliability Program? Fallacy!

I have often heard employees complain they have no data to initiate a proper Reliability Improvement Program. This is not always true. And to no fault of theirs. They just don’t know how to use what they already have in terms of records. If you are running an operation, you should at least have production records – i.e. how much you are producing on a daily basis. If you don’t have this, then maybe you should not be in business at all. This article looks at ways to initiate a Reliability Program using the Barringer Process Reliability (BPR) methodology. The greatest advantage of this methodology is that it only requires productions records as an input.  That is how many units of production the plant produces on a daily basis.  For example, the barrels of crude oil processed per day in a refinery. Or the hectoliters of beer brewed in a brewery daily.

[Read more…]

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

by André-Michel Ferrari 2 Comments

Interpretation of Moubray’s Failure Patterns

Interpretation of Moubray’s Failure Patterns

Failure Patterns according to Moubray

In his book Reliability Centered Maintenance1, John Moubray highlights 6 patterns of failure. However, one needs to be careful about how those patterns are interpreted and used. Or misused. These 6 failure patterns are as follows:

  • A: Bathtub Pattern
  • B: Age Related or Wear Out Pattern
  • C: Fatigue Pattern
  • D: Initial Break-in Pattern
  • E: Random Pattern
  • F: Infant Mortality Pattern
[Read more…]

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

by André-Michel Ferrari Leave a Comment

Building a PF Curve Using Life Analysis Concepts

Building a PF Curve Using Life Analysis Concepts

Definition of a PF Curve

A PF curve is a graphical tool used in the field of maintenance and reliability. It essentially illustrates a component’s health degradation over its lifetime. As well as a visual guide on when to conduct appropriate action to minimizes operational risks related to unplanned failures. It is essentially a planning tool.

[Read more…]

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

by André-Michel Ferrari Leave a Comment

Using a Weibull Distribution to model Production Output

Using a Weibull Distribution to model Production Output

In probability theory and statistics, the Weibull distribution is a continuous probability distribution. It models a broad range of random variables. Largely in the nature of a time to failure or time between events. It addresses mechanical or structural failures in the field of Reliability Engineering. By nature, the Weibull distribution provides a lot of information such as aging characteristics or expected asset lifetime. One of its most common outputs is the Bathtub Curve.

[Read more…]

Filed Under: Articles, on Maintenance Reliability, The Reliability Mindset Tagged With: Weibull distribution

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