André-Michel Ferrari — Accendo Reliability New Contributor

The Weibull distribution is a highly popular workhorse in Reliability Engineering. Sometimes a little too popular.  Its shape parameter (β) provides valuable information on the life characteristics of assets. In practice, a single Weibull distribution often fails to represent field or fleet data because the observed failures are not generated by only one homogeneous population. Instead, the data is frequently a blend of multiple underlying subpopulations. Relating to different designs, suppliers, duty cycles, environments, maintenance histories, etc. This is where multipopulation (mixture) Weibull models become valuable. They explicitly model heterogeneity rather than forcing one curve to “average out” incompatible behaviors. It is therefore incorrect to assume the population of failure times comes from a single Weibull distribution characterized by shape parameter 𝛽 and scale parameter 𝜂.

Reliability Engineering has a bias that is both practical and measurable: simpler systems tend to be more reliable. This is not a philosophical preference for elegance; it is an outcome rooted in how failures occur, how they propagate through architectures, and how uncertainty accumulates when complexity grows. When we say “simple,” we do not mean “unsophisticated.” We mean fewer parts, fewer interfaces, fewer operating modes, fewer dependencies, and fewer opportunities for human and environmental variability to turn into functional failures.

In an increasingly complex and interconnected world, ensuring that systems—from machinery to software, from power networks to consumer products—perform reliably across their intended lifetimes, is essential not only for safety and quality but also for economic viability. This intersection between ensuring dependable performance and managing costs is broadly studied under what is known as Reliability Engineering and Economics. Or Relia-nomics.

Parameter Definition in Statistics

In statistics, parameters are numerical characteristics that describe a population or distribution. They summarize important aspects of the population. For example, in a normal distribution, the mean and variance are key parameters that define the distribution. Parameters are often denoted by Greek letters (e.g., μ for mean) and are used to create a parametric family, which consists of all distributions defined by the same parameters.

There is typically at least one parameter in every distribution. Some distributions can have different multiples of parameters. The exponential can have up to two, the lognormal have up to 3. This article deals with the Weibull distribution which can have 2 or 3 parameters.

Introduction

An extended warranty is a prolonged warranty offered to consumers in addition to the standard and generally free manufacturer’s warranty on new items. It is also referred to as a service agreement or service contract, providing coverage for repairs or replacements after the manufacturer’s warranty expires. Extended warranties may be offered by the warranty administrator, retailer, or manufacturer, and they often come at an additional cost. Sometimes significantly in relation to the item’s base price.

The basis of this article is: should you consider buying extended warranty? What are the pernicious calculations it hides? It is often alleged that extended warranty is a ploy to extract more funds from a customer. Whilst this article does not question the honesty of the vendors of extended warranty, it demonstrates how warranty is calculated and that’s where the “devil” might lurk.

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.

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.

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.

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

Introduction to Reliability or Life Analysis

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.

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.

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.

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.

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…]