As an engineer, you know that everything has a limited amount of time to work.
Over time, it will become less reliable until it finally breaks.
But did you know that there is a curve that can tell you when that failure is most likely to happen? It is called the "bathtub curve," and it is one of the most important ideas in reliability engineering.
By understanding this curve, you can find the different stages of a device's life, figure out when it is most likely to break, and take the right steps to stop it from breaking.
In this article, I will go into detail about the bathtub curve.
I will look at its three phases, the common factors that contribute to each phase, and ways to make failure less likely.
Whether you are a student of engineering or a professional engineer, you need to understand the bathtub curve to make sure that the equipment you design, build, or maintain works reliably for its whole life.
So let us dive in and learn more about this important idea.
Introduction to the Bathtub Curve
Formal definition:
An equipment failure-rate curve with an initial sharply declining failure rate, followed by a prolonged constant average failure rate, after which the failure rate again increases sharply.
Understanding the Bathtub Curve
The bathtub curve is a graph that shows how often a product or group of products breaks down over time.
It is often used in reliability engineering and modeling of asset deterioration to predict and plan for asset failures.
The curve has three separate parts: the period of infant mortality, the period of normal life, and the period of wear-out.
Infant Mortality Period
The first part of the bathtub curve is the period of high failure rates, which is when babies die.
During this time, new assets are more likely to fail because of problems with the design, the materials, the way they were made, or the way they were started up.
Because of these flaws, assets fail early in their life cycle, which makes the failure rate rise during the first stages of operation.
Normal Life Period
After the infant mortality period, an asset enters the normal life period, where the rate of failure is low and fairly constant.
During this time, most of the problems have been fixed, and the asset is working as it should.
The asset is in great shape, and preventive maintenance can help it keep running well.
Wear-out Period
The last part of the bathtub curve is the wear-out phase, which has a higher rate of failure.
During this time, the asset is more likely to break down because of things like age, wear and tear, corrosion, or fatigue.
The asset has reached the end of its useful life, and it may need to be replaced or taken out of service to avoid a disaster.
Strategies to Extend an Asset's Useful Life
Teams working to extend the useful life of an asset can use what they know about the bathtub curve to set expectations for how the asset usually works over its life cycle.
Each point on the curve suggests a different way to avoid failing.
During the Infant Mortality Period, teams should focus on finding and fixing design flaws, material flaws, production flaws, or wrong ways to start up.
It may be necessary to do maintenance or inspections more often to find and fix problems before they cause failures.
During the normal life period, teams should focus on preventive maintenance to keep assets running at their best.
Routine inspections and maintenance can help find potential problems and fix them before they become big problems.
Wear-out Period: During this time, teams should focus on predictive maintenance to find problems before they happen and fix them.
To avoid catastrophic failures, it may be necessary to replace or sell some assets.
Advanced Analysis of the Bathtub Curve
Reliability experts often use a Weibull chart to look at a bathtub curve's cumulative distribution function.
Researchers from the University of Glasgow, the University of Cambridge, and Rolls-Royce have shown that the wear-out stage of the bathtub curve can be taken to a higher level and turned into the idea of the "bathtub surface
This advanced analysis helps to model how temperature, pressure, and stress, among other things, affect the wear and tear of an asset.
It gives useful information about how things wear out and helps improve the performance and reliability of assets.
Are you ready to use the Bathtub Curve to improve the reliability of your equipment?
Still hard to understand? Let me change the point of view a bit:
Are you sick of your things breaking down when you need them the most? Do you like the thrill of always having to buy new tools and gadgets that break?
Do not bother with the bathtub curve!
Who needs a reliable failure-rate curve when you can just wing it and hope for the best? After all, nothing gets the adrenaline going like a piece of equipment breaking down at the last minute.
But if you are a practical engineer who cares about safety and reliability, keep reading.
We are about to get into the fascinating world of the bathtub curve.
Okay, that was just a joke made to look like a TV ad.
Now let's go back to the explanation.
Factors Contributing to Each Phase
Each phase of the bathtub curve is caused by a number of things.
During the period of infant mortality, failures are caused by problems with how the product was made and how it was used.
During the normal life period, on the other hand, factors like maintenance and the environment can affect how long an asset lasts before it breaks.
Lastly, failure rates can go up in the wear-out period due to things like old parts and lack of maintenance.
Understanding these factors can help teams working to extend an asset's useful life by implementing specific strategies for each phase along the bathtub curve.
Risk and Probability Distribution
Infant Mortality Phase
In the early part of the bathtub curve, which is also called the "infant mortality phase," products are most likely to fail.
Failures during this time are usually caused by problems with the design, the materials, the way they were made, or the way they were started up.
For example, a newly bought house may have many problems in the first few years, like cracks in the walls and doors, because the materials or work are not very good.
During this phase, the people who are most at risk depend on the product being looked at.
People who buy electronics like smartphones or laptops when they are first released are most likely to have problems with them during the "infant mortality" phase.
On the other hand, companies that buy industrial equipment used in factories or power plants when it is first released are most likely to have problems with it during the "infant mortality" phase.
Probability Distribution
The bathtub curve is often represented by the Weibull distribution, which is a type of probability distribution.
It has a shape parameter (called beta) and a scale parameter (eta).
The bathtub curve is a plot of the failure rate over time, and the Weibull distribution can be used to describe the distribution of failures in all three phases of the curve.
Weibull Model
Modern semiconductor chips usually follow a Weibull model with a beta in the range of 0.2 to 0.6 for how often babies die before their first birthday.
The opposite of the failure rate is the mean time between failures (MTBF), which can be used to figure out what a product family is likely to do.
Strategies and Applications
Strategies to Reduce Early Phase Failures
The bathtub curve shows how likely it is that an asset will break down over time.
It has three distinct phases: failures in the early phase, random failures, and failures caused by wear and tear.
Early phase failures can be caused by mistakes in the design, in the materials, in the way the product is made, or in how it is started up.
Several strategies can be used to make it less likely that the first phase will fail.
Highly Accelerated Life Testing (HALT) is a way to find design flaws in products before they cause problems in the field.
This is done by putting products through extreme conditions.
Highly Accelerated Stress Screening (HASS) is a screening method that puts products through high levels of stress to find any weak parts before they fail in the field.
Design for Reliability, or DFR, is a method for making sure that products are built with reliability in mind from the start.
Design for Six Sigma, or DFSS, is a method that uses statistical tools to improve the reliability and quality of a product's design.
Burn-in is another strategy that involves putting products through a lot of stress for a long time to find any weak parts before they break in the field.
Using the Bathtub Curve for Maintenance Planning
The bathtub curve can also be used to make informed decisions about equipment maintenance and replacement.
If you know the three stages of the bathtub curve, you can change your maintenance plan as the bathtub ages.
During the infant mortality period, it is important to do preventive maintenance to find and fix any manufacturing flaws or installation mistakes that could lead to early failure.
During the normal life of a piece of equipment, it is important to do regular maintenance to keep it in good shape.
During the time when the equipment is worn out, it may be cheaper to replace it than to keep fixing it.
By looking at how equipment has broken down in the past, you can figure out where each piece of equipment falls on the "bathtub curve" and change your maintenance plan to fit.
For example, if you notice that a certain type of equipment tends to break down when it is getting old, you might want to replace it before it breaks or check on it more often during this time.
By using the bathtub curve as a guide for planning maintenance, you can extend the useful life of an asset while reducing the amount of time it is out of use and the amount it costs to fix.
Applications of the Bathtub Curve
The bathtub curve is often used in factories to make maintenance easier or to get production going quickly and reliably.
It can also be used to help understand why failures occur on certain assets and how to predict and prevent them.
The bathtub curve can be used in many different fields, including aviation, cars, electronics, medical equipment, oil and gas, power generation, transportation, and more.
For example, in aviation, it can be used to predict when aircraft parts will fail so that they can be replaced before they cause accidents.
In medical equipment, it can be used to predict when something will break down so that it can be replaced before it hurts a patient.
It can be used to predict when turbines will break down in power plants so that they can be fixed before they cause blackouts.
To sum up, the bathtub curve is a graph that shows how often an asset fails over time.
It is used in reliability engineering and modeling of how things break down over time.
There are three parts to the bathtub curve: infant mortality, useful life, and wear and tear.
Early phase failures can be less likely to happen if you use strategies like HALT, HASS, DFR, DFSS, and burn-in.
By using the bathtub curve as a guide for planning maintenance, the useful life of an asset can be extended while downtime and repair costs are kept to a minimum.
The bathtub curve is often used in many fields to make maintenance easier or start production quickly and reliably.
Modeling and Analysis
The rate at which software systems break down over time follows the same pattern as that of physical assets.
This lets owners of software systems understand their operational lifecycle and plan for when they will need to be replaced.
This text will talk about how the bathtub curve can be used in software engineering to model and analyze things.
Software Reliability Models
Software reliability models can be used by reliability engineers to model and study the "bathtub curve.
These models can be used to predict how often software systems will fail and to improve the way software is made.
Some software reliability models that can be used to model the bathtub curve are the Jelinski-Moranda (JM) model, the Musa-Okumoto (MO) model, and the Goel-Okumoto (GO) model.
Most of the time, these models look at how often a system fails over time, as well as other factors like software complexity, code quality, and testing coverage.
By looking at the failure data and using the right software reliability model, software engineers can figure out how likely it is that something will break and make plans for maintenance, testing, and replacement.
Statistical Process Control
Software engineers can also keep an eye on how well software systems work over time by using statistical process control (SPC) techniques.
SPC techniques can be used to find changes in how software works, figure out what causes failures, and fix problems before they happen.
In short, software engineers can model and analyze the bathtub curve by using software reliability models and statistical process control techniques.
These tools can help predict failure rates, improve the way software is made, and make software systems last longer.
By using these methods, software engineers can reduce the risks of software failures and keep the reliability of the system.
Bathtub Curve Explained (Reliability Curve)
Tip: Turn on the caption button if you need it. Choose “automatic translation” in the settings button, if you are not familiar with the spoken language. You may need to click on the language of the video first before your favorite language becomes available for translation.
Conclusion
As we come to the end of our discussion of the bathtub curve, it is clear that this idea is an important tool for any engineer or engineering student who wants to design, build, and maintain equipment that works as well as possible.
By knowing the three phases of the bathtub curve and the common factors that affect each phase, you can make smart decisions about maintenance, replacement, and overall risk management.
But before you leave this article, I want to challenge you to think about the bathtub curve in ways other than how it can be used in real life.
What can this curve tell us about how things work and why they fail? What can we learn about the fact that decay and decline are always going to happen?
These are deep and important questions that go beyond how the bathtub curve can be used in engineering.
But by thinking about them, we can get a better understanding of how important reliability engineering is in today's world.
So, as you move forward in your engineering career, remember the bathtub curve, not just as a tool for reliability but also as a symbol of how people fight against the forces of decay and decline.
And I hope that knowing this will inspire you to make equipment that will last, even as time wears it down.
Links and references
A Dynamic Failure Rate Forecasting Model for Service Parts Based on Bathtub Curve (BTC)
