Accendo Reliability Webinar Series

Let's discuss and understand the basics of hazard analysis as it is the systematic process identify and control safety risks

In this short webinar let’s discuss when and why you would use Weibull analysis, the basic process, and limitations.

Let’s consider the most expensive aspect of your reliability program. It’s not testing.

What’s coming to reliability engineering in 2020 and beyond? Let’s explore a few trends and their implications.

What’s coming to reliability engineering in 2020 and beyond? Let’s explore a few trends and their implications.

Having a reliability target for your product is great. But how does that help all the little design teams

Having a reliability target for your product is great. But how does that help all the little design teams

Let’s take a look at a few ways to really learn what you need to know along your journey to become a reliability engineer.

at the heart of statistics are these intimidating functions like ‘probability density functions’ and ‘cumulative distribution functions.’

Ever wondered if your servicing intervals are ‘good’? Do you know what ‘good’ is? Are you servicing too much or too little?

Minitab itself has many reliability functions available; this presentation covers the basics.

If reliability engineering ends up costing you money, then you are doing it wrong.

This webinar will open your eyes to this important perspective. its’ so simple and has made many heroes in the data analysis world since Abraham Ward.

Some of you may have heard of ‘Bayesian analysis.’ You may think this is something fancy that only universities do.

Let’s discuss the concept of failures along with a few other concepts universal to reliability engineering.

Warranty returns are a great place to start for setting targets for new products. But how do you translate that to specific numbers to design to?

So how do you convert reliability data analysis into useful information for that decision?

Your science, engineering, and math formal training will serve you well as a reliability engineer, and that is not sufficient to be successful.

So how do you convert reliability data analysis into useful information for that decision?

Using a particular formula requires understanding the purpose, limitations, and assumptions involved. It also requires using the right formula.

The goal of this presentation is to educate the audience of options to prevent electronic equipment failure used in enclosed spaces from corrosion.

So how do you convert reliability data analysis into useful information for that decision?

Let’s talk about risk, risk management, and our role in identifying and mitigating risks.

Let's get into the details and code to make this analysis happen.

Looking Forward with Reliability Engineering is about understanding the decisions that the information we should create will inform.

In my experience, there are three basic approaches to ALT: Test to pass, test to failure, and degradation testing. Each is good for specific situations

you can use your brain to work out the right number of spares you need and save lots, and lots of money.

Acceleration factors translate one stress level to another, which is rather useful for accelerated life test interpretation.

Let's explore a set of data with a Weibull plot and enjoy reliability making money

How can you make reliability relevant in your organization - plus a few ways to know it is not relevant.

Let's explore the many elements that become inputs to creating a plan for your next accelerated life test.

Exploring ten reasons to do FMEAs, yet actually, reasons to do amazing FMEAs.

This webinar takes you through the key steps of decision-making – particularly relating to reliability engineering.

This webinar takes you through a framework to explore the understand the root cause of a failure

There comes a time in everyone’s reliability ‘journey’ where it is either too hard or too expensive to keep perfecting your product so make it fault tolerant.

Let’s explore the many reasons to conduct testing and how to clearly link those tests to the decisions that rely on the test results.

Visit a vehicle scrap yard to witness the impact of ‘getting a new car’ has on the environment. Let’s explore the many ways creating a reliable product is beneficial to the environment.

OK … that is a little macabre. But reliability engineers need to ask themselves this question a lot.

Visit a vehicle scrap yard to witness the impact of ‘getting a new car’ has on the environment. Let’s explore the many ways creating a reliable product is beneficial to the environment.

Then comes the part where we have to work out how many of them we need (if they make up a fleet) or how many spare parts we need to keep them running.

Other than the situation where you build your product inside your customer’s facility, your product will require some form of transportation to move from your factory to your customer.

how do you go about telling a memorable story about a Weibull plot of field data? Or, a mean cumulative plot of a repairable system?

The essence of the idea behind the site is to provide our community with great content to help you solve problems, learn, and improve.

Let’s explore the many ways we use, or should use, statistics in our engineering role. From gathering data to presenting, from analyzing to comparing

You may have heard of Statistical Process Control (SPC), 6-sigma, Shewhart, the Japanese Economic Miracle, X̅ – R Charts … or perhaps none of them.

Let’s talk about how quality and reliability overlap and rely on each other's set of capabilities, plus where we differ.

In short, a process is considered ‘capable’ if it creates products that in spite of all the unavoidable natural variation in production still fall within the boundaries of what ‘good’ looks like.

Let’s explore the overlap and differences between a safe and a reliable product or system.

This webinar introduces you to the topic of reliability growth (both qualitative and quantitative) along with key concepts

Let’s explore what residuals are, where they come from, and how to evaluate them to detect if the fitted line (model) is adequate or not.

there are ways you can suck out information from a group of experts in a quantifiable and remarkably accurate way.