Your Reliability Engineering Professional Development Site
I am constantly confronted by students, reliability engineers and other people banging fists on tables and saying …
… 89 percent of failures are random …
Firstly, 100 percent of failures are random. It’s just that there are lots of textbooks and experts telling us that a ‘random’ failure is one that happens irrespective of age. That is, a failure with a constant ‘failure rate’ where the item in question doesn’t appear to age or wear out.
The manufacturing sector has seen a rise in competitiveness because of the introduction of Industry 4.0 technologies over the last decade. It has become important for manufacturers to stand out in the industry by optimizing their systems to be as perfect as possible. One of the factors that make a factory competitive is its reliability, and a smart factory has great opportunities to improve it using the failure data from its assets.
The smart factories created using these technologies have greater efficiency and provide valuable data about the assets which can be used to further improve plant performance. The asset failure data gathered from different assets can provide insights into the reliability of assets and how to improve it. [Read more…]
The first step to any reliability improvement program is to define what data or information will be required to drive defect elimination. In the previous posts, the following topics were discussed;
All of the topics discussed what data is required and how to structure the data to implement a reliability engineering program or defect elimination process. Understanding the what, when and why set up and is required to setup a reliability engineering program. However, where many organization fail in this process is getting the data collected in a consistent and timely manner. [Read more…]
A few years into your reliability journey, you start to struggle to make the improvements you were able to when you first started.
Why is this? You were able to systematically eliminate all of the low hanging fruit using the existing data in your CMMS. But now you have to dig deeper to realize the improvements and that requires better data. [Read more…]
Beyond understanding what will fail (failure mechanisms) we need to know when a system will fail. Both kinds of information can be found in a variety of sources.
Unlike the stock market where we are regularly warned that past performance does not indicate future performance; in reliability engineering past performance is the best predictor of future performance. [Read more…]
The other day a student said they are asking for failure dates, not failure rates. That is a marked improvement when requesting information for the many sources of reliability information. It is not sufficient though.
Also ask for the type of failures expected or encountered during the testing or observation. [Read more…]