Your Reliability Engineering Professional Development Site
The mathematical and/or graphical analysis of time-to-failure data of items in use by customers. Analysis of field data enables an estimate of the actual field time-to-failure distribution or failure rate.
The Kaplan-Meier estimator is the maximum likelihood, nonparametric reliability estimator for censored, grouped lifetime data. It’s traditional. It’s in statistical software. Greenwood’s variance formula is well known. Could Kaplan-Meier be improved: smaller variance, better actuarial forecasts, seasonality, separate cohort variability from reliability? Could you estimate reliability without life data and preserve privacy?
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Historically, Reliability Engineering of Electronics has been dominated by the belief that 1) The life or percentage of complex hardware failures that occur over time can be estimated, predicted, or modeled, and 2) the Reliability of electronic systems can be calculated or estimated through statistical and probabilistic methods to improve hardware reliability. The amazing thing about this is that during the many decades that reliability
[Read more…]This is the wheel of the Curiosity Rover after millions of rotations on Mars.
This is how I feel after I ask customers about legacy product performance. [Read more…]
By the time a product fails in the field, the design team is focused on the next design.
They are looking to the future and not looking for field reliability feedback. We know that each failure contains valuable information.
We, as reliability professionals, often work to create as much useful information concerning failure modes and mechanisms as possible. We want to improve the design.
Yet, what happens when the design team has moved on to the next project? When the expertise to effectively make changes to the design to improve product reliability performance is no longer paid to work on the previous design?
What can you do to engage the right people to implement the necessary changes?
Here are a few ideas that I’ve seen used to effectively make good use of field failures to create meaningful field reliability feedback. [Read more…]
Field data analysis starts with the collection of data.
In a previous article, we used a Nevada chart to gather the counts per month of field failure data. The chart also provides the necessary data to account for how many units have not failed as of yet.
The Nevada chart on its own is just a table of numbers and does not reveal patterns of the changing nature of failure rates over time. Are we experiencing early life failures or wear-out related failures?
We need to conduct some data analysis to learn what message the data contains. [Read more…]
It is rare to have insight into any internal company history of serious electronic and electromechanical failures. Failure analysis and the causes of electronics or electromechanical systems failure can be a difficult investigation for any manufacturing company. Disclosure of the history and data is rarely if ever published due to the potential liability and litigation costs as well as loss of reputation for reliability and safety.
A common and poor technique to gather field data is to count the number of returns by week or month. This can provide a graph showing the number of returns over time.
It hides information you need to understand your field failures.
Let’s take a look at a way to gather the same field failure data and retain the critical information necessary for time to failure analysis. [Read more…]
Fielded products fail day by day. Customers report these failures generally seeking a way to remedy this issue. Gathering the reported or returned products or confirmed failures is common practice.
Depending on the product a simple replacement or exchange may suffice. For other products, repair or a refund may be appropriate.
In general, and not always, when a product fails in the hands of a customer, the organization designing, manufacturing and distributing the product learns of the failure. [Read more…]
Customers experience product failures.
Understanding these failures that occur in the hands of customers is an essential undertaking. We need this information to identify increasing failure rates, component batch or assembly errors, or design mistakes. [Read more…]
Historically Reliability Engineering of Electronics has been dominated by the belief that 1) The life or percentage of complex hardware failures that occurs over time can be estimated, predicted, or modeled and 2) Reliability of electronic systems can be calculated or estimated through statistical and probabilistic methods to improve hardware reliability. The amazing thing about this is that during the many decades that reliabilityengineers have been taught this and believe that this is true, there is little if any empirical field data from the vast majority of verified failures that shows any correlation with calculated predictions of failure rates.
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We rely on data to make decisions, to reveal patterns or trends, to learn about our systems and world. Data has many forms and sources. Reliability data may provide what will fail and/or when a device will fail. [Read more…]