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Fred Schenkelberg — Thought Leader

Author of CRE Preparation Notes, Musings", NoMTBF, multiple books & ebooks>, co-host on Speaking of Reliability>/a>, and speaker in the Accendo Reliability Webinar Series.


This author's archive lists contributions of articles and episodes.

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Reliability Block Diagrams Overview and Value

Reliability Block Diagrams Overview and Value

A reliability block diagram is a graphical and mathematical model of the elements of a system permitting the calculation of system reliability given the reliability of the elements.

The model reflects the reliability performance structure including series, parallel, standby and other arrangements of system elements.

Each block in an RBD represents a component or subsystem of the system. The organization and connecting lines represent the reliability structure of the system and may or may not be representative of the system’s functional block diagram. [Read more…]

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Purpose of a Reliability Program

Purpose of a Reliability Program

The reliable performance of a system is important. It is important to the customer, to our business and to us.

Very few argue that we should ignore the reliability characteristics of a product. We also deem cost, time to market or feature set as important also. The trouble is we can measure the latter directly every day, where the reliability performance is often difficult to measure.

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Confounded DOE

Confounded DOE

A simple assumption in many experiments is to assume the variable act independently on the response.

This means when I change the temperature a little in a polymer dryer silo that time to achieve a certain dryness goes down. And, changing the humidity or airflow rate or pressure either do not change or as they change has no impact on the relationship between temperature and drying time. [Read more…]

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Life Cycle Cost Analysis and Reliability

Life Cycle Cost Analysis and Reliability

The purchase price of a product may be the start of the cost of ownership.

Maintenance and repairs may cost many times the initial purchase price. An efficient maintenance process and low-cost spare parts may help minimize cost, yet a design that optimizes the total cost of ownership is the most effective method. [Read more…]

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Duration and Reliability Goals

Duration and Reliability Goals

Coupled with probability is the duration over which the probability applies.

For example, if we desire 99 of 100 to survive, we must state over which period of time this applies. It is proper to state the couplet of 99% reliable over 1 year.

It is not sufficient to define reliability as ‘5-year product’ as it does not contain the information related to how many are expected to survive the 5 years. Likewise, it is not sufficient to say a product has 5 – 9’s reliability (meaning the probability of failure is less than 0.00001) as it does not contain the duration.

If the product has high reliability for only a few seconds, that does not help us make judgments about the first year of life. [Read more…]

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Nelson Funnel Experiment

Nelson Funnel Experiment

Dr. Nelson created a simple experiment to illustrate the effect of meddling with a stable process. (Dr. Deming did something similar)

While the intent of making small changes to a process may be to make improvements, these perturbations generally increase the variation of the results.

To improve a process requires understanding the source of the variation and often controlled experiments to identify process improvements. [Read more…]

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Probability and Reliability Goals

Probability and Reliability Goals

Roll the dice.

It is about that simple if any one product will survive to a specific time. Every product has a chance, not a guarantee. The time to failure for each product is a function of the use, stresses, assembly, latent defects or imperfections, and many other variables.

The result is generally unknown. And, we often establish a reliability goal that includes the probability of success. Keep in mind that a probability is only meaningful when defined over a specific duration.

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Individuals and Moving Range Charts

Individuals and Moving Range Charts

Another variation of the X-bar and R chart, in this case measuring and plotting individual readings instead of a sample average. The range value is obtained from the current reading and a fixed number of previous readings.

This type of control chart is suitable for calibration or testing situations where it is not practical to create subgroups of items for samples. [Read more…]

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Environment Element of Reliability Goal

Environment Element of Reliability Goal

This element of a reliability requirement answers the questions of where and under what conditions the product should operate.

It includes storage, transportation, and installation conditions too. One way to think of the environment is to consider the weather around the device. Temperature, humidity, preoccupation, etc.

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X-bar and Sigma Charts

X-bar and Sigma Charts

The s chart replaces the R chart and provides an increase in sensitivity to variation of the spread of the data.

The s-chart works better with 10 or more items per sample in order to obtain the s (standard deviation) estimate. The use of a spreadsheet or calculator expedites the calculation of the sample standard deviation.

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Introduction to Control Charts

Introduction to Control Charts

Control charts provide an ongoing statistical test to determine if the recent set of readings represents convincing evidence that a process has changed or not from an established stable average.

The test also checks the sample to sample variation to determine if the variation is within the established stable range. A stable process is predictable and a control chart provides the evidence that a process is stable or not.

Some control charts use a sample of items for each measurement. The sample average values tend to be normally distributed allowing straightforward construction and interpretation of the control charts. The center line of a chart is the process average. The control limits are generally set at plus or minus three standard deviations (of the sample means – commonly called the sampling error of the mean) from the grand average.

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