Your Reliability Engineering Professional Development Site
Find all articles across all article series listed in reverse chronological order.
In this edition of the Prelical Video Podcast, we will be exploring techniques for data collection when performing failure analysis. You can learn more from our book on Root Cause Analysis entitled Root Cause Analysis – Improving Performance for Bottom-Line Results.
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Many of us like to think that we can, at least sometimes, be objective. Which is the opposite of being subjective.
What does this mean? Something is ‘objective’ if it only depends on the world around it, and nothing else. Like the ‘perfect juror’ who is only swayed by facts and evidence when determining if he or she thinks someone is guilty of murder. Something is ‘subjective’ if it can be influenced … by itself. Like the ‘imperfect juror’ who decides to acquit a murder suspect regardless of the evidence … because the suspect is his or her brother. This is called bias.
A Business Case Evaluation (BCE) is a decision-making tool that assists in making value-based funding decisions. Business cases are developed in the early capital and operating budget planning cycles as a standardized and systematic process to analyze the benefits and costs of various options to solve an identified problem or a cost-saving/revenue-generating idea. A big part of building better business cases is analyzing the system over the long term for monetary and non-monetary aspects. Building better business cases improves reliability and performance by formally documenting “needs” versus “wants.” Business cases serve as a powerful communication tool at all levels of an organization. [Read more…]
RCM is one of the most powerful maintenance and reliability improvement processes out there. The name Reliability Centered Maintenance lends itself to a process that’s used to develop proactive maintenance for an asset – and it is. But RCM can be used to formulate scores of solutions that reach far beyond maintenance.
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Giving a presentation last week and asked if anyone uses an 85%RH/85°C type test, and a couple indicated they did. I then asked why.
The response was – just because. We have always done it, or it’s a standard, or customers expected it. The most honest response was, ‘I don’t know’.
Why is the test being done? Who is using the information for a decision? What is the value of the test results? If ‘just because’ is the best you can say about a test, why do it?
An MTBF calculation is often done to generate an indicator of plant and equipment reliability. An MTBF value is the average time between failures. There are serious dangers with the use of MTBF that need to be addressed when you do an MTBF calculation.
Take a look at the diagram below representing a period in the life of an imaginary production line. What is the MTBF formula to use for the period of interest to represent the production line’s reliability over that time? [Read more…]
Dianna Deeney in her piece in CERM Insights #362 notes that big data may provide the quality profession with more professional opportunities. Dr Anil Maheshwari in his book Data Analytics: Made Accessible specifically indicates that quality management will benefit from big data. (1)
There can be no doubt that big data is an important environmental change and challenge for most businesses. It changes the environment because with increased computer storage and computational power, companies can access and evaluate more and more information about process performance and customer satisfaction. It is a challenge because the field is relatively new, and management is still trying to understand how the information can best be applied.
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Ramesh Gulati and George Williams discuss Certifications and the importance of obtaining them.
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Lifetime data is nice to have, but lifetime data is not necessary! Generally Accepted Accounting Principles require statistically sufficient data to estimate nonparametric reliability and failure rate functions. Some work is required!
ISO 14224 “Petroleum, Petrochemical and Natural Gas Industries—Collection and Exchange of Reliability and Maintenance Data for Equipment” requires lifetime data to estimate exponential or Weibull reliability functions! Sales or ships and returns or failure counts are statistically sufficient to make nonparametric estimates of reliability and failure rate functions, without unwarranted distribution assumptions or lifetime data!
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One of the weak areas in designing parts is deciding tolerances of various parts. We have shared a video of statistical tolerancing for assembly of parts. Many viewers have expressed that we should also post a video of application of Monte Carlo Simulation for tolerance design when there is a transfer function that relates the input parameters to an output variable. We therefore present in this video an application example of designing tolerance for a helical spring using Monte Carlo Simulation. The video explains this procedure step-by-step using Simular software. I have used Simular software to demonstrate this with a practical example of spring. Simular is a free software (emailware) which can be downloaded from https://www.simularsoft.com.ar/. However, one can use other software such as Crystal Ball, @Risk etc. for such analysis. Tolerance design is usually an essential step in Design for Six Sigma (DFSS) projects.
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A machine run-off, refers to the process of testing and adjusting a new or modified machine or piece of equipment before it is put into regular use. When a run-off is performed prior to shipping to the customer, it is called a Factory Acceptance Test (FAT), and when it is performed after installation at the customer’s facility it is called a Site Acceptance Test (SAT).
Both types of machine run-offs are common with large, complex, and/or expensive equipment. And both have the same goal of ensuring the equipment is safe and reliable, and meets the customer’s requirements and functional criteria before it is launched into production where repairs and corrections become much more expensive. The SAT is largely a repeat of the FAT expect it additionally verifies that no damage was incurred during shipment and that the unit is correctly installed.
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Maintenance Optimization is a Reliability Engineering process which helps organizations avoid unnecessary spend whilst minimizing the risk of a costly failure. Planned replacements or inspections detect or prevent failures for components or systems with increasing failure rates. This improves asset reliability and helps control maintenance spend. Increasing failure rates refer to having a Weibull distribution shape parameter Beta (β) greater than one. Specifically, the failure rates located in the right section of the bath tub curve as shown in Diagram 1 below. Admittedly, the life characteristics have to follow a Weibull distribution in this case.
I’ve often railed on and on about the inappropriate use of MTBF over Reliability. The often cited rationale is, “it is simpler”. And, I agree, making simplifications is often necessary for any engineering analysis.
It goes too far when there isn’t any reason to knowingly simply when the results are misleading, inaccurate or simply wrong. The cost of making a poor decision based on faulty analysis is inexcusable.
When engineers design a machinery part, they begin by defining the operating load range it will experience during its service life. This range will include the loads when the machine part is not working through to the maximum stress that it will operate under. Examples of high stress situations include operating overload events, or when starting up under a large load. How great the imposed stress reaches and how often those events occur changes a part’s reliability.
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Today with robotic development, digital transformation, Artificial Intelligence, and other projects t will involve a multi-discipline team with many interfaces. To keep budget and schedule on these types of projects, inputs and outputs from all disciplines must be accurate, complete and timely. If not, your project will be risk.
All tasks (Work Packages) have a manager. Inputs from another task manager to start your task is your supplier. Output from your task goes to another task manager. In this case you become a customer. Every task manager is a supplier and a customer.
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