Your Reliability Engineering Professional Development Site
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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Reliability Centered Maintenance principles will be celebrating their golden anniversary soon. And you may be surprised where the process finds its roots!
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I am happy to share my next video on ‘Life Data Analysis of Right Censored Data using Minitab Software’ as many viewers had requested! In this video, we revisit the types of failure data and explain procedure to analyse right censored data in Minitab software with an example.
The procedure is predominantly in three steps:
The procedure is explained in detail using an application example of camshaft failure data. In the video, I have also explained how to estimate expected number of failures by 100000 Kilometres and also the 95% upper bound which is the worst-case scenario. I am sure, you will find this video interesting and useful for practical application of Life Data Analysis! Your feedback on the video is welcome!
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Fred asked me to explain why use nonparametric statistics? The answer is reality. Reality trumps opinion, mathematical convenience, and tradition. Reality is more interesting, but quantifying reality takes work, especially if you track lifetimes. Using field reliability reality provides credibility and could reduce uncertainty due to tradition and unwarranted, unverified assumptions.
Data is inherently nonparametric. Cardinal numbers are used for period counts: cohorts, cases, failures, etc. Accounting data is numerical; it is derived from data or from dollars required by GAAP (Generally Accepted Accounting Principles); e.g., revenue = price*(products sold), service cost = (Cost per service)*(Number of services), or numbers of spare parts sold. Why not do nonparametric reliability estimation, with or without lifetime data?
[Read more…]Young professionals (under 40) will make up over half of the workforce by the end of next year. That means communication between young professionals and experienced professionals is essential for workplace success. Younger professionals dominating the workforce means that reliability engineering is undergoing formative changes. These are a few general thoughts on the impacts of the changing workforce and five ways it will impact reliability engineering.
Here are a few thoughts on intergenerational communication from my recent interview on The YoPro Know Podcast.
Companies are generally awkward with social media and how to reach the younger generations. On the one hand, companies know to try. On the other hand, the HR people and some of the technical managers are really bad at it.
Companies should do a gap analysis on reaching young professionals. There are inadvertent things companies do that really cause them to stumble inadvertently. Most companies do too much of a cookie-cutter approach to recruiting and retention.
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In this episode we will discuss the two main types of failures and how to go about developing a bad actor equipment list.
