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Do the Best You Can With Available Data?

Do the Best You Can With Available Data?

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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Case Study in Tolerance Design of a Spring using Monte Carlo Simulation

Case Study in Tolerance Design of a Spring using Monte Carlo Simulation

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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Machine Run-Off’s: What Are They and Why Do Them

Machine Run-Off’s: What Are They and Why Do Them

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 Interval Optimization – Identifying the best Maintenance Strategy

Maintenance Interval Optimization – Identifying the best Maintenance Strategy

What is Maintenance Optimization?

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.  

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Use the Right Fit

Use the Right Fit

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.

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Designing with Physics of Failure

Designing with Physics of Failure

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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Internal Supplier Performance Can Be a Risk to Your Project

Internal Supplier Performance Can Be a Risk to Your Project

Guest Post by John Ayers (first posted on CERM ® RISK INSIGHTS – reposted here with permission)

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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Life Data Analysis of Right Censored data using Minitab Software

Life Data Analysis of Right Censored data using Minitab Software

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:

  • 1. Identify the distribution that best fits our data
  • 2. Estimate parameters of the selected distribution
  • 3. Estimate reliability or probability of failure at specified time(s)

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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Why Use Nonparametric Reliability Statistics?

Why Use Nonparametric Reliability Statistics?

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?

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The Rising Tide of Young Professionals: Overcoming Intergenerational Communication Gaps in Reliability Engineering

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.

Some General Thoughts on Intergenerational Communication

Here are a few thoughts on intergenerational communication from my recent interview on The YoPro Know Podcast.

On Using Social Media

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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Boeing is Steering Harder into its Spiral of Death

Boeing is Steering Harder into its Spiral of Death

Boeing has somehow managed to make the bad public relations created by those pesky onboard batteries catching fire in 2013 practically disappear. Not through good management. But through a never-ending series of disasters and catastrophes that shows no sign of letting up which is dominating Boeing’s news cycle that there is no remaining airtime for missteps like those battery fires.

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The Language We Use Matters

The Language We Use Matters

During RAMS this year, Wayne Nelson made the point that language matters. One specific example was the substitution of ‘convincing’ for ‘statistically significant’ in an effort to clearly convey the ability of a test result to sway the reader. For example ‘the test data clearly demonstrates…’

As reliability professionals let’s say what we mean in a clear and unambiguous manner.

Thus, you may suspect, this topic is related to MTBF.

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