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Asset Lifecycle Management for Zero Defects and Zero Risks

Asset Lifecycle Management for Zero Defects and Zero Risks

Every physical asset has a lifecycle starting at conception through to its eventual disposal and site remediation. In each phase of the lifecycle errors can occur that cause defects within the asset. In operations with hundreds of assets there easily can be tens-of-thousands of defects. Each defect is a risk that eventually contributes to breakdowns, losses, wastes, safety incidents during operation, and shortens the asset’s service lifetime. Eliminating thousands of defects and risks forever is what Plant Wellness Way EAM does for its users

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Can Starting with Waterfall Lead to Better Agile?

Can Starting with Waterfall Lead to Better Agile?

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

We should all be aware by now that business agility is the primary enabler for companies seeking sustainability.  In the past, companies would evolve in chunks, a project at a time.  The costs and risks of implementing change, whether system-related or otherwise, were large, so designing and planning upfront (the Waterfall approach) was applied to create predictability and moderate risks.

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Kaplan-Meier Ignores Cohort Variability!

Kaplan-Meier Ignores Cohort Variability!

The Kaplan-Meier reliability estimator is the nonparametric, maximum likelihood estimator from right-censored, grouped lifetime data. It has been used since publication, most statistics programs do it, and it has been taught since I was in school. I give away a spreadsheet version. 

Lifetime data requires tracking individual subjects or units from their start to failure, death, or censoring. Data may be collected periodically grouped by cohorts: monthly sales, ships, or other collections of individuals, subjects, or units and each cohort’s lifetimes. Data could be displayed in a “Nevada” table with random cohorts in one column, and each cohort’s lifetimes grouped in periodic age-at-failure intervals in columns to the right [Schenkelberg].

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Calculating when to Economically replace Assets

Calculating when to Economically replace Assets

Optimum replacement time – Theory

The optimal asset replacement time calculation has one goal, and that is to calculate the lifetime of when assets should be operational as to have the lowest possible operating cost over its lifetime.

This means balancing two opposing forces, namely the increasing operational cost and unreliability of the asset as it ages, with the decreasing effective cost of ownership. [Read more…]

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Time to Event Analysis: An Introduction

Time to Event Analysis: An Introduction
Part of the Statistical Methods for Failure-Time Data article series.

Introduction

I am currently reading the book Survival Analysis: Techniques for Censored and Truncated Data, Second Edition (John P. Klein and Melvin L. Moescheberger). Although the techniques presented in this book focus on applications in biology and medicine, the same statistical tools can also be applied to disciplines ranging from engineering to economics and demography. I have a background in mechanical engineering and am interested in applying survival modeling concepts to data from reliability engineering, manufacturing and quality assurance. This article is the first of, hopefully, many articles that I intend to write as I finish reading different chapters from the book.

The data set(s) that will be analysed are the ones that have been used as examples in another book: Statistical Methods for Reliability Data, Second Edition (William Q. Meeker, Luis A. Escobar, Francis G. Pascual). Both the books I mentioned are excellent resources for anyone who is interested in learning more about this topic.

In this article, we will analyze vehicle shock absorber failure time data Failure time data is also known as survival data, life data, event-time data or reliability data, depending on the field of study. and estimate a few basic survival quantities. The data contains failure times (in kilometers driven) and the mode of failure, first reported by O’Connor (1985) O’Connor, P. D. T. (1985). Practical Reliability Engineering. Wiley. [54, 610]. We will ignore the mode of failure for now and will only consider whether a failure occurred or not, i.e., censored. In a future article, I plan to use the different failure modes to discuss competing risks for time-to-failure data.

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Lifetime Evaluation v Measurement

Lifetime Evaluation v Measurement

Lifetime: Evaluation vs. Measurement

Guest Post by Oleg Ivanov

How can we tell whether an iron is hot enough? The answer is obvious: We can measure temperature by using a thermocouple and a meter. But, in practice, we lick our finger and touch the iron. Sizzle…. Yes, it’s hot!

We know a priori the boiling temperature of water and we can evaluate the temperature of the iron. This method has a lower cost. [Read more…]

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15 Ways to Safely Reduce the Size of Your Maintenance Crew!

15 Ways to Safely Reduce the Size of Your Maintenance Crew!

 Discover 15 ways to operate so there is less maintenance, fewer maintenance people, lower maintenance costs and (because there is less maintenance) your plant runs reliably for longer! Most of the 15 topics covered are new to operations and maintenance. Each one reduces downtime, save money and improves operating uptime and performance. 

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Project Assurances

Project Assurances

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

We have many kinds of assurance in the project management world.  These, generically include business assurance to track the economic viability of a project, technical assurance for the technical integrity of a product, process or system, and user assurance to check that specified requirements have been met.  Assurance demonstrates compliance to a project’s business case, meeting end user’s operational requirements, and that there is technical compliance against codes, standards or specifications.

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Three Ways Great Facilitators Anticipate Trouble

Three Ways Great Facilitators Anticipate Trouble

Great facilitators anticipate trouble when guiding groups. We normally consider conflict between the participants as the most likely disruptor. However, facilitators should be prepared to overcome a handful of disruptor types in collaborative sessions. This article discusses technology misfires and provides three case examples with solutions.

Virtual Meetings Get Zoom-Bombed

We’ve been having monthly Pee Dee River Basin Council meetings for nearly two years. I serve as the facilitator of the 25-person group and Clemson’s University’s Tom Walker is the logistics coordinator. The meetings are in a hybrid format that allows the public and stakeholders to view the meetings virtually. The meeting was Zoom-bombed mid-way through the January 2024 meeting.

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Futility of Using MTBF to Design an ALT

Futility of Using MTBF to Design an ALT

Futility of Using MTBF to Design an ALT

Let’s say we want to characterize the reliability performance of a vendor’s device. We’re considering including the device within our system, if and only if, it will survive 5 years reasonably well.

The vendor’s data sheet lists an MTBF value of 200,000 hours. A call to the vendor and search of their site doesn’t reveal any additional reliability information. MTBF is all we have.

We don’t trust it. Which is wise.

Now we want to run an ALT to estimate a time to failure distribution for the device. The intent is to use an acceleration model to accelerate the testing and a time to failure model to adjust to our various expected use conditions.

Given the device, a small interface module with a few buttons, electronics, a display and enclosure, and the data sheet with MTBF, how can we design a meaningful ALT? [Read more…]