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In probability theory and statistics, the Weibull distribution is a continuous probability distribution. It models a broad range of random variables. Largely in the nature of a time to failure or time between events. It addresses mechanical or structural failures in the field of Reliability Engineering. By nature, the Weibull distribution provides a lot of information such as aging characteristics or expected asset lifetime. One of its most common outputs is the Bathtub Curve.
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Failure Modes and Effect Analysis (FMEA) is an excellent foundation for Reliability Programs. It is also a stepping stone to build Reliability, Availability, and Maintainability (RAM) models. Here lies the “beauty” of doing Reliability and Maintenance analysis. Whereby information from one analysis can help enhance another analysis.
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Concurrent or simultaneous failures can happen with redundant or spared systems. This means that both spared equipment can fail at the same time leaving the operator with no production output. For example, we have two alternating pumps operating in a parallel configuration. Each one acts as a spare and at any one time can take over if the other one fails. This article is based on a question I was asked during a recent industry presentation. I thought the example was interesting and informative enough to share with the Maintenance and Reliability community.
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Assets or components in an operation can be dependent on each other. The network of equipment contributing to the operation output is complex and intricate. A RAM model helps account for the complexities including dependencies.
The fundamental purpose of Reliability, Availability, and Maintainability (RAM) modeling is quantifying system performance, typically in a future interval of time. A system is a collection of items whose coordinated operation leads to the output, generally a production value. The collection of items includes subsystems, components, software, human operations, etc. For example, an automobile is a system. Its sub-components being the drivetrain, engine, gearbox, etc. In RAM models, it is crucial to account for relationships or dependencies between items. This helps determine the final output of the system. In various industries, RAM models have proven to be effective as cost avoidance or decision-making tools, as well as their ability to confirm or counter stated assumptions by internal stakeholders.
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It is becoming more apparent that companies are treating their operational data with the “respect” deserved. About time and if not, then they should start now. Operational Data is typically stored in the CMMS (Computer Maintenance Management System) or historical records repositories.
Data is knowledge. Data is power. Data is the basis of good decision making.
This article also suggests that data could be revenue generator. Therefore, a company’s data is akin to extracting gold from a mine. Like gold, data requires hard work to get and hold on to. Yet, it is highly lucrative when exploited, refined, and traded. The organization running the equipment generates the Operational Data. It can also be generated by a third party. In the following paragraphs, we see the untapped opportunities data and historical records can offer.
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Adding equipment redundancy to a system can improve uptime and reliability leading to increased output. When adding new equipment, it is cheaper to evaluate the benefits, or lack of thereof, on paper before implementing the change. Typically done as part of the design phase. However it can also happen after commissioning but its is more expensive. In other words, its better to get it right before “shovels go in the ground”.
[Read more…]Paul H. Barringer invented the Barringer Process Reliability (BPR) process. Paul was a fellow Reliability Engineer “extraordinaire” and an outstanding mentor for myself and countless others in this field of practice. BPR highlights operational issues. If not addressed or mitigated, those could have significant revenue impacts on an operation. A BPR analysis uses the Weibull probability plot which happens to be a very well-known tool in the field of Reliability Engineering. On one side of a sheet of paper only, the BPR plot can tell the true “story” on the operation. One of those “stories” relates to high impact events best tackled by a Root Cause Analysis.
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A parametric Life Analysis involves “forcing” or “imposing” a distribution’s parameters on a data set in order to obtain the “best fit”. However, it can lead to errors in results. The non-parametric estimation suggests that there are other approaches though not necessarily the easiest or “most elegant” ones. In the field of reliability engineering, we tend to like something so much that we use them in every “sauce”. A classic example is the Weibull Distribution. It has become so popular that Life Analysis is also known as a “Weibull Analysis”. As a reminder, the Weibull distribution is only one parametric distribution amongst a myriad of others, invented by Walodi Weibull in 1937. Dr Bob Abernathy’s New Weibull Handbook1 quotes: “the Weibull distribution provides reasonably accurate failure and failure forecasts……”. Thus, parametric distributions are good enough but not perfect to make a decision.
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Traditional spare parts or inventory calculation methods involving Min-Max levels lack rigor, especially for critical equipment. Reliability, Availability and Maintainability (RAM) models provide a better approach to this calculation.
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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.
In line with the RAM acronym sequence, we often start and go no further than the “R” in Reliability. In doing so, we forget about the “M”. The question often asked is: “what is the reliability of the system?” But rarely asked is: “what is the maintainability of the same system?” Myself, am guilty of this omission. Hence this article to remind myself and you the reader, of the importance of Maintainability in industry.
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A proper CMMS (Computerized Maintenance Management System) setup can make a world of difference in an organization’s asset management journey. Conversely, a substandard setup can be a living hell for Reliability Engineers like myself and other analysts. I have personally wasted hundreds of hours of my work life sifting through a poor CMMS structure trying to find records. Ever wanted to make your Reliability Analysts more productive and engaged? If yes, then this article highlights 10 highly recommended set-up requirements.
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Building a Reliability, Availability and Maintainability (RAM) model can provide numerous benefits to an Asset Management program. This includes conducting a Criticality Analysis. Criticality Analysis involves ranking assets based on their potential risk to the organization. It considers risk categories such as Production Impact, Safety, Environment, and Reputation, amongst others. The category list can vary and is a reflection of the organization’s structure. Organizations generally have a finite financial pool allocated to preserving asset function. A Criticality Analysis helps optimize the allocation of resources to each asset according to its importance in the system network. Ultimately this helps maximize revenue, minimize spend and maintain the organization’s license to operate.
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A proper Computer Maintenance Management System (CMMS) setup can make a world of difference in an organization’s asset management journey. Conversely, a substandard setup can be a living hell for Reliability Engineers like myself and other analysts. This article highlights some practices to avoid in order to gain the most out of your CMMS in the long run. The examples given are based on my own experience in industry. There could be many more learnings out there. Reader comments and suggestions are most welcome as this will benefit the entire Maintenance and Reliability community.
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There is a definite distinction between Reliability Engineers (RE) and Maintenance Engineers (ME). Although they are highly dependant on each other. REs rely a lot on MEs and vice versa. However, those roles are often confused let also not well understood by recruiters, managers or regrettably, even professionals in the roles.
In a Reliabilityweb.com article, Paul Barringer provides a good summary of this distinction. He also underlines the vital partnership between those specialties. Some aspects highlighted by Barringer are:
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