The Reliability Mindset: Practical Applications in Industry

I have often heard employees complain they have no data to initiate a proper Reliability Improvement Program. This is not always true. And to no fault of theirs. They just don’t know how to use what they already have in terms of records. If you are running an operation, you should at least have production records – i.e. how much you are producing on a daily basis. If you don’t have this, then maybe you should not be in business at all. This article looks at ways to initiate a Reliability Program using the Barringer Process Reliability (BPR) methodology. The greatest advantage of this methodology is that it only requires productions records as an input.  That is how many units of production the plant produces on a daily basis.  For example, the barrels of crude oil processed per day in a refinery. Or the hectoliters of beer brewed in a brewery daily.

Failure Patterns according to Moubray

In his book Reliability Centered Maintenance¹, John Moubray highlights 6 patterns of failure. However, one needs to be careful about how those patterns are interpreted and used. Or misused. These 6 failure patterns are as follows:

• A: Bathtub Pattern
• B: Age Related or Wear Out Pattern
• C: Fatigue Pattern
• D: Initial Break-in Pattern
• E: Random Pattern
• F: Infant Mortality Pattern

Definition of a PF Curve

A PF curve is a graphical tool used in the field of maintenance and reliability. It essentially illustrates a component’s health degradation over its lifetime. As well as a visual guide on when to conduct appropriate action to minimizes operational risks related to unplanned failures. It is essentially a planning tool.

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.

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.

The Concept of Equipment Redundancy

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”.  

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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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.

CMMS overview

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.