on Maintenance Reliability

The reliability definition in relation to asset failures

Maintenance and Reliability professionals deal with equipment failures all the time. However, the word “failure” could have different definitions or thresholds. In order to take adequate and effective action, it is important to have clear specifications for what a “failure” truly is.

Introducing confidence boundaries

Confidence boundaries can be confusing to reliability engineering practitioners and their audience. Yet, they can play an important role in the risk-based decision-making process. When building statistical models, there is always uncertainty around the model because it is usually based on a smaller sample of the studied population. The confidence interval is the range of values you expect your model to fall between a certain percentage of the time if you run your experiment again or re-sample the population similarly. For example, using a 90% confidence boundary, one would expect 90% of the records to fall between the upper and lower confidence boundaries. As a rule of thumb, the more data you have, the more precise the model and the narrower the confidence boundaries.  In essence, if we have an infinite amount of data, we will end up with a perfect model. However, this is never the case. Confidence boundaries help establish the accuracy of the model and also provide some information on the validity of the data.

The concept of degradation variables

Assets typically age over time, leading to degraded performance and loss of function. Asset life models are built in order to predict future degradation patterns. Those models are based on asset degradation variables such as time or usage. Those variables could be for example, time between failures or distance covered between failures. Many assets have more than one degradation variable. In this case, it is important to define which of the multiple variables is the dominant one and will subsequently provide the Reliability Engineer with the most precise life model. 

Reliability is a probability. Specifically, the probability that a system will perform its intended function within a specified mission time and under specific process conditions. Therefore, most reliability calculations incorporate a time element as a degradation variable. Generally, when building life models, we default to using calendar time as it is more straightforward. We have had tools to easily measure elapsed calendar time for centuries now. [Read more…]

Reliability, Availability, and Maintainability (RAM) modeling overview

The concept of Reliability Block Diagrams (RBD) is also known as Reliability Modeling or Reliability, Availability, Maintainability (RAM) analysis. With RAM models, the interaction of large, complex, and multi-layered systems can be analyzed using Monte Carlo simulation methods. This help quantify the output of the entire system with greater accuracy than other estimating tools or methods. [Read more…]