Your Reliability Engineering Professional Development Site
A listing in reverse chronological order of these article series:
In the 19th century, factories and mills were major concentrations of capital. Manufacturing completed for investment money, and business cases could be as closely examined as any other risky investment. In 1884, Edwin Matheson wrote about how maintenance affected accounting and business prospects in The Depreciation of Factories and their Valuation. Matheson’s book became the basis of modern views of depreciation.
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.
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Every Reliability Engineer will be familiar with the Weibull Analysis. Most of us even have an Excel template laying around that we refer to!
The problem is, that when we have to handle Suspended data (e.g. components that haven’t failed yet at time of observation), the Excel sheet must use VBA in the background if the user wants a “single-button” tool.
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Over thirty years ago, Steven R. Covey, renowned author and business management guru, introduced to us The 7 Habits of Highly Effective People, which presented an approach to being effective in attaining personal or business goals by aligning to what he called “True North” principles based on character ethics. This book has become a best seller, a must-read, and has sold 40 million copies worldwide.
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One of the common questions teams have when they first explore using Predictive Maintenance is “Is the data good enough to perform the analysis?” Answer to that question is nuanced with the reliability objective and the quality of the data available.
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Let a Plant Wellness Way EAM System-of-Reliability halve your Annual Maintenance Costs
The Plant Wellness Way is business paradigm to create world-class performance and results in any operation by the correct selection and use of engineering, operating, maintenance, and reliability strategy and practices.
The six IONICS steps are used to develop lifecycle asset management, reliability improvement and maintenance management strategy and activities needed for endless operational excellence. Simply identify where you are in the above process map, come in at that point, and then continue on through the process to the point where your answers are available.
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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…]
The falling cost of sensors for Industrial Equipment & the popularity of AI-based solutions means that Organizational teams are defaulting to using this strategy on all their Equipment, regardless of its criticality or other effectiveness. This is a strategic error.
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You must select OHS risk management mitigations appropriate to a job safety hazard using a formal method that delivers safe work practices.
Each task safety control will need to be developed, assessed for suitability, and recorded so it’s clear what the plan is, and how it is to be done.
In the end, there is a practically designed, completely resourced, fully scheduled, and totally sure safe work practice procedure approved for use.
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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…]
In the previous article, P-F Curve was used to understand the Remaining useful life (RUL) of an asset. RUL can be estimated at any time during the asset’s life, but it’s opportune to calculate RUL at the time ‘t’ when the asset shows signs of an impending failure. In the P-F Curve terminology the point at which the asset shows signs of failure is called the Potential Failure Point (Pf), which can also be stated as the time of anomalous behavior. The exercise of detecting anomalous behavior is called “Anomaly Detection (AD)”.
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When equipment working parts are operated within their precision quality standard zones, world-class reliability is guaranteed.
The figures below demonstrates the importance of setting precision quality standards to achieve outstanding equipment reliability. It comes from a conference presentation on the production equipment reliability improvement in a steel mill in Australia.
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Humans are often so focused on the complex, that the simple gets overlooked. Even as technology and our equipment gets more complex, we need to be firmly rooted in the basics of maintenance and reliability.
That’s one reasons why the first step of RCM is so important.
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Recently, there has been an influx of Industry 4.0 companies promising their product/application would help predict the Remaining Useful Life (RUL) of a physical asset. Each uses a mix of machine learning algorithms to estimate the RUL based on the data available. This is their value proposition. But what is this ‘life’?
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