Whether you’ve been working in maintenance for a long time or not, you have surely heard a lot about machinery reliability. But does your staff really know what this means, what it’s used for, and how to calculate it?
The following definitive guide brings together everything you need to know about reliability. It also provides tips on how to focus your maintenance on this parameter and use smart tools to keep your assets reliable all the time. Enjoy your reading!
Reliability can be understood as the probability of an asset performing its specified function according to the operating conditions for a given period of time. This is the definition established by the Brazilian Technical Standards Association’s NBR-5462.
The calculation of this probability is important because it tells the maintenance manager how much he/she can trust that equipment, assuring (or not) that the production will follow its intended flow. In order to get to this result, there are some formulas that, although different, share a common point: when calculating reliability, they take into account the failures that have already occurred in that equipment. Hence the importance of constantly monitoring the machinery, a strategy that will ensure accurate and up-to-date data.
Before moving on to the next topic, another important thing to know is how to differentiate reliability from availability and maintainability. The three concepts always go together and make up the acronym of RAM Analysis, but should never be understood as synonyms.
Essentially, while reliability indicates how much we can trust the asset to work properly, availability shows whether it can be used at that particular moment, and maintainability represents how easy it is to repair the equipment and return it to its function after a failure.
As mentioned above, some tools help us figure out the reliability rate of an asset. Some of these include the FMEA (Failure Modes and Effects Analysis), the FT (Fault Tree), and the RBD (Reliability Block Diagram).
But although the listed analyses are very efficient, the manager’s greatest ally when it comes to measuring equipment reliability is, without a doubt, the MTBF (Mean Time Between Failures).
This is one of the most important metrics for maintenance since it allows us to calculate the average number of hours, days, or weeks of good performance of an asset between failures, based on the time of operation. The higher the MTBF value, the greater the reliability of the machine, since it is taking longer to fail or breakdown.
But if the manager wants to know precisely how likely a piece of equipment will work perfectly next week or next month, it is not enough just to consider the MTBF score. Besides this, it is necessary to define exactly the time range in which you want the machine to perform properly and to calculate the failure rate of that same asset. When you put these three pieces of information together, you have the reliability formula:
Through this calculation, it is possible to know the probability of an electric motor that is critical to production running as expected for the next 30 days, for example. This way, the manager ensures that the operation will follow the flow as planned and deliveries will be made within the agreed deadlines.
However, for this to be possible, the team needs to have the exact and updated machinery data at their reach — which can be collected and analyzed in real-time by an online monitoring software.
TRACTIAN, for instance, combines hardware and software to bring companies the best online monitoring system on the market. With IoT (Internet of Things) technology, the Smart Trac sensor collects vibration, hour meter and temperature data from industrial assets in real time, sending them to the platform, which will then learn the machines’ behavior and analyze the collected data to transform it into insights, diagnostics, and prescriptions.
Alternatives like TRACTIAN not only listen and record the key information emitted by your assets but also use it to automatically calculate the reliability and MTBF of each machine, giving the manager accurate and reliable results about the performance of the operation. This frees up maintenance personnel to concentrate on more important tasks than manually filling out spreadsheets — and increases machine reliability.
You may have heard of Reliability Centered Maintenance (RCM), right? Developed by the US Army to improve the uptime of military equipment affordably and efficiently, this methodology uses data collection and analysis to select the appropriate maintenance strategy for each asset.
What makes this maintenance model one of the most cost-effective available is its careful and analytical approach, which recognizes the importance of anticipating failures and avoiding unexpected downtime and emergency corrections. The manager who centers the maintenance plan on reliability can reduce costs, extend the useful life of the machines, and optimize the team’s work – advantages that can be intensified by adopting predictive maintenance tools.
We have already said that online monitoring software can automatically and accurately calculate assets’ MTBF and reliability, key indicators for good maintenance management. But there is more: by monitoring the equipment 24/7, listening to everything the machines have to “complain” about (symptoms that would not be captured in time by human perception), these technologies gather all the information emitted by the machinery, as well as their analysis, in intuitive platforms. Thus, they allow the manager and the maintenance team to visualize the real conditions of their assets and draw their own conclusions based on the diagnoses and precise insights sent by the software.
By getting ahead of the failure and monitoring the machines with the help of artificial intelligence, you enhance not only the reliability but also the availability and maintainability of the items in your operation. As a consequence, this strategy reduces maintenance costs, eliminates breakdowns and unexpected stops in production, and increases the company’s profitability and competitiveness. All this with a single tool. Can you imagine that?
We have talked about how online asset monitoring greatly improves machinery reliability, but you may be wondering how this works out into practice. Rather than simply explaining the process, here is a real example of a TRACTIAN customer that achieved excellent results after adopting our predictive maintenance software.
Before installing the TRACTIAN sensors on assets that are critical to production, the maintenance team of Yara Fertilizers in Paulínia, in the state of São Paulo (Brazil) was already monitoring the vibration of these machines through other systems. The difference is that the process was based on intuition or conventional spreadsheets.
By understanding the specific behavior of the company’s equipment, the online monitoring software helped in the reduction of the number of cooling tower fan failures, which were giving the maintenance staff a hard time. The constant monitoring of vibration rates in this equipment and the sending of alerts and guidance to the team ensured that the professionals always took early action, keeping them ahead of breakdowns at all times.
With the aid of notifications, the operators at Yara in Paulínia got to know their machines better and, therefore, trust them more. This is because, in addition to the constant data analysis, they were able to understand through the platform exactly what should be the intervals between cleaning and inspection cycles of the machines. In other words, the predictive maintenance technology not only optimized the crew’s work but also improved their preventive activities, making them less random and more assertive.
If your assets are not performing at their optimum reliability rate, the problem is not with them, but with the management. The reliability level is always related to the efficiency and intelligence of the maintenance plan: the more the manager relies on technology and strategically defined prevention activities, the more confidence he/she can place in the equipment.
And the first step to increasing machinery reliability and positively impacting production and company profitability is, as you already know, making predictive maintenance a priority in the plan and using an online monitoring software.
By replacing spreadsheets and manual records with tools based on artificial intelligence and data science, you guarantee excellent results as you anticipate failures and improve the uptime of your assets. Join the Yara team and turn your traditional maintenance management into a 4.0 , more profitable and efficient. Of course, to make this change happen, you can count on TRACTIAN. Schedule a demonstration and talk to one of our experts.
About the author:
Journalist, post graduate in Marketing and Digital Media with focus on Business, Law and Administration. She is a specialist in content production for industry and maintenance areas at TRACTIAN, responsible for helping teams in innovative processes.
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