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VIEWSIn industries where equipment failure is one of the largest causes of downtime, manufacturers must find solutions that prevent lengthy and unplanned downtimes. Why? An unplanned downtime can destroy their ability to be competitive in the market. Traditional solutions to handle these downtimes are either replace the parts before their end of life comes up by making calculated guesses, or predict downtimes based on past occurrences. Unfortunately, both solutions are not a perfect fit and can be cost intensive and very risky on the manufacturer’s end. The current trend of maintenance requires a tradeoff, whether it be reactive, planned, or proactive, whereas in predictive maintenance era, this will not be required.
Due to how limited traditional solutions are, they have quite a few tradeoffs when utilizing them. For instance, if a manufacturer wants to be reactive to downtimes, they must understand when their machine parts will hit maximum utilization. At max utilization, the whole machine can become damaged and result in the downtime.
On the other hand, if a manufacturer wants to plan for the downtime and not risk the breakdown of their machines, they must replace parts before their end of life, with the tradeoff being increased part replacement costs. In order to be proactive in protecting themselves from unplanned breakdowns, one must increase maintenance costs in order to extend machine part life. As we can see, these tradeoffs are not necessarily cost-effective or foolproof solutions. With predictive maintenance, these tradeoffs are eliminated as it minimizes downtime and backs up proactive maintenance with data so that costs can be maintained.
When you attach the Internet of Things to a physical asset like a manufacturing machine, you create a cycle between the IoT device and the physical machine. This cycle works on a physical to digital and back to physical cycle, meaning your physical asset provides digital data to the device, which creates a physical solution. Here is how it would work.
Essentially, this IoT enabled cycle allows organizations to take actions based on real-time data, making companies more agile in their response times and creating smoother operations with minimized disruptions.
While the major benefit of predictive maintenance is the ability to identify and manage risks that are associated with machine downtimes, it can be used across a wide variety of industries that require the assessment of operational data. Here is how predictive maintenance ensures that a smooth operation occurs.
In countries where infrastructure is in dire need of repair, predictive maintenance can aid in helping construction crews know what needs to be repaired when and why. For instance, things like bridges, sewers, and highways that go unrepaired for long periods of time can have dire consequences on the population, but if a predictive maintenance model is used, disasters can be avoided, and costs controlled.
One always hears about the vehicle recalls, which is not only annoying for customers who have these vehicles, but it poses quite a dangerous risk to those who have vehicles being recalled for serious issues. If automakers were to use data and machine learning from predictive maintenance, issues could be uncovered during the design and roll-out phases, preventing major recalls.
There are millions of pipelines that traverse countries with the sole purpose to transport oil and gas. Many of these pipelines were constructed decades ago, resulting in deterioration from overuse and aged materials. As these pipelines get older, there is a much higher likelihood that failures will increase. Being able to predict and prepare for these situations can avert major disasters.
When it comes to airline maintenance, it can get complicated very quickly due to how vast the aviation industry is. It may take several teams to understand why a specific part is in disrepair. Predictive maintenance offers a safer and simpler solution that is economical.
Yes, predictive maintenance is technically possible, and it is currently technically accessible as well. Companies do not need to make massive investments into acquiring the predictive maintenance model because the data that is needed to be proactive about maintenance is already available. It just needs to be captured, stored, and analyzed. The first step in making it happen is connecting machinery to IoT sensors so that real-time data can be tracked. This data should then be fed into a network where it can be stored and processed in large volumes. Once the data is gathered, anomalies and patterns should be tracked, and corrective action should be taken based on data insights.
Let’s Nurture, being the best IoT solutions and services provider in India, USA and Canada, provide expert consulting to help Companies not to make massive investments into acquiring the predictive maintenance. Our expertise in providing IoT based solutions has helped us in developing a Smart Automotive Solution, KarConnect, on Web and Android platforms.
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