In industries where equipment failure is one of the largest causes of downtime, manufacturers must find solutions that prevent lengthy and unplanned downtimes. Why? 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 the predictive maintenance era, this will not be required.
Why Do traditional solutions have tradeoff?
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. To be proactive in protecting themselves from unplanned breakdowns, one must increase maintenance costs 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.
How does the Internet of Things work with predictive maintenance?
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.
- An IoT device with sensors is placed onto the manufacturing machine. The sensors generate data that pertains to how the machine is functioning.
- The generated data is digitized and analyzed for insights. This could be analyzing information about part breakdown percentages, parts that are not working correctly, or even data on the end of a specific part of the life cycle.
- These insights are relayed back to the machine and physical actions, like replacing parts can be taken.
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.
How does predictive maintenance ensure smooth operations?
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.
- By using a combination of science, math, and engineering principles, predictive maintenance brings dependability and efficiency to the productive process and all products within that process.
- Predictive maintenance can analyze multiple operational data sources and types. It can generate and analyze real-time data, batch-data, structured or unstructured data, and even at rest or streaming data components.
- By using data from IoT enabled devices, the predictive maintenance model can be continuously improved upon, factoring in other types of data generation points like motor circuits, vibrations, and thermography.
- Predictive maintenance can create a data-based multivariate metric that provides manufacturers with information on end of life or remaining life for parts. This is statistically significant because it removes the guesswork that is used with traditional methods.
Which industries can be impacted the most with Predictive maintenance?
1) Structural Infrastructure:
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 can have dire consequences on the population, but if a predictive maintenance model is used, disasters can be avoided, and costs controlled.
2) Automakers:
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.
3) Oil & Gas Production:
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 the 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.
4) Airline Maintenance:
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.
Is Predictive Maintenance Possible?
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.
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