Maintenance is a demanding and taxing process. Not knowing when to schedule a maintenance run or send the maintenance team is even more menacing. With the advent of the fourth Industrial revolution, connected devices makes information about machines more available than before.
Your machines have a lot to tell you about when they need to be maintained, if you’re listening that is. Thus, the best way to optimize your machine health is by monitoring it.
A huge advantage of monitoring your systems is knowing when to accurately plan a maintenance run for your machines.
The internet in the last decade has proved to be a pivotal enabler of functionalities which would have been deemed impossible by its previous generations.
Industry 4.0 partly deals with interpreting machine data and figuring out patterns from raw data to convert into an algorithm.
Once these machines become Artificially Intelligent, they can learn from these algorithms to find out when it is going to need maintenance or when the machine is going to have a breakdown and take appropriate actions of fixing its machine cycles on its own.
We have not reached that level of technology or trust, where humans program machines and allow them to take decisions on their own. But, we have certainly dawned upon the concept of predicting the period when maintenance could be needed for a particular machine.
By installing automation controllers on machines, we receive data on the functioning of that instrument. Using this data, we can create a pattern of when the appliance is working in pristine condition and when it is not. Using this pattern, an algorithm is created to accurately detect when the machine could need its next maintenance run. Thus we reduce the requirement of a preventive maintenance run by accurately predicting when the machine needs maintenance the most.
Predictive Maintenance will also deliver on reporting the condition of the machine at all times, thus keeping track of its health.
Why is Predictive Maintenance so crucial?
Well for humans, not knowing what the future lies for you is considered to be a good thing. Significantly, it is quite the opposite for machines. For instance:
- Early maintenance / Preventive Maintenance could risk loss of revenue for the machine needs to stop functioning for the maintenance run to be carried out, only to find out that the machine did not need maintenance in the first place.
- Late maintenance would cause greater loss of revenue as not only the appliance has to be stopped, but some machine parts could also need replacement due to excessive use, corrosion or wear and tear.
- In some industries & factories, condition reporting is a manual task. Staff members have a habit of submitting hand written reports of machine health after each shift. This irregular information format makes it difficult to assess issues and condition trends.
- Accurately planning a maintenance run will lower machine downtime.Thus, assets will be able to deliver with greater efficiency at a reduced maintenance cost.
IOT is nothing but transferring machine data from one platform to another(normally a cloud platform) using the internet. Predictive maintenance is a huge application of what the Internet of Things can do with machine data.
Embedos being an Industrial IOT company is a big believer in predictive maintenance. We design our devices generally based on customer requirements with a default option of reporting when machine health deteriorates.
Machine cycle time, duration, output, idle times etc is what is reported about to predict when maintenance is needed the most.
With IOT, condition reporting can be automated with access to loads of data, thus allowing system generated reports to help in more accurate decision making. By collecting and displaying data on a consolidated dashboard, maintenance runs can be evaluated and scheduled before the condition becomes severe.
Maintenance is a calculated and a tactical process for manufacturers and product developers alike. Almost 30 percent of planned maintenance schedules are ineffective with regard to its timing. Preventive maintenance is too costly and is deemed futile by consuming essential resources needlessly.
As Machine to Machine(M2M) technology progresses, human intervention for asset upkeep will not be required, thus defining the true power of predictive maintenance.
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