What is Predictive Maintenance?
The internet of things has radically transformed the face of manufacturing in recent years. IoT technology has enabled huge savings both by predicting repair jobs before they are due and cutting down on time when the machine would have otherwise been in a state of disrepair. This is essentially predictive maintenance at work. In this article, we explain what IoT based predictive maintenance is and the advantages it brings to the table.
The internet of things has spawned an entirely new era in the 20th-21st century digital revolution. IoT technology allows us to perform a number of previously unimaginable feats of technology quite routinely, on an every basis - and to think that this is just the beginning! Industry experts widely believe that IoT technology has not reached anywhere near its potential. In the next few years, there will likely be no sector that will remain untouched by some form of disruption brought about by IoT technology.
One of the many exciting possibilities that IoT enables is predictive maintenance. IoT based predictive maintenance saves billions of dollars for thousands of companies and manufacturing operations around the world. Consulting firm Gartner has estimated that by the year 2022, global spending on IoT-based predictive maintenance will rise to around $13 billion. It was also estimated that monitoring assets using IoT-enabled predictive maintenance would lead to massive savings of around 40% for businesses using the technology.
Predictive maintenance is going to be arguably the most important IoT-based solution in the coming years. There is likely to be an explosion in the space when more businesses start to see the obvious potential for savings and efficiency. Moreover, the market also needs time to saturate with the right kind of solutions that offer both reliable performance and substantial savings. Moreover, with ever-tighter bureaucratic restrictions being placed on manufacturers, any technology that will help cut down on unnecessary costs can’t be ignored for long.
Predictive maintenance is going to feature prominently in the industrial landscape of the coming years. Some kind of an IoT-enabled prediction mechanism where precious manpower hours and other scarce resources will only be spent as and when required.
So, what is IoT-based predictive maintenance?
Imagine this - instead of servicing your car every 5,000 miles, you only take it to the service station when something is actually about to go wrong with it. The operative phrase here is about to - This, in essence is what predictive maintenance is all about.
These setups can be very basic or phenomenally complex and intricate but the key idea is this - connected machines will be able to interact with you and inform you about potential failures, before they happen. Avoiding unplanned downtime can potentially save operations upto millions of dollars. Often, these large-scale industrial operations are budgeted down to the last cent and can’t afford any unforeseen expenses or losses, due to malfunction.
This is where predictive maintenance comes in - IoT systems can predict which component is nearing the end of its life and alert the management before any actual failure occurs. This allows the management to intervene in a systematic manner and avoid losses due to unexpected offtime due to breakdowns or part malfunctions.
How does predictive maintenance work?
Predictive maintenance essentially refers to any technique or technology that enables the working condition of any equipment currently in use, to be assessed or estimated reliably, which makes it all the more important that there is no reason to believe that
There are certain industries where this is a more important issue than others - for instance, the automotive industry is particularly prone to this kind of loss. It is estimated that unplanned downtime can cost a factory anywhere from 5% to 20% of its production, if they are to face an unplanned stoppage in operations.
This is exactly the problem that IoT based predictive maintenance solves - It uses multiple data-rich streams and computes all the gathered variables to arrive at a prediction of what kind of repairs can occur and when. If there are any parts or components of a system that need to be replaced, it offers a time cushion to make adequate preparations - This translates to huge savings for the manufacturer over a period of time.
Benefits of IoT based predictive maintenance
Decreased cost of ownership
Each and every piece of equipment on the manufacturing floor is associated with a certain cost of ownership throughout its period of deployment - Predictive maintenance enables significant savings on this figure, over the lifetime of the setup. This can be invaluable in certain industries which are asset heavy and therefore, stand to gain the most from avoiding losses due to breakdown and repair - Using predictive maintenance can result in massive savings in these kinds of settings.
IoT based predictive maintenance uses historical information collected from sensors and edge devices to make precise estimations about the working condition of equipment and machinery - this allows companies to reliably predict failures and repairs before they actually happen.
Increased uptime, decreased downtime
While on the one hand IoT based predictive maintenance allows companies to cut down on losses due to unexpected failures and breakdowns, it also enables more revenue. Unplanned downtime not only costs companies capital but also eats into overall productivity.
The more a facility can keep downtime to a bare minimum, the more its productivity will be and in turn, its profits. As such, pre-scheduled regular maintenance is the norm in most manufacturing facilities - maintenance checks are performed periodically whether or not there is any real issue - However, this is incredibly wasteful and robs the unit of valuable time where the floor has to be taken down for a stipulated amount of time.
With predictive maintenance, there is simply no need for pre-emptive checks that end up being futile and costing the management dearly - maintenance and repair only need to be performed when it is necessary; i.e when there is an actual issue that needs to be fixed. By predicting machine failures ahead of time, the overall efficiency and utilisation of the resources at hand is improved exponentially.
Extended lifetime of assets
Predictive maintenance allows companies to effectively monitor the working of their prized assets over a long period of time - especially those that are already getting on in years. Predictive maintenance allows real-time monitoring of valuable company assets which gives a bird’s eye perspective on their inner workings.
This makes for a more efficient utilization of existing assets. Moreover, over a period of time, this makes for higher reliability and performance.
Let’s face it - nobody wants workplace mishaps - but sadly, they have always been viewed as an inevitable and unavoidable aspect of any manufacturing or on-field enterprise. This is another huge benefit that predictive maintenance brings to the table. With predictive maintenance, potential safety threats and hazards can be preemptively identified and dealt with, well before a worker comes to any harm.
In this model, vast swathes of data, collected from various internal and external sources by IoT sensors is put through the scanner to identify possible problem areas. When large amounts of data are analysed in this manner over long periods of time, any possible threat that could endanger a worker’s safety can potentially be identified and appropriately managed. Moreover, the richer the dataset, the more of these potentially life-saving predictions that can be made. A report by PwC reports that using predictive maintenance could reduce safety, environmental and health risks by upto 14%.
So, what is the future of IoT based predictive maintenance? Is it living up to all that it was made out to be a few years ago? This post is meant to explore what predictive maintenance means, how it works and why it’s important in the context of smart manufacturing and the internet of things, at large.
We’ll take a more comprehensive look at the ins and outs of predictive maintenance in a later post.