As we know, the IoT is being leveraged across an enormous range of industries and contexts. These applications are hugely diverse – and yet some strands of consistency do appear across them. One such strand is the significance of predictive maintenance.
What is predictive maintenance?
Predictive maintenance essentially means building an analytical model to predict when a piece of equipment is likely to fail, so that remedial action can be taken beforehand.
First, then, it involves collecting applicable data. This might be anything from the number of revolutions taken by a particular machine, to the amount of oil, water or other consumables left within it, to air temperature, to the speed of a particular action – any information which helps to collectively build up a picture of how a piece of equipment is performing and what demands have been placed on it.
Second, that data needs to be consolidated and analysed, so as to create that picture and compare it to past performance records, current performance levels, pre-determined algorithms and any other relevant information. From there, a prediction can be made as to when the equipment is likely to start degrading or fail altogether.
Finally, that prediction needs to be turned into an alert and an appropriate action – generally, a maintenance and engineering activity which should prevent the failure from occurring.
Why does it matter?
The importance of predictive maintenance always comes back to the business bottom line. No matter what business context we are considering, equipment slowdown or failure is usually extremely costly. It causes process bottlenecks, reducing or even eliminating productivity altogether. This is expensive in itself; it also frequently has a knock-on effect on reputation and market positioning. Then there’s the expense of actually repairing or replacing the failed equipment. It is always more efficient and more cost-effective to repair equipment before it fails altogether – and this is precisely what predictive maintenance should engender.
Predictive maintenance can fuel other benefits too. The insights it generates on equipment performance can be used to drive process enhancements and greater productivity. The efficiency it generates can improve customer satisfaction. And by cutting down on emergency hardawre failures, it can have a positive impact on staff morale and engagement too.
Where does the IoT come in?
What is the role of the Internet of Things in all this? As we have shown, predictive maintenance depends on data. It requires an existing bank of data on equipment performance, and continuous data collection from the hardware in question. It then requires an analytics engine to consolidate all of that data, and turn it into actionable insight.
The IoT provides the architecture for all this data collection, analysis and application. From embedded sensors that measure those initial factors such as temperature and water levels, to the networks that transmit that data to a centralised point, to the analytics engines that make sense of it, the infrastructure we are describing here is an IoT platform.
Little surprise, then, that many industries which are harnessing the IoT are also making innovative use of predictive maintenance. From utilities firms using it to predict transformer failure, and therefore replacing transformers at the most cost-effective point, just before they break, to logistics firms predicting when RFID chips will fail and immediately cause management nightmares, plenty of organisations are using predictive maintenance to ward of costly and complex incidents. Could your business be next?