Waste and inefficiency have been sadly inevitable elements of every business, especially in the heavy sector where in some industries the estimated loss caused by production system unplanned downtime can be up to 5% of the annual production volume. However, development of intelligent technologies let companies leverage data for minimizing waste, which saves precious time and money.
PM – preventive predictive, or prescriptive maintenance? Those terms are important to differentiate!
Preventive maintenance is a regularly performed method that aims at discovering early system downtime which as a result can be scheduled and planned much earlier, with sufficient time to prepare all needed resources for the maintenance work. Many of the working frameworks are defined by people operating the equipment, basing on e.g. projection of the machine lifespan. It’s designed to keep machines, infrastructures and systems work longer.
Predictive maintenance is a next step and its advancement relies on analyzing historical and real-time data in order to schedule maintenance when it is required. Usually it means embedding a system of IoT (Internet of Things) and cloud data storage solutions that enable continuous data collection which can be analyzed and once the relevant parameters indicate a maintenance work should be performed, it communicate it to the system.
Prescriptive maintenance is a more complex solution, because there is an element of automation incorporated in it. Not only does this method monitor real-time data, just like predictive maintenance, but also includes machine learning algorithms that are able to suggest the most optimal course of action. The result is, that the algorithm will try to define the cause of predicted downtime and will provide different scenarios of the possible outcomes which will help the team operating on the system make decision knowing all the included risks.
However this is not the only stage in the process, where machine learning can be leveraged. There are much more opportunities to use advanced technologies, especially to analyze different types data and look for signals indicating the need for maintenance work.
We have a very interesting case of applying machine learning in preventive maintenance in the energy sector. The solution that K1 Digital has developed is able to detect dangerous changes in an infrastructure’s environment based on image processing with the use of machine learning. The energy sector is mainly all about providing continuously utilities to massive number of recipients and any breakdowns in those systems result in serious consequences for both, energy plants and utilities users. In order to provide more security to the energy infrastructure through analyzing potential threats in the background, we use drones to collect images which later were processed in a machine learning system. Our solution with accuracy of 92% can alert about potential disruptions in the work of the system. The future of this cooperation in promising, the solution has already reduced costs of maintenance and reduced risk of system damage.
The use artificial intelligence in maintenance has an enormous potential and coming years will result in wider variety of available technologies that can help companies reduce inefficiencies, risks, and costs related to production and operating on infrastructure that, by nature, is not meant to last forever.
If you wish to know more about the use case or you are wondering how you can benefit from AI solution for your company, contact us though contact form or write us an email on: email@example.com
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