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Inrobin, the startup that applies the Industrial Internet of Things to predict faults and calculate risk premiums

We are already accustomed to hearing talk of the Internet of Things (IoT), but there is another expression that is also going to begin to sound familiar to us. It is the IIOT (Industrial Internet of Things). This technological category encompasses those IoT devices that are specially designed for what is known as industry 4.0.

The aim? To optimise industrial production thanks to data collected by smart sensors, and to carry out maintenance on machinery to help prevent faults. Dedicated to precisely that is Inrobin, a startup that was set up in 2017 in Edinburgh (Scotland), formed by a multidisciplinary group that is passionate about industrial management.

“Industries have a lot of data, but they don’t always know how to use them. We help industries to give them value,” explains Inrobin’s electromechanical engineer André Faroni. Its predictive maintenance platform uses artificial intelligence techniques and automatic learning to anticipate, in real time, what a piece of machinery’s faults are going to be. This is very important for companies, because “when machines are not working, businesses lose productivity and their costs increase,” adds Lara Neira, data scientist and CTO of Inrobin. They provide the software that will analyse the data collected, but will also advise industries on the installation of IoT sensors.



At the moment, Neira calculates that they can predict breakdowns “with more than 80% success”. But they are continuing to work to improve their platform, as Faroni outlines: “We want to reach a point where we can not only detect faults, but also identify what type they are to be able to provide solutions with the existing data. That would be ideal for companies.” To achieve this, they need to continue training the platform with more observations from industry.

In addition, “every customer has a different prediction goal, that is why we need a lot of data,” adds the CTO. They are already working in the automotive industry, textiles, sugar, the cement and steel industry, and oil and gas, among others.

Since the startup was conceived, they had a clear idea of the interesting role that these predictions could play in the insurance sector. “Predicting a machine’s faults is closely related to predicting the risk premium associated with the likelihood of that failure, so we can calculate it as optimally as possible with IIoT”, explains Neira.

The intelligence that their platform provides is useful for insurance companies, and that is what they are working on in MAPFRE’s insur_space. “The learning that we have here is an incredible gift that is not found just anywhere. We are learning to better manage the business and sales channels, and to have a better perspective on the world of startups”, acknowledges Faroni.

Their mentor in the programme, managing director of Globalborn, Tanguy Jacopin, praises their project: “The IIoT sector is moving more than 24 billion dollars worldwide, and no entity has yet found the optimum formula to manage to safely predict machinery faults. Inrobin’s proposition is very interesting because it aims to tackle a problem that affects all industrial entities”.