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NEM Solutions boots GAMESA’s digitalisation

NEM Soltuions- Gamesa

Approximately a year ago GAMESA made an important decision acquiring 50% of NEM Solutions: to speed up their big data operation and maintenance business. NEM’s entry in GAMESA with their product A.U.R.A was not decided overnight, it was a long process involving intensive technical diligences in which GAMESA also tested other companies and different technologies worldwide.

A year later, Pedro López, who was Director of Services in Gamesa for 16 years and is now a member of the board for NEM Solutions, explains that “NEM’s approach is different from that of all the other companies we have tried and assessed. NEM Solutions analyse normality and they do it well. Furthermore, not only they have excellent experience in the wind energy sector, but also in the railway sector, which we have found to be very similar to ours in terms of digitalization. The exchange of experiences between both sectors has been rewarding so far and has proven to be beneficial for both parties and for A.U.R.A’s evolution as a multitechnological product”.

Gamesa’s case is not different from any other big company in the sector. They have a lot of data, a lot of knowledge, and technology is giving them the chance to take a great evolutionary leap in the way they work. Alberto Conde Mellado, founder and CEO in NEM Solutions talks about the relationship with GAMESA in this way. “Gamesa deals with  vast amounts of data, turbine variables, productions, alarms, maintenance interventions work orders with the detail of material consumption, meteorological data, etc. It is very important that the data analytics we are doing, enriched with GAMESA’s technological know-how, is perfectly aligned with the business needs and the staff’s knowledge. It is not easy to find companies willing to listen to their teams and make their knowledge be part of decision-making processes. That is where NEM and the A.U.R.A technology help, we provide the interpretation of data depending on the business”

Alliance’s results and future expectations

The first acid test that NEM had to overcome was a blind test on the historical SCADA data of different types of turbines resulting in a relevant success in the prediction of masked failures in the turbines without any kind of help. It was the best result received by GAMESA in a similar test with commercial solutions. BY the end of 2016, with more than 1000 MWs already online in A.U.R.A and in the context of 14 GWs signed for the next years, GAMESA and NEM Solutions, will be able to predict with enough anticipation most of the corrective maintenance interventions.

“The truth is that the set up and commissioning of this technology and working together with NEM Solutions and their platform A.U.R.A is allowing us to move forward very quickly in the adjustment of our processes: the way in which the field information is collected and how the maintenance operations are displayed in GAMESA.

NEM Solutions is helping us with one of the critical backbones of our improvement plan, anticipating failure and prognosis”, comments Pedro López who states that “it is not that difficult to predict that a specific component will fail in a certain number of turbines next year as you can apply statistical methods associated to the operational experience. What is truly difficult to know is what the best moment to intervene is, anticipation in the short run and not in the long run. This dynamism is what really improves your maintenance operations and provides you with a differential feature”.

For this purpose, NEM relies on a thorough roadmap, based on the technology scalability and reliability”, according to Alberto Conde Mellado. We have been working with the same objective since 2007. Competitiveness in this sector does not allow us to have a tool or software platform on which to work every day. It requires solutions that eliminate work and provide valuable information for the kind of decision making demanded in such a dynamic business as the wind energy sector.

A.U.R.A is already automatic in the generation of normality models, the rule assignment and the boundary conditions according to the type of machine, and the set up validation. It is greatly automatic as well in the maintenance and constant adjustment of the intelligence used for diagnosis. Besides, our customers count on our team for permanent support.