Big Arena breidt uit

Het samenwerkingsverband Big Arena wordt groter. Recent hebben twee nieuwe deelnemers zich aangesloten bij het Big Arena initiatief.

Big Arena is een platform voor een verzameling van innovatieve Nederlandse bedrijven op het gebied van big data. Op initiatief van Ordina is Big Arena in 2013 gestart met 8 deelnemers. Nu komen daar twee interessante bedrijven bij: Eligotech en Crowdca.lc.

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Gedistribueerde rekenkracht van Crowdca.lc

Gedistribueerde rekenkracht, dat is voor mij de kern van Cowdca.lc. Waarom dure hardware aanschaffen om big data applicaties op te draaien als het ook kan op bestaande infrastructuur? Crowdca.lc heeft een manier ontwikkeld om de rekenkracht die in elk bedrijf al verspreid aanwezig is te benutten op een makkelijke en veilige manier. Big data, maar dan uitermate kosteneffectief.

Big data applicatie van Eligotech

Eligotech heeft onder de naam Harpoon een totale big data applicatie ontwikkeld waarin alle onderdelen naadloos met elkaar samenwerken om het doorzoeken, ontdekken en analyseren van big data makkelijk te maken. In mijn ogen is de grote meerwaarde dat u zich geen zorgen meer hoeft te maken over alle losse componenten die nodig zijn voor grootschalige big data verwerking. In Harpoon werkt het allemaal al samen.

Meer waarde voor onze klanten

Met deze twee nieuwe toevoegingen aan het palet van Big Arena wordt het samenwerkingsverband nog krachtiger en hebben we als samenwerkingsverband onze klanten nog meer te bieden.

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How predictive analytics improves wind turbine maintenance

Wind turbines are big and expensive machines, so keeping them running smoothly helps keeping their operational cost down. The sensor data generated by the turbine can help achieving this – by analysing it, you can spot potential failures earlier. The longer the warning period before a part fails, the better you can prepare for it.

Main parts of a wind turbine

Main parts of a wind turbine

The business case

Preventive maintenance saves you money when you have:

  1. Shorter downtime and less lost production
  2. Better planning of people and materials
  3. Cheaper repairs

To do that, you need to be able to anticipate failures in heavy and expensive parts like the gearbox, generator and main shaft.

For Vattenfall we, Algoritmica, have implemented a solution based on this concept that is currently monitoring 78 turbines. It provides a daily overview of the turbine status, helping Operations staff to prioritize inspections and investigate potential problems.

Shortly after going live, this solution helped identify a bearing defect which could be claimed under the manufacturer’s warranty, thereby partially paying for itself.

How it works

Wind turbines have an array of sensors that measure temperatures, pressures, voltages, currents, and blade angles. This data is available for analysis, typically as 10-minute averages of the sensor values.

The computer that controls the turbine uses these measurements for its operations. This includes error thresholds like ‘the gearbox oil temperature should be below 120 degrees Celsius’. However, by the time the threshold is exceeded it is usually too late: the damage has already been done. To catch failures earlier we should look for anomalies, e.g. measurements that are unexpected and therefore might indicate a problem – but are not yet so severe that they exceed a threshold.

A typical wind farm has about 30 turbines, with around 50 sensors each. That means about 200 thousand new measurements every day. Clearly, looking for anomalies is not something a human analyst can do manually; fortunately we can automate this.

Anomaly detection

Anomaly detection begins by defining what measurement values are expected and then calculating the difference with the actual situation. Since sensor data is delivered as a time series, we create a model that predicts the next value of a specific sensor given its previous values as well as the previous values of any other sensors that may be relevant. Based on these multiple inputs, the model then calculates its predicted value and compares it with the actual sensor reading. The difference (or residual) is now a measure of how much the turbine is deviating from its expected performance. If it is persistent or grows too large (i.e. becomes an anomaly), an analyst can investigate the cause and decide on a course of action together with the Operations staff at the wind farm.

The anomaly detection process

The anomaly detection process

Predictive Model

To create such a sensor model we apply machine learning, i.e. one or more algorithms that use a set of examples (the ‘training set’) to learn a predictive model. For a wind turbine, it is a natural fit to use a year of sensor data as the training set so that all seasonal variation is included. Since each turbine can have its own training set we can train a model for each individual turbine. This has the advantage that we automatically take into account the influence of the specific location and turbine-specific parts. Note that we have to be careful in selecting the training period: if the turbine had a defect during that time, the model will learn from bad examples and think the defective situation is normal.

Driven by data

This is a data-driven approach: the model learns the relationship between the various sensor readings purely based on the training data. This is in contrast to a so-called physical model that explicitly describes the turbine design using detailed knowledge of its physical characteristics. The main advantage of a data-driven approach is that the model can be trained by a non-turbine expert and matches the actual situation by definition, whereas a physical model has to be carefully calibrated by an expert.

As shown in the illustration, the data-driven approach is generic. This means you can apply it to other machinery like trains, cars or printing presses – anywhere it makes sense from an economic or safety perspective to know the condition of your assets.

Please contact us if you want more information.

Personal data, the greatest cash crop ever?

Should we see personal (social) data as the greatest cash crop ever?

  • you don’t have to plant it
  • it’s free to harvest (at least for the time being)
  • it generates profits for a select few, virtually out of thin air.

One important limitation might be privacy. However, what is privay worth in today’s world? Not much, is it? But did the Web kill it or did the NSA? What is privacy, anyway? And if we decide it’s worth preserving, how can we do that?

According to data visionair Jaron Lanier the solution to overcome the privacy issue is that consumers should take back ownership of their data. Basically to compensate them for the data they now give away. But why would those who are making fortunes with their data suddenly decide to pay them for it?

This eye-opening documentary tells you more..

Watch the documentary here

Social data: bagger of goud?

Social media content met name nutteloze prietpraat? Vast staat dat het reuze interessant voor velen is, blijkens de populariteit hiervan. Maar, hoe waardevol is deze informatie voor bedrijven?

Dit ondervinden is het doel van de Customer Intelligence case die Xomnia in samenwerking met Active Professionals uitgevoerd heeft bij Travix, het moederbedrijf van o.a. Cheaptickets.nl. Welke waarde heeft social data in potentie voor deze en andere organisaties? Lees verder

Social media als voorspellers: 6 inzichten na Project X Haren

Snel mobiliserende groepen, zoals bij het sneeuwballengevecht in Breda en de Harlem Shake zijn een punt van zorg voor de openbare orde. Zit er voorspellende waarde in de social media die helpen om zo’n mobilisatie of het verloop van zo’n evenement te duiden?  Drie analyses helpen om deze vragen scherper te formuleren. En na ‘Cohen’ en met de aankomende troonwisseling is daar veel behoefte aan! Lees verder

Is social CRM voor B2B eigenlijk wel interessant?

Is social CRM (een customer relations managementsysteem, verrijkt met sociale gegevens van klanten) alleen interessant voor B2C-bedrijven? Welnee! Omdat consumenten zich steeds vaker via social media uitlaten over bedrijven, merken en producten is het voor B2C-bedrijven noodzakelijk dit te monitoren en erop in te spelen. Hoewel het volume aan sociale interacties voor B2B-bedrijven vaak lager ligt, lijkt social CRM minder interessant. Maar niets is minder waar! Lees verder

10 Tips for a Successful Predictive Analytics Project

In his book “Predictive Analytics” Eric Siegel calls predictive analytics “the power to predict who will click, buy, lie, or die”. You can apply this to both people and machines.

With the increase of data-generating devices, sensors, and software, the amount of data in organizations is growing exponentially. But more data doesn’t automatically translate into information for man and machine until you can extract actionable information. Unfortunately, the capacity of most organizations to analyze this data has not increased at the same pace as the available data. In order to replace gut feeling based on experience with a data-driven approach we need to enhance this capacity by introducing predictive analytics. Lees verder