Siemens > Case Studies > Siemens | Using Machine Learning to Get Machines to Mimic Intuition

Siemens | Using Machine Learning to Get Machines to Mimic Intuition

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 Siemens | Using Machine Learning to Get Machines to Mimic Intuition - IoT ONE Case Study
The Challenge

The ability to learn is a precondition for autonomy. With this in mind, Siemens researchers are developing knowledge networks based on deep learning-related simulated neurons and connections. Such networks can be used to generalize information by identifying associations between extraordinarily complex realms, such as the publicly accessible Internet and a company’s internal information systems. Far-reaching and generic, this technology appears to hold the potential of mimicking what humans call intuition.

About The Customer
This specific case study analyzes the ramifications of neural networks on the renewable energy industry, specifically companies involved with wind turbines.  
The Solution

Biological systems that learn include everything from roundworms with approximately 300 nerve cells to adult elephants, whose brains contain 200 billion neurons. But regardless of whether you’re dealing with a fruit fly, a cockroach, a chimpanzee or a dolphin, the neurons of all of these creatures process and transmit information. Moreover, they do so for the same reasons: All organisms need to be able to discern and interpret their surroundings and then react appropriately in order to avoid danger and ensure their survival, as well as their ability to reproduce. They also need to be able to recall stimuli that signal risk or reward. In other words, learning is the key to survival in the natural environment.  Through creating a computational neural network that mimics the neurons in a human brain, Siemens' engineers are getting closer to creating virtual intuition.

Operational Impact
  • [Efficiency Improvement - Production]
    Researchers at Corporate Technology (CT) are studying how machine learning techniques could be used to enable wind turbines to automatically adjust to changing wind and weather conditions, thus boosting their electricity output. The basis for self-optimizing wind turbines is the ability to derive wind characteristics from the turbines’ own operating data.  Up until now, this type of data has been used exclusively for remote monitoring and diagnosis; however, this same data can also be used to help improve the electricity output of wind turbines.”

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