DeepSense raised $2 million

19 June, 2016

The Haifa based company developed a neural-network based data processing system that “learns” how machines behave, to predict future malfunctions

The Haifa based company developed a neural-network based data processing system that “learns” how machines behave, to predict future malfunctions

DEEPSENSE-CLOUD
The system learns how machines behave

Israeli startup DeepSense, specializing in the development of long term industrial machine failure, has completed a $2 million financing round. The round was led by the AfterDox fund, a growing angles fund in Israel. It was joined bt JANVEST, SeedIL as well as the chief scientist’s office in the Israeli ministry of industry.

DeepSense’s system collects massive amounts of data from production lines. The data is streamed to the cloud and processed by DeepSense software. The specialized software is able to process various data formats collected from production line machines as well as from thousands of sensors. The system learns how groups of machines behave and is thus able to detect abnormal events, recognizing corralations between such events and consequently – predict evolving failures. The system does not only predict a failure – it also outlines the best solution to the problem based on the machines history, or harnessing big data. According to DeepSense’s estimates, most production lines suffer from prolonged downtime (17 days a year in average) due to failures which could be predicted.

DEEPSENSE
CTO David Lavid, CEO Eitan Vesely and Chairman Dr. David Almagor

DeepSense currently employs 8 workers. It was founded in 2015 by CTO David Lavid, CEO Eitan Vesely and Chairman Dr. David Almagor. The company strives to become a major player in the fast growing field of “Industrial IoT” and a leading supplier of deep machine intelligence based maintenance and prediction services. The company is a “graduate” of Microsoft’s 7th accelerator batch, as well prestigious Technion University’s accelerator.

“The company was founded in order to provide a solution to manufacturers and machine operators which incur great losses from machine failures and unplanned shutdowns,” said CEO Vesely, “At this point our main focus is on the energy market – power plants, turbine manufacturers and operation & maintenance companies. Our system is already installed in a power plant in the US, and we are currently in discussions with additional energy providers.”

The funds raised in the current funding round will be dedicated to the expansion of DeepSense’s R&D team and the recruitment of machine learning developers, deep learning researchers and big data experts, In addition, the funds will be used to establish a presence in Europe. Chairman Dr. Almagor sai that DeepSense “brings advanced technology into traditional industry”.

While similar systems in use today are based on machine simulation based physical modules, DeepSense’s engine is completely agnostic to the data’s content. The engines learns continuously, building neuron-network based prediction modules for components, machines, sites and more). These properties ensure that the system requires minimal integration, it is genertic and can be used in different verticals.

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