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How Industrial IoT is Influenced by Cognitive Anomaly Detection

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There are about 6,000 sensors on an A350 aeroplane. The average Airbus flight generates 2.5 petabytes per flight with over 100,000 flights per day! 

Industrial Internet of Things, or IIoT, is a massive market.

It includes aeroplane and car manufacturers, power plants, oil rigs, and assembly lines, all of which contain sensors measuring thousands of different attributes. But most IIoT companies let 80% of their data go unused. And this is a big challenge for businesses. 

But there are other challenges too, like latency issues that affect the results from real-time data, the failure to predict when parts will breakdown, and the expense of hiring data scientists.

A Cognitive approach to Anomaly Detection, powered by Machine Learning and excellent data and analytics, is providing IIoT businesses with solutions and helping them to overcome the limitations of traditional statistical approaches. Machine Learning is becoming a commonplace tool for businesses, accelerating root cause analysis. Anomaly detection refers to the problem of finding patterns in data that don’t conform to expected behaviour. There are many different types of anomalies, and determining which is a good and bad anomaly is challenging. 

In Industrial IoT, one main objective is the automatic monitoring and detection of these abnormal events, or changes and shifts in the collected …

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