How Industrial IoT is Influenced by Cognitive Anomaly Detection

There are about 6,000 sensors on an A350 airplane. 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 airplane 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 behavior. There are many different types of anomalies, and determining which is a good and bad anomaly is challenging. In Industrial IoT, one main objectives is the automatic monitoring and detection of these abnormal events, or changes and shifts in the collected data, including all the techniques aimed at identifying data patterns that deviate from the expected behavior.

With the help of Data Scientist Taj Darra from DataRPM, we can understand the importance of a bottom up approach to anomaly detection, which you can see here: When Machine Learning is enhanced with a cognitive IoT framework, it enables IIoT businesses to detect anomalies from the initial ingestion of sensor data to outputting predictions and determining whether or not something is an anomaly in just 2 days. With cognitive predictive maintenance powered by Machine Learning, all of the sensors can be measured in parallel.

Let’s break down the phases of anomaly detection: Cognition is giving businesses the means to gain control over enormous quantities of sensor data generating from every machine. This means augmented asset failure management, reduction of unplanned downtime, improved failure prediction, and enhanced asset life. As the IIoT industry moves into the future, there is an urgency for change because of the limitations of traditional machine learning approaches.

There are opportunities for businesses to take advantage of Cognitive Anomaly Detection now. I would like to thank DataRPM and Taj Darra for their insights.