It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the particular pool of collected data, has become one of the main objectives of the Industrial IoT.
Abnormality detection refers in order to the identification associated with items or occasions that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by the human expert. Such anomalies can generally be translated into problems such as structural defects, errors or frauds.
Examples of potential anomalies
A leaking connection pipe that leads to the shutting down of the entire production line;
Multiple failed login attempts indicating the possibility of fishy cyber activity;
Fraud detection in financial transactions.
Why is it important?
Modern businesses are beginning to understand the particular importance of interconnected operations to get the full picture of their…
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