Anomalies Detection in records of operational failures using IoT devices and data mining
The industry underwent several transformations initiated by the first Industrial Revolution at the end of the 18th century. Today we are experiencing the Fourth Industrial Revolution, where equipment is capable of processing data and connect to communication networks. Maintenance planning can use large volume of data generated by IoT devices to act preventively . This work aims to propose an architecture that uses an outlier detection algorithm, Local Outlier Factor, to detect anomalies in machine failure records, producing information to support equipment maintenance decisions.
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