Detecting the Onset of System Failure using Anomaly Detection Techniques is one of the key demands in Industry 4.0. Nowadays implementation of this functionality is often related to two concepts: Digital Twins and Anomaly Detection with AI
Anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such as e.g. bank fraud, medical problems, structural defects, malfunctioning equipment etc.
Digital twins have been around for several decades, the rapid rise of the internet of things (IoT) is that they have become more widely considered as a tool of the future. Digital twins are getting attention because they also integrate things like artificial intelligence (AI) and machine learning (ML) to bring data, algorithms, and context together
In this presentation will be discussed various techniques that can be used to detect the onset of failure occurring in systems in the context of Microsoft Azure, using Azure Digital Twins Service and Azure Cognitive Service Anomaly Detector API.
Azure Digital Twins is a game-changer in the modern IoT and AI solutions. This a SaaS offering easy to build digital models of complex systems.
The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data with machine learning. This feature adapts by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume.
This talk is covering the whole content from Digital Twins and Anomaly Detection concepts via Azure based implementation demonstrating real use cases and demos.