This example shows how to use the Industrial Edge app "Anomaly Detection" to analyze your automation process. During this tutorial you will go through every single setup step to train a machine learning model on time series input data.
This document describes how to create an Anomaly Detection model. This model is used to detect abnormal behavior in time series data. If an unusual behavior is detected, the app can be used to identify such divergence and in some cases you’ll get a first impression what caused the problem and where to start the further investigation, e.g. to make a deep dive analysis with the Anomaly Detection.
- You will learn how to select the incoming data and how to potentially transform this data in order to come up with a machine learning model
- After that you will see how to define the model parameters and start the training.
- In the last step you will use this model for inference and start the Live Anomaly Detection
- Access to Industrial Edge Management System (IEM)
- Onboarded Industrial Edge Device (IED) on IEM
- Industrial Edge Device Version simatic-ipc-ied-os-2.0.0-19-x86-64
- Databus V 2.3.1-2
- IIH Essentials V 1.9.0
- Flow Creator V 1.16.0-2
- Anomaly Detection V 1.1.0
To successfully run the application, you need to follow these steps:
You can find further documentation and help in the following links
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