Anomaly Detection in Process Control Data with Machine Learning

Anomaly Detection in Process Control Data with Machine Learning

Introducing anomaly detection with data generated from your own desktop on the Temperature Control Lab device. We’ll be able to generate some real data with the Temperate Control Lab device and train a supervised classifier to detect anomalies.

Anomaly detection is a powerful application of machine learning in a real-world situation. From detecting fraudulent transactions to forecasting component failure, we can train a machine learning model to determine when something out of the ordinary is occurring.

When it comes to machine learning, I’m a huge advocate for learning by experiment. The actual math behind machine learning models can be a bit of a black box, but that doesn’t keep it from being useful; in fact, I feel like that’s one of the advantages of machine learning. You can apply the same algorithms to solving a whole gamut of problems. Sometimes, the best way to learn is to handle some real data and see what happens with it.

In this example, we’ll be able to generate some real data with the Temperate Control Lab device and train a supervised classifier to detect anomalies. The TCLab is a great little device for generating real data with a simple plug-and-play Arduino device. If you want to create your own data for this, there are great introductory resources found here; otherwise, I included data I generated on my own TCLab device in the Github repository. If you want to run these examples on your own, you can download and follow the code here.

Problem Framework

The TCLab is a simple Arduino device with two heaters and two temperature sensors. It’s a simple plug and play device, with a plug to power the heaters and a USB port to communicate with the computer. The heater level can be adjusted in a Python script (be sure to pip install tclab), and the temperature sensor is used to read the temperature surrounding each heater. For this example, we’ll keep it basic and just use one heater and sensor, but the same principles could also be applied to the 2-heater system, or even more complex systems such as what you might find in a chemical refinery.

machine-learning engineering data-generation anomaly-detection

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