How to Use Machine Learning to Identify Faulty Weather Station Sensor Data

I'm new to the machine learning.

I've got a huge database of sensor data from weather stations. Those sensors can be broken or have odd values. Broken sensors influence the calculations that are being done with that data.

The goal is to use machine learning to detect if new sensor values are odd and mark them as broken if so. As said, I'm new to ML. Can somebody push me in the right direction or give feedback to my approach.

The data has a DateTime and a value. The sensor values are being pushed every hour.

I appreciate any kind of help!

Hi Tobi,

Not really a electronic engineering question. If you want to implement it on a small micro-controller it is best to read the book TinyML (https://tinymlbook.com/) It also covers detection of faulty devices through sensor data (provided it is tagged). Otherwise you can better post your question at some Machine Learning Forum.

Cheers,
Vincent

Since the question is pretty general in nature, I will provide some basic thoughts. Maybe you are already slightly familiar with them.

Set up a dataset that contains both broken sensors, as well as good sensors. That is the dependent variable. With that set you also have some variables that might predict the Y variable. Let's call them X.

You train a model to learn te relationship between X and Y.

You predict, based on X values where you do not know the outcome, what Y will be.