Production Data
Hello, I have a question and i didn't find any related course in the platform. I hope you can guide me. What is the best method for classification the production data? The data that is acquired by sensors. Consider a machine that is working with some sensors. A component enters the machine. some process happens and then the component leaves. The component is either Ok or NOk at the end. And now we have 10 seconds of data from these sensors for each component. (columns -> different sensors and rows -> time series) What is the best method or library that i can use for Supervised classification this kind of data? Thanks in advance
Hi Alireza,
For classifying production data from sensors, consider using a 1D Convolutional Neural Network (CNN) if you're working with raw time series data directly, or a Random Forest if you prefer to work with extracted features (like statistical summaries). Use libraries like TensorFlow/Keras for CNNs and Scikit-learn for Random Forests. These approaches are robust and effective for handling time series sensor data for classification tasks.
Best,
Ned
Submit an answer
