welcome to the community and thanks for your question!
There are several approaches, dependent on which kind of data you have.
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In reality often normal data is easy to get (but abnormalities are rare and therefore data with anomalies are expensive to acquire): in this case an unsupervised approach is powerful, training your model only on normal data to learn what “normal” looks like: e.g. you could start with a PCA or if you have big data also autoencoders represent a popular unsupervised solution, see also: Backpropagation algorithm - #4 by Christian_Simonis .
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However: If you have rich data for both, “normal and bad” cases, you could go for supervised learning and e.g. classification model like a logistic regression or also a Gaussian mixture model might be worth a look, see also: What will be good machine learning algrothim for this distribution - #9 by Christian_Simonis
If you are looking for an application from reality, this patent application for an early warning system for EV batteries, utilizing a variational autoencoder for predictive maintenance, where I also could contribute a bit, could be worth a look!
Please let me know if this helps!
Best regards
Christian