HIGHLIGHTS
SUMMARY
Traditional methods work under the following assumption: the distribution of the training data is similar to that of the test data (e_g, the fault samples), implying that the training data should contain a good number of fault samples so that the models used for fault detection are well trained. Such an algorithm first augments the data-by-data generation or data resampling, then uses the augmented data to extract features with machine_learning models, e_g, neural_networks, together with a feature adaptation process (where necessary), and finally builds a suitable fault classifier to identify the types . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.