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Abstract: Anomalies could be the threats to the network that have ever/never happened. To protect networks against malicious access is always challenging even though it has been studied for a long ...

Abstract: The traditional deep learning-based bearing fault diagnosis approaches assume that the training and test data follow the same distribution. This assumption, however, is not always true for ...