Toward Unsupervised Domain Adaptation Fault Diagnosis: A Multisource Multitarget Method

Despite the remarkable results that can be achieved by data-driven intelligent methods developed for fault diagnosis, they typically presuppose the same distribution for the training and test data sets, as well as sufficient labeled data. Various operating states often exist in practical scenarios, leading to a domain shift that hinders fault diagnosis effectiveness. While recent unsupervised domain adaptation (UDA) methods have focused on …