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Intelligent data-driven fault diagnosis methods for rolling element bearings have been widely developed in the recent years. In real industries, the collected machinery signals are usually exposed to environmental noises, and the bearing operating condition changes in different working scenarios. That leads to distribution discrepancy between the labeled training data and the unlabeled testing data, and consequently the diagnosis performance deteriorates. This paper proposes a novel deep distance metric learning method for rolling bearing fault diagnosis based on deep learning. A deep convolutional neural network is used as the main architecture. Based on the learned representations through multiple hidden layers, a representation clustering algorithm is proposed to minimize the distance of intra-class variations and maximize the distance of inter-class variations simultaneously. A domain adaptation method …