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This paper presents a semi-supervised fault detection method based on deep learning framework, which utilizes the stack of Restricted Boltzmann Machines in Deep Belief Network to abstract high-level features from original signal data automatically, and apply Support Vector Data Description model to implement fault data detection. This method only needs normal status data as training samples and no any labeled fault data is required. Meanwhile, real-time detection and auto-recognition of fault data can be carried out without expert intervention. As result, the fault recognition rate achieves 100% in treating the standard bearings experiment data, which shows a significant effect and strong application value.