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Bearings are widely used in rotating machinery, such as aircraft engines and wind turbines. In this paper, we proposed a new data-driven method called frozen convolution and activated memory network (FCAMN) for bearing remaining useful life (RUL) estimation based on the deep neural network. The proposed method is composed of two parts: the multi-scale convolutional neural network is carried out to pretrain the raw data to directly obtain the global and local features, and the second step isdoi:10.1109/access.2019.2929271 fatcat:iljysitqvfe3jeu3hi4dy7knoy