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Conventional machine learning techniques, such as support vector machine and back propagation, have disadvantages in handling the non-linear relationships and complicated structure of massive data. Deep learning (DL) methods have a greater capability to address complex and heterogeneous machinery signals, and identify faults more accurately. This paper presents a review of DL methods in emerging research in the machinery fault diagnosis field. First, common DL models are briefly described. Then, the application of DL to machinery fault diagnosis is described in detail, including the problems DL aims to solve and the achievements it has accomplished thus far. To demonstrate the capability of DL to handle the multiplicity and complexity of equipment faults and massive data, we examine experimental results for typical reciprocating compressor and bearing. 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