Whether a rotating part has local fault or not, the key is to judge if its vibration signal has periodic impact in space and period size. Convolutional neural network (CNN) is good at mining local important information features in data space, and has the advantage of "end-to-end" to overcome the defect of manual feature extraction. As vibration signal also contains rich information in time domain, the long-short term memory (LSTM) network is good at learning temporal relevance from dynamic sequence data. The gate recurrent unit (GRU) is a variant of LSTM, but compared with LSTM, its structure is more concise and the number of parameters is less. Here, combining advantages of CNN’s spatial processing ability and GRU’s temporal processing ability, a dual-channel feature fusion CNN-GRU gearbox fault diagnosis method was proposed. A parallel structure was adopted to make CNN and GRU extract fault features of the original vibration signal of gearbox simultaneously, and then feature vectors extracted from the two channels were combined into a fused feature vector, it was input into the software SoftMax for fault classification. It was shown that the proposed method can adaptively extract fused features of space and time sequencies directly from the original vibration signal to realize the "end-to-end" fault diagnosis. The test results showed that the proposed method has a higher recognition accuracy, and a certain practicability and feasibility.
Key words
gearbox /
convolutional neural network (CNN) /
gated recurrent unit (GRU) /
fault diagnosis
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