Aiming at the traditional rotor fault diagnosis method,which has low precision,poor adaptability,and is difficult to meet the demand for identifying complex and variable fault types,a fault detection method based on convolutional neural network is proposed to realize the diagnosis and research of rotor system faults.Through the HZXT-009 sliding bearing fault simulation comprehensive test rig to carry out rotor misalignment,unbalance,touching and other fault tests,to obtain data and analyze the time-domain vibration signals;to build a convolutional neural network fault diagnosis model;and to compare the method with the deep belief network(DBN)algorithm.The accuracy of this method can reach 99.16%,which is higher than that of the Deep Belief Network(DBN)algorithm,and this method is expected to be widely used in actual production to improve the efficiency and reliability of rotor system fault diagnosis.
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