Aiming at fault vibration signals of rolling bearings with features of stronger non-stationarity and easy to be disturbed by strong background noise, a fault diagnosis method based on the short-time Fourier transform (STFT) and the convolution neural network (CNN) was proposed to realize the end-to-end fault pattern recognition.Firstly, STFT was performed for vibration signals of rolling bearings to get their time-frequency spectrum samples divided into a training set and a test one.Then, the training set was input into CNN to do learning and update parameters of CNN.Finally, the CNN model with updated parameters was applied in the test set to output the fault recognition results.Through simulation tests of rolling bearing faults, the feasibility and effectiveness of the proposed method were verified.The results indicated that the proposed method has higher recognition accuracies for different types of faults; the robustness of this method can be improved with increase in amount and type of fault data; it is a fault diagnosis method suitable for dealing with big data.
李 恒，张 氢，秦仙蓉，孙远韬. 基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J]. 振动与冲击, 2018, 37(19): 124-131.
LI Heng, ZHANG Qin, QIN Xianrong, SUN Yuantao. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(19): 124-131.
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