Bearing Fault Identification Method under Small Samples and Multiple Working Conditions

Abstract

Aiming at the problem of the low bearing faults identification accuracy of the method based on the deep neural network under small samples and multiple working conditions, a novel bearing fault identification method combined with the coordinate delay phase space reconstruction method (CDPSR), residual network, meta-SGD algorithm, and the AdaBoost technology was proposed. The proposed method firstly calculates the high-dimensional space coordinates of bearing vibration signals using the CDPSR method and uses these coordinates to construct a training set, then learns and updates the parameters of classifier networks using the meta-SGD algorithm with the train set, iteratively trains multiple classifiers, and finally integrates those classifiers to form a strong classifier by AdaBoost technology. The 4-way and 20-shot experiments of artificial and natural bearing faults show that the proposed method can identify the fault samples and nonfault samples with 100% accuracy, and the fault location accuracy is over 90%. Compared with some state-of-the-art methods such as WDCNN and CNN-SVM, the proposed method improves the fault identification accuracy and stability to a certain extent. The proposed method has high fault identification accuracy under small samples and multiworking conditions, which makes it applicable in some practical areas of complex working conditions and difficulty obtaining bearing fault signals.