Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model.

The working environment of rolling bearings is highly complex and often the vibration signal of the bearing is mixed with noise, which makes fault diagnosis challenging. As such, it is imperative to denoise the vibration signal of rolling bearings, extract effective vibration features, and improve classification accuracy. In this research, we propose a rolling bearing fault diagnosis model based on adaptive modified complementary ensemble empirical mode decomposition (AMCEEMD) and a one-dimensional convolutional neural network (1DCNN). Firstly, the AMCEEMD method is proposed. This algorithm is an improved signal processing technique based on CEEMD, which introduces fuzzy entropy and kurtosis values to remove noise and identify impulse signals. The purpose of AMCEEMD is to obtain standard Intrinsic Mode Functions (IMFs) while removing noise. Secondly, we introduce the energy ratio, fuzzy entropy, and kurtosis as selection indices for IMFs. The selection of IMFs is adapted, and the selected IMF features are inputted into 1DCNN for fault classification. Finally, it was validated by two bearing experiments and compared with other classification methods. The classification accuracy of AMCEEMD-1DCNN method in this study is higher than other methods. The effectiveness of the AMCEEMD-1DCNN fault diagnosis model was verified.

Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN.

Due to the structure of rolling bearings and the complexity of the operating environment, collected vibration signals tend to show strong non-stationary and time-varying characteristics. Extracting useful fault feature information from actual bearing vibration signals and identifying bearing faults is challenging. In this paper, an innovative optimized adaptive deep belief network (SADBN) is proposed to address the problem of rolling bearing fault identification. The DBN is pre-trained by the minimum batch stochastic gradient descent. Then, a back propagation neural network and conjugate gradient descent are used to supervise and fine-tune the entire DBN model, which effectively improve the classification accuracy of the DBN. The salp swarm algorithm, an intelligent optimization method, is used to optimize the DBN. Then, the experience of deep learning network structure is summarized. Finally, a series of simulations based on the experimental data verify the effectiveness of the proposed method.