Fault diagnosis of bearing bidirectional sensor information fusion based on probability slice cumulative characteristics

Since the collected bearing vibration signals are susceptible to many uncertainties caused by the environment, a fusion fault diagnosis method based on probabilistic slicing cumulative features of bi-directional sensor information of bearings was used for the qualitative analysis of bearing faults. Probability box theory (P-box) method was applied to model the time domain signals from horizontal and vertical sensors separately to reduce the negative impact of cognitive uncertainty and fully characterize the multi-directional fault vibration information. Further, the probability box was divided into focal element slices to extract the model probability slice cumulative feature matrix. Subsequently, a dual-channel Parallel Convolutional Neural Network (PCNN) was established to fuse the bi-directional feature information by constructing a fusion layer before the fully connected layer of the network. A normalized exponential function was an input to achieve the fault site identification. The analysis results of wheel-to-wheel bearing data of a railroad bureau showed that the proposed method had high accuracy and stability when dealing with imbalanced data sets of fault degree and had certain robustness under different noise conditions. © 2023 CIMS. All rights reserved.