Dynamic modelling of the defect extension and appearance in a cylindrical roller bearing

Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation

Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization

Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement CNN

The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads.

https://www.mdpi.com/2079-9292/10/1/59/pdf

Dongxing Mao

Papers

Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network