Abstract
Industrial machinery is developing in the direction of large-scale, automation, and high precision, which brings novel troubles to mechanical equipment management and maintenance. Intelligent diagnosis of mechanical running state based on vibration signals is becoming increasingly important, and it is still a great challenge at pattern recognition. As one of the indispensable components in mechanical equipment, planetary gearboxes are widely used in wind power, aerospace, and heavy industry. However, the problem of automatically maximizing the accuracy of planetary gearbox under different working conditions has not been solved. Therefore, an intelligent diagnosis method for planetary wheel bearing based on constrained independent component analysis (CICA) and stacked sparse autoencoder (SSAE) is presented in this research. Firstly, the fault signal with obvious time-domain characteristics is extracted by constrained independent component analysis (CICA), and the fault signals and noise is separated. Then, calculating the correlation kurtosis value of the time domain signals at different iteration periods as the eigenvalue to obtain the training samples and the test samples. The parameters of the network layer, the number of hidden nodes and learning rate are determined to build the model of SSAE. In the end, the training samples are input into the model for training and the whole network is fine-tuned. The advantages and disadvantages of the model are verified by the test samples. The intelligent classification and diagnosis of the mechanical running state are completed. Experiments analysis with real datasets of planetary wheel bearing show that the proposed method can achieve higher accuracy and robustness for fault classification compared with other data-driven methods. The application of this method in other major machinery industry also has bright prospects.
Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu's Thresholding Technique
