An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder

Table 2

Parameter description of the five methods in Experiment 1.

Methods	Parameter description
The proposed method	The network structure parameters are 1200-800-100-20-10, learning rate is 0.3, momentum is 0.5, training iteration number is 10, fine-tuning iterations are 50, the Gaussian kernel parameter is 26.40.
Standard SAE	The network structure parameters are 1200-800-100-20-10, learning rate is 0.3, momentum is 0.5, training iteration number is 10, fine-tuning iterations are 50.
Standard DBN	The network structure parameters are 1200-800-100-20-10, learning rate is 0.3, momentum is 0.5, training iteration number is 10, fine-tuning iterations are 50.
The proposed method with PK	The network structure parameters are 1200-800-100-20-10, learning rate is 0.3, momentum is 0.5, training iteration number is 10, fine-tuning iterations are 50, the PK parameters are b = 0, d = 1.20.
The proposed method with PEK	The network structure parameters are 1200-800-100-20-10, learning rate is 0.3, momentum is 0.5, training iteration number is 10, fine-tuning iterations are 50, the PEK parameter is 30.00.