The work aims to conduct research on a variable-condition fault diagnosis algorithm based on parameter transfer to improve the accuracy and reliability of bearing fault diagnosis under complex working conditions, so as to address the challenge that the large mechanical specifications and high operating speeds of printing equipment lead to significant noise in bearing vibration signals, weak fault characteristics, and difficulties in precise diagnosis. A DSCDSN (Deep Separable Convolutional Dense Network) intelligent fault diagnosis model was constructed, and the noise was reduced through a data preprocessing module. A depthwise separable convolution layer was introduced to reconstruct the convolutional layer of the dense neural network, with its structure and hyperparameters determined. Based on the concept of fine-tuning transfer learning, a Fine-Tuning transfer learning model was built. The source-domain dataset was used to pre-train the network, transfer parameters, freeze part of the structure, and optimize by fine-tuning the hyperparameters of the last dense block's convolutional layer. Validation on the Case Western Reserve University dataset showed that the Fine-Tuning model could effectively identify bearing faults under variable conditions, demonstrating good diagnostic performance. In conclusion, this algorithm combines DSCDSN with the Fine-Tuning model, giving full play to the advantages of transfer learning, achieving effective diagnosis of bearing faults under complex conditions, meeting the research objectives, and providing an effective solution for fault diagnosis of actual production equipment.
Key words
variable conditions /
bearing fault diagnosis /
deep transfer learning /
parameter migration /
dense neural network
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