MSDF-VAE: A Cloud–Edge Collaborative Method for Fault Diagnosis Based on Transfer Learning

In intelligent manufacturing systems, accurate and timely fault diagnosis is crucial for ensuring a safe and stable manufacturing process. While transfer learning (TL) can mitigate the need for extensive labeled data, not all historical datasets are applicable to specific fault diagnosis tasks, and the use of inappropriate datasets can deteriorate the accuracy of TL models. To address these issues, a TL fault diagnosis method based on cloud-edge collaboration is proposed. First, a variational autoencoder TL algorithm based on multiscale convolution and domain fusion (MSDF-VAE) is presented to effectively leverage extensive historical fault data, particularly in scenarios with limited labeled samples. Second, a lightweight autoencoder model (LAE) is employed to improve the reusability and specificity of historical data and fault diagnosis models by analyzing the correlation between historical data and the current data. Additionally, to reduce latency and meet real-time requirements for fault diagnosis tasks, a cloud-edge collaborative framework is proposed, within which MSDF-VAE and LAE are deployed. This approach enables real-time diagnosis using the MSDF-VAE model at the edge layer, while the cloud layer concurrently trains a high-precision model with the selected data by the LAE. The experiments verify the accuracy of the MSDF-VAE and confirm the effectiveness of the proposed cloud-edge collaboration framework.