A meta transfer learning fault diagnosis method for gearbox with few-shot data

Abstract The gearbox of large-scale mechanical equipment operates under complex working conditions, and its operation and maintenance are crucial to ensure safe production. This paper proposes a fault diagnosis method for gearboxes under variable operating conditions with few-shot data to address the scarcity of data for specific fault types. Firstly, several fault diagnosis tasks are constructed under variable operating conditions. These deep features of different tasks are extracted by a deep convolutional neural network. In the training process, the model adaptively adjusts its parameters and inner loop learning rate, enabling it to acquire domain invariant features. The Batch Spectral Shrinkage is presented to reduce the impact of negative transfer and catastrophic forgetting on model optimization during knowledge transfer. The loss function is reconstructed using the weight balance strategy to mitigate the distribution discrepancy between the source and target domains. Consequently, the Meta-SGD transfer neural network framework enables a fault diagnosis model for gearboxes under variable operating conditions with few-shot data. The experimental datasets of the gearbox verify the effectiveness of the proposed fault diagnosis framework.