Meta-learning with variational inference for few-shot faults diagnosis of automotive transmission under variable operating conditions

The automotive transmission is a critical component for regulating vehicle speed. However, in real industrial settings, the complexity and variability of operating conditions, along with a limited number of fault samples, make traditional deep learning methods inadequate for practical applications. To address these challenges, this paper presents a few-shot fault diagnosis method for automotive transmissions under variable conditions, based on Variational Agnostic Meta-Learning for Robust Inference (VAMPIRE). First, the vibration data collected from sensors is sliced and converted into two-dimensional grayscale images to create the dataset. Next, by integrating Bayesian theory with a meta-learning framework, we use variational inference to approximate the posterior distribution. This allows the learned meta-parameters to coherently explain the variability of the data, thereby enhancing the model's generalization ability across different operating conditions. Finally, this study utilized data from an industrial-grade gearbox test bench and real-road test data of an industrial truck gearbox to conduct comparative experiments under multiple variable working conditions, and compared the results with various methods. The experimental results show that regardless of the sample size or the complexity of working conditions, the proposed method performs excellently in terms of accuracy, stability, and generalizability. For example, in test scenarios involving multiple unknown working conditions, the proposed method achieved an average diagnostic accuracy of 96.52 % for test bench data and 97.54 % for real-vehicle data in 5-shot learning tasks. Even in the most challenging 1-shot learning tasks, its average accuracy remained at 93.88 % and 94.82 %, respectively, significantly outperforming the comparative methods.