Improved Semi-supervised Prototype Network for Cross-domain Fault Diagnosis of Gearbox under Out-of-distribution Interference Samples

In practical engineering, it is difficult to acquire sufficient available fault samples of gearbox with the same distribution, in addition, the acquired unlabelled samples will inevitably be mixed with some out-of-distribution unknown interference samples,which will bring challenges to the existing research on intelligent fault diagnosis of gearbox. A new method based on an improved semi-supervised prototype network is proposed for cross-domain fault diagnosis of gearbox between different working conditions under out-of-distribution interference samples. First, a label allocation criterion is designed, which can fully exploit the information and assign pseudo-labels of the unlabelled samples to effectively suppress out-of-distribution interference samples. Then, a modified cost function is defined based on label smoothing and metric scaling to fully evaluate the similarity between fault samples, which can exploit the generic characteristics of the meta-learning task, and further improve network generalisation