Zero-shot bearing diagnosis via semantic prototype synthesis: From single to compound faults

Abstract

The diagnosis of compound faults in bearings is a challenging issue in real industrial scenarios, as these faults have a significant impact on production and safety. Moreover, the data related to these compound faults are difficult to collect and label, making data-driven fault diagnosis methods inapplicable. To address the scarcity of compound-fault data and the absence of labeled samples in bearing systems, this article proposes a zero-shot diagnosis method based on semantic embedding. By training solely on single-fault data, this approach effectively overcomes the shortage of compound fault samples. First, a binary semantic representation is constructed for each single fault using Top- k masking, and a neural semantic composer (NSC) is pretrained to generate realistic compound-fault prototypes. Second, a multisensor robust convolutional neural network is designed for feature extraction. Finally, a synthetic compound-fault training strategy is introduced to enhance model generalization. By aligning the compound semantic targets generated from feature mixing and NSC, this strategy enables the model to learn the patterns in unseen compound samples. The necessity of each module is demonstrated through ablation experiments. The effectiveness and generalization ability of the method are verified using the HDU and HUST datasets. Under various operating conditions, the proposed method demonstrates optimal or nearly optimal accuracy. The code will be made publicly available on https://github.com/su-yibei/zero-shot-fault-diagnosis .