Zero-Shot Compound Fault Diagnosis Method Based on Semantic Learning and Discriminative Features

Compound fault identification has always been a challenge in bearing fault diagnosis. Existing learning-based compound fault diagnosis methods require numerous labeled compound fault samples. However, it is impractical to collect numerous samples for each compound fault in industrial scenarios. Based on this, a zero-shot compound fault diagnosis method based on semantic learning and discriminative features is proposed in this article. First, to solve the absence of fault semantic vectors, we construct novel single and compound fault semantics, which contain both manually constructed low-dimensional semantics from the original vibration signal and high-dimensional semantics learning from the convolutional autoencoder (CAE). Then a convolution neural network-based feature extractor is designed, and an adaptive edge center loss is defined on the feature extractor to maximize the intercategory distance and minimize the intracategory distance. Finally, the semantic vectors of the fault are embedded into the feature space, and the Euclidean distance is used to measure the distance between the fault features and the semantic vectors to identify the category of unknown compound fault. The proposed method is validated on a self-built testbed. The results showed that the accuracy of compound fault identification reached 78.74% in the absence of compound fault samples.