A label information vector generative zero-shot model for the diagnosis of compound faults

Diagnosis of compound faults remains a challenge owing to the coupling of fault characteristics and the exponential increment of the number of possible fault types. Current compound faults diagnostic methods often require a large number of training data for each type of compound fault. In real-world scenarios, training data of compound faults are usually difficult to acquire and sometimes even inaccessible. In contrast, single fault samples are much easier to obtain. Thus in this paper inspired by the idea of zero-shot learning, we present a novel label information vector generative zero-shot model to identify unknown compound faults, using only single fault samples as the training set. This model comprises several modules, namely label information vector (LIV) definition, feature extractor, and generative adversarial modules, respectively responsible for representing the prior knowledge of specific class labels for the single fault and compound fault, extraction of fault features, and mapping the relationship between the fault features and the fault LIVs. By adversarial training between the samples and LIVs of single faults, the model can generate compound fault features using the compound fault LIVs. Thus the unknown compound faults are identified by measuring the distance between the features extracted from the testing compound fault samples and the generated features from LIVs. The proposed method is evaluated on a self-built experimental platform. The results demonstrate that without any compound fault samples in the training set, the compound fault classification accuracy of the model reaches 78.10%.