Semantic-Consistent Embedding for Zero-Shot Fault Diagnosis

Abstract

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that cannot be collected in advance, which may restrict the performance of fault diagnosis methods. In this article, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Barlow matrix</i> is designed to measure the consistency between the embedding o