A Zero-Shot Fault Diagnosis Framework for Chillers Based on Sentence-Level Text Attributes

Abstract

In the real chiller fault diagnosis tasks, the training data of target faults is commonly unavailable. This scenario restricts the diagnostic performance of data-driven methods. The existing zero-shot learning-based diagnostic models have conquered this challenge by introducing sparse fault semantic attributes. However, the sparse semantic representation misses the interconnections between attributes, which causes difficulty in representing holistically the latent associations between categories. In this article, an innovative zero-shot fault diagnosis (ZSFD) framework for chillers based on sentence-level text attributes is proposed to overcome this difficulty. The defined fault attributes contain two textual labels for the cause and consequence. Subsequently, all text attributes for each category are combined into a sentence (in text form), which is named sentence-level text attributes. Afterward, a semantic representation model, namely, sentence-level bidirectional encoder representations from transformer (SBERT), is employed to project sentence-level text attributes as the dense semantic attributes. Later, a cross-modal contrastive embedding (CMCE) model is established to embed semantic attributes and collected sensor data into a common latent space, where the distributions of attributes and data are aligned. Meanwhile, a hybrid reconstruction classification strategy is designed in CMCE to fully integrate the intrinsic characteristics of categories from both modalities. Finally, the unseen semantic attributes embedded by the CMCE are employed for training zero-shot learning classifier. Extensive experiments are designed and executed on the chiller dataset. The results demonstrate that the proposed framework can improve the distribution consistency between data and attributes compared with the ZSFD methods based on sparse representation, and achieves satisfactory diagnostic performance.