With the advancement of industrial big data technology, supervised methods applied to industrial objects have been widely studied. However, real data often follow the long-tailed distribution phenomenon, which poses problems such as model degradation and failure in practical applications for traditional supervised models. The proposal of zero-shot learning (ZSL) technology provides a new approach to solve this problem. The objective of ZSL is to train the model using collected seen category data so that it can also be applied to unseen categories whose data cannot be collected. By incorporating auxiliary knowledge such as fault text description into the model, ZSL reduces the dependence of the model on training data collection in practical industrial scenarios and enhances its generalization performance. However, there is still a lack of systematic review and discussion on the application of ZSL in the industrial field. Compared with ZSL in other fields, industrial ZSL is unique in terms of auxiliary knowledge collection and processing, research methods, and application scenarios. Given the potential great application value and future development potential of ZSL in the industrial field, this paper systematically summarizes and presents the motivation, evolution, and challenges of ZSL theoretical models for industrial applications. Firstly, this paper reviews the development of ZSL settings and related methods, analyzes their correlation with other task settings, and highlights differences between this paper's review and previous ones. Next, this paper reviews the current state of zero-shot learning research in the industrial field, introduces typical industrial zero-shot learning tasks and auxiliary knowledge, analyzes the features and typical problems of industrial zero-shot learning, and summarizes the existing methods used in industrial zero-shot tasks. In addition, this paper also presents the benchmark datasets and open-source works for industrial zero-shot tasks. Finally, based on the existing research, this paper summarizes the problems and challenges faced by industrial zero-shot tasks, and provides some prospects for the research in this field.