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Intelligent pulping equipment is a complex engineering equipment with high professionalism. Once a fault occurs, it will have serious consequences. However, in the process of transforming relevant scientific and technological achievements into products, it is difficult to find the potential faults of the products by means of big data analysis due to the lack of actual use data of the previous products. In this paper, non-participatory observation method and field investigation method are used to investigate the behavior of users operating intelligent pulping equipment. Taking workflow as the main line and operation behavior as the hub, the relationship between human-machine-environment is established. Through the analysis of user behavior video data, the potential fault behavior of users was found out, and the corresponding fault causes were analyzed. Then, the cluster analysis of the fault causes is carried out, and the index system of potential fault identification of intelligent pulping equipment is constructed, including 3 first-grade indexes, 18 second-grade indexes and 57 third-grade indexes. Finally, the index system is applied to evaluate a new intelligent pulping product, and 43 potential faults are quickly identified, which clarifies the direction for the subsequent optimization design of the product. The results show that the index system of potential fault identification of intelligent pulping equipment based on user behavior analysis can quickly identify the potential faults and fault causes of new products, so as to quickly improve the maturity of products, which has certain guiding significance for the transformation of relevant scientific and technological achievements.
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