Abstract: In order to address the problem that there were difficulties in equipment reorganization, featureless extraction of major faults and insignificant interaction between devices in the health assessment methods of traditional equipments in factories, a dynamic Bayesian network-based equipment health assessment method was proposed. Using the real-time data collected from the production line of the intelligent factory, the factory data was characteristically extracted and the hourly output was used as an indicator to assess the health condition of the factory; and the hourly output was used as an index to evaluate the health of the factory; the random forest algorithm was used to extract the main fault types that affected the output of the factory's production line, and a dynamic Bayesian network was constructed for equipment health assessment. The results demonstrate that dynamic Bayesian networks have a superior ability to learn, and that when the input sample is large enough, the health level of equipment is estimated to rise from 38.4% to 64.1%, the prior probability gets closer to the true distribution, which is more conducive to analyzing the interactions between equipment and the health of the production line equipment. It can provide an effective way to evaluate the equipment and achieve effective maintenance of the equipment.

Key words:  random forest algorithm; dynamic Bayesian network (DBN); health assessment;fault types