Abstract: Electromechanical equipment for mine production is a complex coupling structure of mechanical,electrical,hydraulic,and control systems. During the working process,the environ ment and working conditions are varied. Currently there is a lack of effective technologies used for the real-time perception of health status of mining equipment. The Internet of Things,Industrial Internet,artificial intelligence,and big data mining technologies are used to study the modeling methods of mining equipment state knowledge and online diagnostic technologies. The traditional “ human- machine” interactive monitoring mode will be promoted to the “sensor-machine cognition-machine decision” intelligent monitoring mode. The architecture of mining equipment state awareness system under Industrial Internet is analyzed,and the roles of the four layers of multi-source information perception layer,edge intelligence layer,big data a- nalysis layer and data and knowledge sharing migration layer are defined,and the equipment state knowledge sharing and migration mode is proposed. Information description,knowledge representation,and decision fusion methods for the state knowledge modeling of mining equipment systems are designed using ontology semantics,confidence rules,and digital twin technologies. A “virtual-real fusion” perception model for real-time perception,evolutionary analysis,and intelligent interaction for the entire process of mine equipment operation is proposed to achieve “precise mapping,information duality,fusion interaction,and collaborative evolution” of virtual and real system operation processes. The research status,technology architecture,existing problems and research trends of data-driven mining equipment condi- tion diagnosis methods are analyzed,the combination of digital twins,deep learning,transfer learning and other meth- ods are proposed to build the mining equipment status diagnosis mode with the integration of a mechanism model,empirical knowledge and deep features,the big data analysis of mining equipment status and application technology are studied,and the mining equipment status diagnosis and comprehensive Intelligent application service system such as life cycle management,and a big data platform framework for mining equipment condition diagnosis are constructed. Mine equipment operation information perception,knowledge modeling and status online identification method system is formed to provide guidance for the self-diagnosis of mining equipment fault status,early maintenance of hidden dagers,intelligent scheduling and collaborative management and control,and to provide technology for intelligent and unmanned mining support.