Abstract: With the rapid development of mining equipment unmanned, intelligent technology, the traditional regular maintenance due to the difficulty of discovering potential faults and the lack of timeliness of maintenance, it has been difficult to meet the requirements of equipment operation and maintenance of intelligent mines. Therefore, the development of fault diagnosis technology is an important barrier that needs to be broken in the intelligent process of mining equipment. This paper summarizes the current status of the development of fault prediction technology and health condition assessment technology for mining equipment, and gives thoughts and suggestions on the existing problems and future development direction. It summarizes the development status of health condition assessment technology from four aspects: signal acquisition, feature extraction and fusion, health level classification, and assessment model establishment, and analyzes the challenges of mining equipment fault diagnosis technology from four aspects: micro-faults, coupled faults, fusion of multiple types of information, and quantitative and qualitative faults, and then conducts research in the areas of data acquisition, data processing, health condition assessment, and assessment model establishment. In view of the above research status and development direction, research is carried out in the aspects of data acquisition, data processing, health condition assessment and the establishment of assessment model, and it is proposed that the intelligent development of mining equipment should be diversified by combining with the emerging technologies, such as 5G network, artificial intelligence, big data and other technologies.