Abstract: Traditional fault diagnosis methods can only detect single signals, which is difficult for diagnosing complex systems and multi-factor faults. Therefore, this paper selects an expert system to solve this problem. The mechanical equipment fault diagnosis expert system mainly consists of six parts: knowledge base, knowledge acquisition, inference engine, comprehensive database, interpretation module, and human-machine interface. Using this expert system, the location, mechanism, nature, and development trend of faults can be quickly identified, helping to make rapid maintenance and replacement decisions, shorten maintenance and downtime, and avoid significant economic losses. Multiple tests and applications on the Shanghai-50 tractor have shown a fault diagnosis accuracy rate of over 95% and excellent operational performance. Keywords: Machinery; Fault Diagnose; Expert System; Network Database Abstract: Using the traditional fault diagnoses methods can only detect the single signal and it is very difficult to detect the complicated and many embranchment system, so it selects expert system to solve the problem. The expert system includes knowledge database, knowledge acquisition, reasoning machinery, database, explanation module, people and computer interface and so on. Using it can find fault component, mechanism, character and development trend in time, help make maintenance and replacement decisions, shorten maintenance and stop time and then avoid from large losses. At last, using it in Shanghai—50 tractors again and again, the fault diagnoses precision reaches 95% and the capability is good. Key Words: Machinery; Fault Diagnose; Expert System; Network Database Fault diagnosis of machinery is a technology that uses relevant information of machinery in operation to identify whether its technical status is normal, determine the nature and location of the fault, find the cause of the fault, predict the fault trend, and propose corresponding countermeasures [1]. With the development of science and technology, the complexity and automation of mechanical equipment are constantly increasing, making fault diagnosis of mechanical equipment increasingly difficult. Currently used mechanical equipment fault diagnosis technologies mainly include vibration diagnosis, noise diagnosis, infrared technology, ferrography, and acoustic emission technology. These technologies only use a single signal source and do not simultaneously detect and analyze information from other sources, making it difficult to diagnose complex systems or multi-factor faults [2,3]. Expert systems, due to their inherent characteristics, demonstrate superiority in mechanical equipment diagnosis, effectively diagnosing fault causes and trends. Therefore, this paper develops and researches a mechanical equipment fault diagnosis expert system to assist in mechanical equipment fault diagnosis. 1 System Overall Construction An expert system (ES) is a computer program system that simulates human experts solving problems in a specific domain. It contains a large amount of expert-level knowledge and experience in a particular field, enabling it to use human expert knowledge and problem-solving methods for reasoning and judgment, simulating the decision-making process of human experts to solve complex problems in that domain. A mechanical equipment fault diagnosis expert system utilizes the knowledge and experience of experts in the field to diagnose complex mechanical equipment faults, identify the location, mechanism, nature, and development trend of the fault, and quickly make maintenance and replacement decisions, thereby shortening maintenance and downtime and avoiding economic losses. For details, please click: The Construction of a Mechanical Equipment Fault Diagnosis Expert System