Abstarct: Railway is one of the basic economic and communication infrastructures of cities and countries. The role that railways play in the exchange of goods and passengers, is very important in the economic prosperity of countries and the provision of facilities for citizens. One of the main concerns of managers is to increase productivity by reducing costs, as the costs of constructing lines, supplying the railway fleet, and carrying out maintenance and repair activities in railways are very high. Due to the advancement of technology in recent years, the volume of information stored and maintained in organizations is increasing day by day. In a way that can be used using various methods, including methods baxsed on data mining, the patterns and rules contained in this data and used to improve organizations. Using data mining technique, the present study discovers meaningful rules of data from locomotives, as a complex technical system, with the aim of improving the efficiency of the troubleshooting process. In this research, the method of association rule mining using Apriori algorithm has been used. Association rule mining makes it possible to extract patterns and discover rules and relationships between items in a databaxse. The result of this research is the discovery of 20 frequent incidents in passenger locomotives, 18 cases of two-component law and 2 cases of three-component law that can be used in training railway operational personnel of the Islamic Republic of Iran (especially train drivers and locomotive repairmen), the process of troubleshooting and repairing locomotives, reducing off-schedule stops and reducing rail costs are very effective.