Mining is one of the most important industries in the world, playing a crucial role in providing raw materials required by various industries. Mining machinery plays an important role in the extractive industry and can be used in various mining operations, including open pit mining, underground mining and offshore mining. However, mining machinery can pose risks to operators and others in the workplace. These risks include mechanical, electrical, chemical and environmental risks. Risk assessment is an important process for identifying and managing these risks. Risk assessment involves identifying sources of risk, eva‎luating the likelihood of each risk occurring and assessing the severity of each risk. Based on the results of the risk assessment, measures can be taken to reduce the risks. Risk assessment in mining machinery is particularly important to prevent accidents and injuries, as such incidents can lead to serious injury, severe damage or death. They can also cause enormous costs for companies and industries. Risk assessment can also help to improve safety and productivity in the workplace. By identifying and managing risks, the workplace can be made safer. This can help to reduce accidents and injuries and increase productivity. In this thesis, an attempt was made to provide a comprehensive analysis of electrical and mechanical risks with an autonomous system approach by collecting data on the failures of the Sarcheshmeh’s copper mine dump truck fleet. New categories were defined for the data obtained and analyzes were performed based on time and number of failures. The qualitative results of the risk matrix method as well as the quantitative results of the risk priority number show that the highest number of failures is related to the engine, wheels and electrical subsystems, which are the most important subsystem according to the results. As a result, the engine accounts for about 30% of the failures. In addition, the most important failures in these subsystems were identified. For example, in the engine, the main causes of failure are lack of engine power, water leakage, oil leakage and problems with the engine part itself. To control these failures, there are solutions such as the oil threshold control sensor, which sends warnings to the self-driving system and the fuel flow sensor, as well as a sensor to determine the engine water threshold, which is proposed In the event of these failures, In addition to the mentioned cases, the sensors to control each failure were fully proposed and appropriate corrective actions such as emergency braking and non-emergency braking with the self-driving approach in case of any of these failures were also fully reviewed and presented. Comparing the two methods of risk matrix and risk priority number, it is clear that the matrix generally indicates critical failures by 15% more than the risk priority number, while the risk priority number indicates average and less risky failures by 11% more on average. The ultimate goal of this research is to identify and reduce the risks associated with mechanical and electrical failures when the driver is removed from the autonomous systems.

هادي حسيني

ايمان ايزدي نجف آبادي

ابراهيم قاسمي ورنوسفادراني , مرتضي راستي برزكي