Abstarct: In mining, production/output assurance and customer satisfaction depend on foresight and knowledge of the future status of heavy mining equipment. Because the equipment is responsible for production in the current mining automation system, the performance of these types of equipment can be measured baxsed on the device's health or, in other words, the number of failures that occurred. In this case, predicting the breakdown of expensive machinery is fundamental, and their maintenance (net) before failure can prevent the mine from incurring high costs. Therefore, the analysis of the Songun mine extraction fleet requires a broad and detailed concept to monitor the behavior of systems and subsystems more closely during their useful life. The idea of residual useful life (RUL) is a new concept that analyzes system behavior and delivers more realistic results. This concept predicts the remaining useful life before a system fails baxsed on current conditions and past operating profiles. Therefore, RUL estimation will be as effective in net optimization as system reliability estimation. Preventing a failure after identifying it and determining how the system is discounted requires timely application of the net using a correct RUL estimation. In this dissertation, the concept of reliability is used in estimating the RUL. This concept can also be used to plan net activities, estimate spare parts, and determine its profitability for the owner. The impact of environmental conditions as "obvious risk factors" was also included to complete this approach and bring the analysis results to the actual situation. Notwithstanding the rough working environment conditions in the analysis of mines, there will be a significant disruption in all results. However, it is not possible to determine and enter all risk factors in the analysis. These factors should be included in the analysis as "intangible risk factors" due to lack of information, accurate environmental knowledge, expertise, or short data collection period. For this purpose, expansions baxsed on the Cox regression model were used. This model can simultaneously analyze the operational data (time series) and environmental conditions (tangible and intangible risk factors). In the next step, to validate the proposed algorithm for estimating the RUL baxsed on obvious and unobvious risk factors, a case sample of the loading system (Shaul Komatsu 1250) from the Songun copper mine was selected in 8 months. After collecting the required data, reliability analysis was performed, and the RUL of the loading system was estimated. Finally, an appropriate maintenance strategy was proposed baxsed on the calculated RUL for the system.