Abstract: Efficient mining machinery operation is essential for coal mining enterprises to reduce production costs, improve production efficiency, and maximize profits. As a key consumable part of mining machinery, the reliability of machine picks directly determines the operational performance and service life of the machinery. During the mining process, the coal and rock are regarded as normal working loads, the gangue and faults being regarded as random shock loads. The picks can be affected by natural wear and tear caused by the coal and rock, and by load shocks caused by the gangue and faults. The pick failure is the result of the competing influence of soft failure caused by natural wear and tear and hard failure caused by random load shocks. Because the gangue and faults have a certain volume and hardness, a random load shock may last for a certain period of time, producing different acceleration effects on the pick wear under different hardness. At the same time, the ability of the pick to resist the fatal shock may be weakened as the wear degree increases. The reliability of the pick competing failure process under random load shocks is modeled by considering the influence of continuous shocks, the accelerated degradation at changing rates during shock periods, and the changing hard failure threshold on the pick wear. Firstly, the pick degradation model under random load shocks is established by considering the natural wear degradation, instantaneous shock degradation, and accelerated degradation at changing rates during shock periods. Secondly, the pick reliability model under the competing failure mode is established on this basis. Finally, a numerical experiment and an effectiveness analysis of the pick reliability model are conducted based on the engineering data. The results show that the competing failure reliability model of the pick considering continuous shocks, a changing degradation rate and changing hard failure threshold is in line with engineering practice, which provides a theoretical basis for pick design optimization, maintenance decision and spare parts management.