Condition-based maintenance(CBM) has attracted widespread attention as it can help to reduce maintenance cost and enhance reliability of systems. For large K-out-of-N systems, a common problem in CBM is that finding optimal maintenance solution is usually too computationally demanding due to the exponential explosion of state space and action space, when there is dependence between components in the system. In this paper, a scalable approach is proposed aiming at solving CBM problem of large-scale K-out-of-N systems with economic dependence. The approach works to find an approximate solution with an acceptable computational load. The formulation of component-wise Markov Decision Process(CW-MDP) and adjusted componentwise Markov Decision Process(ACW-MDP) are based on extended single-component actions space and distributed singlecomponent reward function so that component-level solutions are obtained and help to make system-level decision without using huge amount of computational resources. Convergence properties are analyzed in this paper to show theoretical optimality gap. Numerical study and case study are conducted to demonstrate the effectiveness and efficiency of our approaches.