An efficient strategy of maintenance and replacement ensures safe and stable operation of power equipment. Existing reliability-centered maintenance (RCM) strategies lack dynamic risk regulation

leading to unevenly distributed inspection timing and significant risk fluctuations. To address this issue

this paper proposes a collaborative optimization method for imperfect maintenance and replacement based on iterative feedback. Firstly

a Weibull-based failure probability model is built that incorporates equipment importance and economic factors and an imperfect maintenance mechanism. Then

a segmented feedback optimization model is established and solved via Lagrange relaxation and mixed-integer linear programming for multi-period decisions. An iterative feedback mechanism integrates prior risk deviations into subsequent decisions

enabling smoother risk profiles and adaptive adjustments. Case studies show that

compared to traditional time-based and condition-based maintenance

the method can be used to improve the cost-efficiency by 8.3%. Against RCM methods

it reduces equipment failure risk volatility by 18% and enhances maintenance timing balance by 17%. Sensitivity analysis confirms method robustness to key parameters and scale. This study offers a systematic theoretical framework and practical solution for achieving economic

reliable

and stable long-term O&M planning in power systems.