With the increase of working time, the health status of analog circuit declines. The faulty parameter estimation in early fault state can accurately evaluate equipment health state and provide reference for fault prediction. Based on the transfer function of the circuit and the measured fault response, the possible fault parameters can be inversely derived. Due to the influence of tolerance, the same fault response can be generated by many parameter combinations. This article transforms the fault parameter estimation problem into a multi-objective optimization problem through mathematical analysis. In view of the problems of the huge difference in the optimization objective scale and the difficulty in generating a reasonable weight vector, it proposes to guide the population evolution based on the logarithmic distribution reference point and proposes a logarithmic distribution reference point-based decomposition multi-objective evolution algorithm. This method can accurately and stably find the optimal solution of the fault parameter estimation problem. Through simulation of the jump filter circuit, it is verified that as the tolerance increases, the parameter range becomes wider, and the maximum standard deviation is only 18. 616 Ω. In terms of time efficiency, there is no significant difference in the running time of 12 fault instances with three different tolerances, and the average running time is 0. 7 s. The correctness and robustness of the algorithm are verified by experiments, and compared with other multi-objective evolution algorithms in the forefront of this direction, the method proposed in this article is more accurate than the bi-objective evolution algorithm by 2~ 3 orders of magnitude in terms of accuracy, and has a wider fault interval than the method proposed by Tadeusiewicz, which verifies the higher accuracy and effectiveness of the method proposed in this article, reflecting the superiority and reliability of the method proposed in this article.