To address bottlenecks and challenges caused by scarce fault samples, unclear failure modes, and insufficient methods for analyzing the reliability performance of regenerative braking energy feedback devices in urban rail transit, this study analyzes equipment reliability by collecting and examining fault event data for these devices. The paper establishes a fault tree model for the regenerative braking energy feedback devices and explores Gaussian kernel density estimation (KDE) to obtain a fitted failure probability distribution function, thereby constructing a reliability modeling and analysis method for such devices. This approach transforms discrete monthly mean time between failures (MTBF) data into a smooth probability density function curve, effectively narrowing the fluctuation range of reliability estimates. Furthermore, it calculates the core MTBF value range with a 95% confidence interval, significantly enhancing both the intuitiveness and scientific understanding of fault data and its distribution for such equipment. This methodology assists in precisely determining the optimal timing for scheduled maintenance, enabling dynamic maintenance adjustments, and supports future equipment selection and lifecycle management decisions.