[18] proposed a new model of a tristable stochastic resonance system. [PERSON] et al. [19] innovated an adaptive two-dimensional piecewise tristable stochastic resonance method. [PERSON] et al. [20] contributed a mutual information-assisted feedforward cascaded SR approach, demonstrating enhanced signal-processing capabilities. In parallel developments, [PERSON] et al. [21] successfully implemented a coupled bistable system for weak-signal feature extraction, with the experimental results confirming its superior detection performance compared to conventional single bistable systems. [PERSON] et al. [22] pro posed a high-dimensional spatial model based on a coupled bistable stochastic resonance system and analyzed the output signal-to-noise ratio of the high-dimensional spatially coupled bistable stochastic resonance system in different dimensions. The results showed that the output signals of these coupled ends with the same system parameters were the same. [PERSON] et al. [23] proposed a novel and complex unsaturated piecewise linear four steady-state stochastic resonance system, which was applied to the early fault diagnosis of various bearing models under Gaussian white noise and demonstrated excellent fault detection capabilities. Further extending this concept, [PERSON] et al. [24] developed an advanced coupled multi-stable SR method. Notably, [PERSON] et al. [25] introduced a groundbreaking three-dimensional coupled multi-stable periodic potential-induced SR framework for rolling bearing diagnosis. [PERSON] et al. [26] proposed a novel stochastic resonance method for multi-stable coupled arrays driven by asymmetric tripartite noise. In addition, [PERSON] et al. [27] proposed a stochastic resonance model based on color noise and time delay, and transformed the classical bistable symmetric potential into a double-well variable potential well, which can directly control the barrier height through adjusting the parameters. SR and its improved methods have made significant progress in the field of weak signal detection. However, there is still a clear research gap in its application in reducing noise and enhancing weak feature extraction of leakage signals in hydraulic valves. The existing literature indicates that although the stochastic resonance method theoretically has the potential to enhance weak signals, its practical application in complex mechanical system fault diagnosis, especially in the processing of hydraulic valve-leakage fault signals, has not been fully explored. The innovation of this article lies in combining a new search and rescue team optimization algorithm with coupled multi-stable stochastic resonance, and applying it to the field of hydraulic valve-leakage signal enhancement. This method achieved effective noise reduction and enhancement of hydraulic valve-leakage fault signals by optimizing the system parameters and inputting them into a coupled multi-stable model. In addition, this method uses the Fourier transform to perform frequency domain analysis on the processed signal, further verifying the superiority of the proposed method in weak feature extraction. This study fills the application gap of stochastic resonance theory in hydraulic valve fault diagnosis. The remainder of this article is organized as follows. Section 2 presents the theoretical foundations, including the SaRT algorithm framework, performance evaluation metrics, and fundamental principles of coupled multi-stable systems. Section 3 details the comprehensive implementation process of the proposed SaRT-CMSR algorithm. Section 4 details the systematic parameter analysis using synthetic sinusoidal signals to investigate the influence of key system parameters on the output characteristics of coupled multi-stable systems. Section 5 presents the validation of the proposed method through experimental case studies, applying the SaRT-CMSR system for noise suppression and feature enhancement of leakage-induced vibration signals in three types of faulty check valves.