This paper proposes a signal diagnosis model that integrates Particle Swarm Optimization (PSO) and Support Vector Machine (SVM), aiming to address the problem of strategic regulation and optimization of braking systems under complex environments. This study established a brake block–metal disc experimental platform and systematically investigated the mechanisms by which exogenous environmental intrusions (dry, rainy, particle, muddy, and icy) influence the surface morphology of friction pairs and braking stability, by integrating surface analysis techniques and vibration signals. Mathematical tools, including the number of Intrinsic Mode Function (IMF) components and autocorrelation coefficients, were introduced to quantitatively characterize the complexity and chaotic features of the signals. The results indicate that different environmental conditions significantly alter the morphological characteristics of the wear interface, and that there exists a clear negative correlation between the number of IMF components and the autocorrelation coefficient. A multidimensional diagnostic model constructed based on these mathematical features enables high-precision identification of time-varying friction signals under various environmental conditions, achieving an identification accuracy of 97% on the test set, which represents a significant improvement over traditional model. The research provides a theoretical basis for the adaptive control of braking systems under complex environments. The proposed interdisciplinary approach, combining tribological measurement and mathematical analysis, holds significant engineering value for the development of intelligent braking technologies.
Lin et al. (Fri,) studied this question.