As the key running part of the railway freight transportation system, the wheel not only bears the load of the vehicle, but also ensures the running and steering of the car body on the rails. The frequent high-speed friction with the rail and brake is the main reason for early failure of wheelset tread. Therefore, real-time status monitoring and early fault diagnosis of wheel treads have become key technical issues that must be solved in the reform of the railway freight maintenance system. In this paper, an adaptive hybrid Simulated Annealing Cuckoo Search algorithm (SA-ACS) is proposed and applied to the Deep Belief Network (DBN). The SA-ACS-DBN algorithm is used to improve the training speed and convergence accuracy of the diagnosis model. Finally, it is found through the comparison experiment of wheel tread fault data that the data results prove the feasibility of the SA-ACS-DBN model in the application of wheelset fault diagnosis.

车轮作为铁路货运系统的关键运行部件，不仅承受车辆的载荷，而且保证车体在轨道上的行驶和转向。与钢轨和制动器频繁的高速摩擦是轮对胎面早期失效的主要原因。因此，车轮踏面的实时状态监测和早期故障诊断成为铁路货运维修体制改革必须解决的关键技术问题。在本文中，提出了一种自适应混合模拟退火布谷鸟搜索算法（SA-ACS）并将其应用于深度信念网络（DBN）。SA-ACS-DBN算法用于提高诊断模型的训练速度和收敛精度。最后，