Real-time detection of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model’s simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function’s limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing.