A fast identification method of train axle crack parameters based on semi-inverse Kriging method

Abstract

On-line monitoring of crack parameters can prevent axle breaking accidents and potentially disastrous consequences, while satisfying the requirement of the damage tolerance, and improving the economy in operation. A fast and reliable crack parameters identification method is required for on-line monitoring of a large number of axles. Based on the response surface characteristics of crack position and depth at two sensors, a new method is proposed adopting two Kriging surrogate models constructed by the 2xRev values of vibration signals from two measurement points. Firstly, by exchanging part of inputs and outputs, a semi-inverse Kriging model is constructed to obtain a crack equivalent line. Then, the crack equivalent line is projected on another Kriging model as the convergence path, which is the key step transforming a two-objective optimization problem into a one-dimensional searching problem. Finally, an improved dichotomy method is used to obtain the result, by which the total number of steps and the calculation time is controlled on the premise of ensuring accuracy. Different from current multi-objective optimization algorithms searching with parallel computing, the proposed method reduces the number of parameters and searching model space to be one, belonging to serial computing. The comparison to other methods shows excellent features in computing speed and stability, and the experiment was implemented on a double-disk rotor system with similar structure to a wheelset, validating the proposed methodology.