Measurement Model of Tread Wear Based on SQPSO Optimized DELM

In view of the difficulty in establishing accurate mathematical model of wheel rail wear and in evaluating, predicting and quantitatively calculating wheel rail wear under various working conditions, this paper proposed a tread wear prediction method based on SQPSO optimized DELM model(SQPSO-DELM). First of all, the derivative characteristics were introduced into the learning machine, and a derivative learning machine model (DELM) was proposed. Then, the sequential quadratic programming (SQP) and quantum particle swarm optimization (QPSO) algorithm were introduced to optimize the parameters of DELM. Through SQPSO-DELM prediction model, the maximum wear of wheel tread under different test parameters of vehicle dynamics model simulation and the actual measured value of wear degree of on-site train tread were trained and predicted. The results showed that the performance parameters of SQPSO-DELM prediction model were better than LSSVM, ELM, PSO-ELM and QPSO-ELM, which can better reflect the influence of different parameters on wheel tread wear value.