Dynamic identification of coupler yaw angle of heavy haul locomotive: An optimal multi-task ELM-based method

Abstract

The lateral instability of the couplers seriously threaten the operation safety of 20,000-tonne heavy-haul trains. It is essential to dynamically monitor the coupler yaw angle (CYA) in order to evaluate the operation stability of the train. However, the traditional direct measurement methods for CYA are hindered by the harsh service environment, making it difficult to maintain long-term stability in monitoring. To address these issues, within this paper a multi-task deep multiple kernel extreme learning machine (MT-DMKELM) is developed to quantitatively identify CYA based on the multi-channel relative displacements between the car body and frame (RDCFs). Firstly, the correlation of multi-channel RDCFs is analyzed based on the dynamics model, and the data fusion of strong correlation channels is achieved through an extreme learning machine auto-encoder. Then, a deep network for feature extraction is created by stacking numerous multiple kernel extreme learning machine auto-encoders. Further, the high-level abstract features are input to multiple kernel extreme learning machine for regression estimation of CYA. In addition, the optimal model parameters are obtained using the genetic mutation particle swarm optimization for MT-DMKELM to improve the CYA identification performance. Finally, both dynamics simulation and field test are conducted to confirm the capability of the proposed methods in CYA identification.