The spring load adjustment method for six-axle high-power locomotives:
JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL(2020)
摘要
The stability and comfort of locomotives need to be guaranteed by load adjustment technology. Considering the defects of the traditional two-step adjustment method for locomotive load distribution, including its lack of efficiency and error accumulation, a new technical approach is developed here under entire locomotive conditions, simulating the load adjustment test via the application of shimming under the treads. In the case of high-power locomotives, a complete theoretical model is established based on the classical two-suspension model. According to the difference between shimming on the treads and on primary suspension positions, a transformation matrix is established with which to describe the conversion relationship between the shim quantity on the primary supporting positions and on the treads. Considering that locomotive load regulation is a nonlinear problem characterised by nonlinearity, parametric uncertainty and multiple optimisation objectives, this paper proposes QAGA, an optimisation algorithm for entire locomotive load adjustment based on an adaptive genetic algorithm and a quantum-behaved particle swarm optimisation algorithm, to carry out simulations using data from an HXD1D-type electric locomotive. Analysis of the simulation results proves that the proposed approach can significantly improve the efficiency, accuracy and feasibility of the entire locomotive load adjustment process.
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关键词
Spring load distribution,high-power locomotives,suspension model,particle swarm optimisation,adaptive genetic algorithm
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