Abstract
Modern low-speed maglev trains typically use multi-node decentralized levitation control modules, which results in a complex levitation control system with coupling interaction. Conducting systematic levitation condition awareness of the levitation control system is still a promising challenge. In this paper, under the hypothesis of levitation residuals following normal distribution, a levitation condition awareness architecture for the levitation control system is proposed based on data-driven random matrix analysis. The proposed architecture consists of an engineering procedure followed by a cascaded mathematical procedure. In the decentralized engineering procedure, the data-driven modeling for individual levitation control modules is achieved by nonlinear autoregressive modeling with an exogenous input neural network, and the unknown parameters are identified by a modified combinatorial genetic algorithm. On this basis, high-dimensional analysis of streaming residual random matrices for the levitation control system is conducted aided by large-dimensional random matrix theory, and the control limits of the constructed indicators are well-designed using the theorical distributions. Based on the comparative analysis of the experimental datasets, the proposed awareness architecture is verified to show the effectiveness of the systematic condition evaluation of the levitation system, and incipient train-guideway interaction vibration abnormalities can be detected in a timely manner.
Highlights
As a promising transportation technology, low-speed maglev train has the advantages of low vibration and noise, no friction loss, environmental friendliness, and low maintenance costs [1]–[5]
The proposed architecture is comprised of two parallel procedures: an engineering procedure based on decentralized data-driven NARX neural network (NARX-NN) modeling, and a mathematical procedure of centralized data-driven random matrix analysis
During the decentralized engineering procedure, the NARX-NN modeling for individual levitation control modules (LCMs) can be achieved by associated parameter identification using the modified combinatorial GA algorithm, and this is the data foundation for the subsequent mathematical procedure
Summary
As a promising transportation technology, low-speed maglev train has the advantages of low vibration and noise, no friction loss, environmental friendliness, and low maintenance costs [1]–[5]. Several studies and demonstration projects have been carried out on medium-low speed maglev train, such as the Korean Urban Transit Maglev (UTM) [3], and the Japanese High-speed Surface Transport (HSST) [4]. Changsha Maglev Express (CME), in particular, which was officially put into operation. In 2016 in China, is the longest low-speed maglev commercial demonstration line in the world [5]. The levitation system is critical to long-term reliable and stable operation of maglev trains. For low-speed maglev trains, electromagnetic suspension (EMS) is utilized for levitation. EMS achieves train levitation through the electromagnetic attraction between the track and the electromagnet on the bottom of the train
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