Suspension system status detection of maglev train based on machine learning using levitation sensors

Wang, Lianchun; Yu, Pei; Li, Jinhui; Zhou, Danfeng; Li, Jie

doi:10.1109/ccdc.2017.7978559

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Experiments have indicated that the malfunction of the suspension control system easily occurs when the linearized model is subjected to interference [11]. In addition, malfunction of the suspension control system can be caused by the failure of its components such as the abnormal of suspension sensors (including gap, current, and acceleration sensors), the short circuit failure, the electromagnet with a less effective number turn or higher temperature, the earth leakage failure of electromagnet, and the failure of power supply [12][13][14]. The malfunctioning suspension control system can result in a deviation of the suspension gap from the equilibrium point and a sudden change or loss of electromagnetic force.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [12] proposed a fault detection method for a suspension control system by using the suspension gap data. Wang et al [13] designed a method based on the singles from gap sensors to detect the abnormal status of the suspension control system. Hou et al [17] used the suspension gap signal for the fault detection of accelerometers in the suspension control system.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Malfunction Detection of Maglev Suspension Controllers

Wang,

et al. 2023

Mathematics

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This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data acquisition unit. The software module of the system consists of codes for three functions: (i) the identification of time intervals in relation to the passage of each suspension controller via synchrosqueezing transform; (ii) the extraction of a feature index (FI) sequence synthesized by modulating the response amplitude, frequency, and running speed; and (iii) the formulation of a Bayesian dynamic linear model for real-time malfunction detection in maglev suspension controllers. For verification of the proposed monitoring system and malfunction detection algorithm, full-scale tests have been conducted on an maglev test line using the devised system, where a maglev train was run at different speeds with malfunction occurring in the suspension controllers. The malfunction detection results of the proposed approach are exemplified via comparison with the recorded suspension gaps after the trial run of the maglev train. The fidelity of the results obtained using the extracted FI sequence and using the raw monitoring data are compared. The superiority of the proposed malfunction detection algorithm is also discussed via comparison with the results of the different train speeds.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Malfunction Detection of Maglev Suspension Controllers

Wang,

et al. 2023

Mathematics

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“…In 2017, he proposed a temperature compensation model of EMS vehicle gap sensor based on a radial basis function (RBF) neural network [30]. Similarly, using levitation sensors, the suspension system status detection of maglev could also be achieved [31]. Then, Sun et al have put forward an adaptive neural-fuzzy robust position control scheme for maglev systems and verified it with experiments [32].…”

Section: Introductionmentioning

confidence: 99%

Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms

Liu

Shi

et al. 2022

Supercond. Sci. Technol.

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By the merits of self-stability and low energy consumption, high temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes 5 different neural network models (RBF, DNN, CNN, RNN, LSTM) to predict HTS maglev guiding force, and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptively iterative algorithms for parameters matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. And according to the results, it is revealed that, the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90000, respectively. Moreover, the K-fold cross validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.

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“…Among the available options, the magnetic levitation (maglev) train is one of the best choices that has many advantages over the classical trains: 1) the fastest ground transportation system; 2) lower power consumption; 3) less noise and vibration; 4) more safety and more convenient; 5) better performance of acceleration and deceleration and also, better movement on a slope; 6) reduction in maintenance costs; 7) elimination of gear, coupling, axles, and bearings; 8) less impressionability to weather conditions; 9) environmentally friendly. So far, much research has been done on these trains' technologies, such as modeling and analyzing linear electric machines, superconducting magnets, and permanent magnets [1][2][3]. Three forces are required to achieve the desired performance in a maglev train: 1) levitation force that lifts the train; 2) guidance force that prevents train derailing; 3) propulsion force generated by a linear motor to move the train.…”

Section: Introductionmentioning

confidence: 99%

Design, Optimization, and Control of a Linear Tubular Machine Integrated with Levitation and Guidance for Maglev Applications

et al. 2021

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Nowadays, with the increase of population, demand for efficient public transportation systems has increased. Magnetic levitation (maglev) trains are one of the best choices for the future. There are three separated systems in a classical magnetic train to achieve desirable performance. Hence, several control systems and sensors are essential for train operation. Accordingly, the classical maglev trains include complex structures, and they are so expensive. This paper presents the design, optimization, and control of a combined magnetic train structure with the integrated performance of suspension and guidance and a complementary propulsion system. These combined topologies provide a simple design, more convenient movement, and reduce construction and operation costs.

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Suspension system status detection of maglev train based on machine learning using levitation sensors

Cited by 5 publications

References 13 publications

Real-Time Malfunction Detection of Maglev Suspension Controllers

Real-Time Malfunction Detection of Maglev Suspension Controllers

Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms

Design, Optimization, and Control of a Linear Tubular Machine Integrated with Levitation and Guidance for Maglev Applications

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