Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

Zheng, Ziyuan; Dai, Shenghua; Xie, Xuxu

doi:10.1109/access.2020.3025628

Cited by 17 publications

(15 citation statements)

References 20 publications

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“…This algorithm is the simplest way to each bird to find food by searching the surrounding area of the bird closest to the object. PSO algorithm is inspired by this biological feature and used to solve optimization problems [47]- [49]. The PSO algorithm uses position, velocity and fitness value to represent the particle characteristics, and determines the optimal position by tracking individual extremum Pbest and group extremum Gbest.…”

Section: Pso Algorithm Optimization Modelmentioning

confidence: 99%

Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

et al. 2021

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Coal mine safety is crucial to the healthy and sustainable development of the coal industry, and coal mine flood is a major hidden danger of coal mine accidents. Therefore, the processing of coal mine water source data is of great significance to prevent mine water inrush accidents. In this experiment, the laser induced fluorescence technology was used to obtain the data information of 7 water sources with the assistance of laser. The laser emission power was set to 100 mw, and the 405 nm laser was emitted to the measured water body to obtain 210 groups of fluorescence spectral data of experimental water samples. The standard normal variable transformation (SNV) and multiple scattering correction (MSC) of the pretreatment algorithm are used to denoise the data and improve the spectral specificity. Due to the excessive calculation of the initial data, principal component analysis (PCA) was used to model and reduce the dimension of seven water samples, so as to obtain small data and maintain the data characteristics of the original information. In order to identify the water inrush type of coal mine water source, the sparrow search algorithm (SSA) is used to optimize the BP neural network in this study. This is because the SSA algorithm has the advantages of strong optimization ability and fast convergence rate compared with particle swarm optimization(PSO) and other optimization algorithms. Experiments show that under the premise of SNV pretreatment, the R 2 of SSA-BP model is infinitely close to 1, MRE is 0.0017, RMSE is 0.0001, the R 2 of PSO-BP model is 0.9995, MRE is 0.0026, RMSE is 0.0019, the R 2 of BP model is 0.9983, MRE is 0.0140, RMSE is 0.0075. Therefore, SSA-BP model is more suitable for the classification of coal mine water sources.

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Section: Pso Algorithm Optimization Modelmentioning

confidence: 99%

Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

et al. 2021

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“…The PSO algorithm helps to approach the proximity of the global minima with a low computational effort, whereas the GA helps to avoid local minima and to obtain the global minima accurately [32]. The advantages of the GAPSO algorithm have been used in some other applications [33], [34]. In this work, it is proposed to use the hybrid GAPSO algorithm for the identification of the parameters that describe the nonlinear dynamic behavior of power electronic converters.…”

Section: Hybrid Gapso Algorithmmentioning

confidence: 99%

Nonlinear Stability Analysis of DC-DC Power Electronic Systems by Means of Switching Equivalent Models

et al. 2021

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The proliferation of renewable energy sources is promoting the use of dc-dc power electronic converters in power distribution systems. It is well-known that the interconnection of power electronic converters can be a source of instabilities. Traditionally, impedance-based stability analyses are performed using either the analytical description or the identification of the output impedance of the source converter and the input admittance of the load converter. However, this methodology is restricted by the small-signal conditions, which are usually violated in this kind of systems due to the high variability imposed by the renewable energy sources. In nonlinear systems, the stability analysis usually consists on the definition of the region of attraction around a stable equilibrium point. In the literature, the analysis of nonlinear systems is almost exclusively applied to analytical systems, where all the details are known. This paper proposes a black-box methodology to obtain the region of attraction of the equilibrium point of commercial off-theshelf dc-dc converters working in power distribution systems. First, optimization algorithms are used to identify the parameters of a predefined structure, such that it is able to reproduce the dynamic behavior of the system. The parameters identified can be used to create a switching equivalent model that accounts for the nonlinearities produced by the switching process of the converters. Second, the bisection method is implemented to minimize the number of simulations needed to determine the region of stability accurately. The proposed methodology has been validated both with simulations and with an experimental setup.

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“…There are 8 carrier-frequency and 18 low-frequency [23], so there are 144 carrier-frequency and low-frequency configuration schemes. In equation (3), set l as 0, ± 1 and ± 2, the standard amplitudes of carrierfrequency, primary side frequency and secondary side frequency components, A 0 , A ±1 and A ±2 , can be obtained as follows [24]: 4)- (6) show that when the low-frequency is determined, the modulation index m can be determined, and the ratio between the amplitudes of each component is fixed. Taking the ZPW-2000 frequencyshift signal with carrier-frequency, low-frequency, sampling frequency, sampling duration and time-domain amplitude of 1701.4 Hz, 29 Hz, 8000 Hz, 20 s, and 1 V respectively as an example, the Fourier transform of the signal is performed, and the signal spectrum is obtained as shown in figure 2.…”

Section: Introductionmentioning

confidence: 99%

ZPW-2000 frequency-shift signal detection algorithm based on nonlinear least squares method

Ji,

Shan,

et al. 2024

Phys. Scr.

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Aiming at the problems of spectrum leakage, difficulty in guaranteeing demodulation accuracy, and endangering driving safety in signal detection algorithm based on periodogram with limited sampling duration, this paper proposes for the first time a kind of ZPW-20000 frequency-shift signal detection algorithm based on nonlinear least squares, so as to improve the detection performance of ZPW-2000 frequency-shift signal, to ensure the safe operation of trains, and to improve the operational efficiency. The mathematical model of ZPW-2000 frequency-shift signal is established, the Cramér-Rao bound (CRB) for carrier-frequency and low-frequency estimation are derived, and a nonlinear least squares algorithm containing three processes, namely, the coarse estimation, the grid search based on Fast Fourier Transform (FFT), and the exact search, is designed, which is applied to the detection of ZPW-2000 frequency-shift signal. The research results show that the accuracy of carrier-frequency and low-frequency estimation of the proposed algorithm is better than that of the periodogram method for different sampling durations, and the number of decoding errors of the proposed algorithm and the periodogram method is reduced to zero when the sampling durations are greater than or equal to 0.11 s and 0.19 s, respectively, and the proposed algorithm reduces the limitation on the sampling durations. As the signal-to-noise ratio (SNR) increases, the root mean square error (RMSE) of carrier-frequency and low-frequency estimation of the proposed algorithm can approach the CRB. when the SNR of −2 dB, the number of decoding errors of the proposed algorithm is reduced to zero, while the periodogram method requires the SNR of 2 dB to achieve the same effect. The proposed algorithm performs well in detection accuracy, real-time and anti-interference ability, which is of great significance for ensuring driving safety and improving the reliability of the vehicle-ground communication mode based on ZPW-2000 track circuit.

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Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

Cited by 17 publications

References 20 publications

Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

Nonlinear Stability Analysis of DC-DC Power Electronic Systems by Means of Switching Equivalent Models

ZPW-2000 frequency-shift signal detection algorithm based on nonlinear least squares method

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