Contactless Fault Diagnosis for Railway Point Machines Based on Multi-Scale Fractional Wavelet Packet Energy Entropy and Synchronous Optimization Strategy

Sun, Yongkui; Cao, Yuan; Li, Peng

doi:10.1109/tvt.2022.3158436

Cited by 76 publications

(33 citation statements)

References 41 publications

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“…The decomposed signal only changes in form, while the total energy entropy of the signal remains unchanged, which is a measure of the unknown degree of the signal, and represents the number of possible states in the signal and the occurrence probability of these states. 61 The wavelet packet energy entropy is to use the wavelet packet normalized energy feature as the probability distribution of vibration signals to extract the vibration signal features. Therefore, the wavelet packet algorithm can not only effectively reduce the number of feature vectors but also make the defect identification model faster calculation and higher accuracy.…”

Section: Resultsmentioning

confidence: 99%

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Xin¹,

Xu²,

Gong³

et al. 2022

iScience

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Section: Resultsmentioning

confidence: 99%

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Xin¹,

Xu²,

Gong³

et al. 2022

iScience

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“…In the future work, the exploration will be conducted on the parameter estimation of the time-varying systems without the empirical values for the invariant matrix. The proposed parameter identification algorithms in this paper can unite other identification algorithms [84][85][86][87][88][89][90] to investigate new parameter estimation methods of other linear and nonlinear stochastic systems with colored noises and can applied to the control and schedule areas [91][92][93][94][95][96][97] such as the transportation communication systems and infomation processing systems [98][99][100][101][102][103] and so on.…”

Section: Discussionmentioning

confidence: 99%

Parameter estimation for a class of time‐varying systems with the invariant matrix

Ding

2022

Intl J Robust & Nonlinear

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This article is concerned with the identification of time-varying systems. Differently from the conventional polynomial approximation approaches, the changing laws of the time-varying parameters are considered to build the identification model for the time-varying systems. Specifically, the concept of the invariant matrix is put forward to characterize the time-varying parameters and to establish the state-space model with regard to the system parameters. Then this article proposes a stacked state estimation algorithm to achieve the time-varying parameter estimation. Moreover, for the purpose of enhancing the computational efficiency, a detached state estimation algorithm is proposed by reducing the dimension of the state vector to reconstruct the state equation. Finally, a numerical simulation example is employed to demonstrate the effectiveness of the proposed algorithms.

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“…The proposed F-AM-RGEG algorithm in this paper can combine some statistical tools and optimal strategies [74][75][76][77][78][79][80] to study the parameter estimation algorithms of various stochastic systems with disturbances [81][82][83][84][85] and can be applied to literatures [86][87][88][89][90] such as paper-making systems and information processing systems and so on. The flowchart of computing the parameter estimation vector θ(t) by using the F-AM-RGEG algorithm in ( 73)-( 87) is shown in Figure 1 and the procedures are as follows.…”

Section: Filtered Auxiliary Model Recursive Generalized Extended Grad...mentioning

confidence: 99%

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

Ding

Xiao

et al. 2023

Intl J Robust & Nonlinear

100

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For equation-error autoregressive moving average systems, that is, Box-Jenkins systems, this paper presents a filtered auxiliary model generalized extended stochastic gradient identification method, a filtered auxiliary model multi-innovation generalized extended stochastic gradient identification method, a filtered auxiliary model recursive generalized extended gradient identification method, a filtered auxiliary model multi-innovation recursive generalized extended gradient identification method, a filtered auxiliary model recursive generalized extended least squares identification method, and a filtered auxiliary model multi-innovation recursive generalized extended least squares identification method by using the filtering identification idea and the auxiliary model identification idea. The proposed filtered auxiliary model recursive generalized extended identification methods can be generalized to other linear and nonlinear multivariable stochastic systems with colored noises.

show abstract

Contactless Fault Diagnosis for Railway Point Machines Based on Multi-Scale Fractional Wavelet Packet Energy Entropy and Synchronous Optimization Strategy

Cited by 76 publications

References 41 publications

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

Parameter estimation for a class of time‐varying systems with the invariant matrix

Filtered auxiliary model recursive generalized extended parameter estimation methods for Box–Jenkins systems by means of the filtering identification idea

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