Erratum to “Fault Diagnosis of High-Speed Train Bogie Based on the Improved-CEEMDAN and 1-D CNN Algorithms” [2021 Art. no. 3508811]

Huang, Deqing; Li, Shupan; Qin, Na; Zhang, Yuanjie

doi:10.1109/tim.2021.3062104

Cited by 4 publications

(3 citation statements)

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“…Therefore, from the experimental results of the above comparison, the conventional signal analysis methods, such as VMD, CEEMDAN, LMD, and ITD, do not effectively extract the original signal x(t); however, the proposed method is superior. As mentioned previously, some researchers have proposed several intelligent optimization algorithms (i.e., PSO, PIO, and WOA) in [26]- [30]. These methods optimize the performance of VMD, and the premise is to design an appropriate fitness function.…”

Section: B Validation Of the Decomposition Methodsmentioning

confidence: 99%

“…Information entropy is a measure of the uncertainty in the amount of information and indicates the average uncertainty of a signal [26]. In a signal, the probability of occurrence of specific information reflects the amount of information that it contains.…”

Section: Information Entropymentioning

confidence: 99%

“…Jiang et al [25] proposed the CEEMD with adaptive noise (CEEMDAN)-based permutation entropy as a sensitive feature for spiral bevel gear fault identification. An improved CEEMDAD was proposed to diagnose the fault of a high-speed train bogie [26]. CEEMDAN decomposition was performed on the denoised signal to obtain multiple groups of intrinsic mode functions (IMF) [27].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Empirical Variational Mode Decomposition for Early Fault Feature Extraction

2022

IEEE Access

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Early fault features of large-scale and low-speed mechanical equipment with heavy duty are weak and exhibit strong non-stationary characteristics. The adaptive extraction and identification of highly relevant important features from such signals has attracted significant attention. In this study, a novel empirical variational mode decomposition and exact Teager energy operator are proposed to explore valuable information. To highlight the fault impact signal representation, we use the exact energy operator to enhance the weak-impact components in the early fault signal. The proposed binary mechanism effectively distinguishes irrelevant features based on the adaptive decomposition parameter construction strategy. Therefore, interference features are easily removed from similar mixed signals, and the independent mode features are determined. The experimental results of the simulation and collected data are compared with those obtained with existing signal decomposition methods, and the superiority of the proposed method, owing to its better modal distinction and less time consumption, is verified.

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Section: B Validation Of the Decomposition Methodsmentioning

confidence: 99%

Section: Information Entropymentioning

confidence: 99%