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Yao Cheng

Bio: Yao Cheng is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Deconvolution & Fault (geology). The author has an hindex of 10, co-authored 23 publications receiving 324 citations.

Papers
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Journal ArticleDOI
TL;DR: Comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.
Abstract: A novel time–frequency analysis method called complementary complete ensemble empirical mode decomposition (EEMD) with adaptive noise (CCEEMDAN) is proposed to analyze nonstationary vibration signals. CCEEMDAN combines the advantages of improved EEMD with adaptive noise and complementary EEMD, and it improves decomposition performance by reducing reconstruction error and mitigating the effect of mode mixing. However, because white noise mixed in with the raw vibration signal covers the whole frequency bandwidth, each mode inevitably contains some mode noise, which can easily inundate the fault-related information. This paper proposes a time–frequency analysis method based on CCEEMDAN and minimum entropy deconvolution (MED) for fault detection of rolling element bearings. First, a raw signal is decomposed into a series of intrinsic mode functions (IMFs) by using the CCEEMDAN method. Then a sensitive parameter (SP) based on adjusted kurtosis and Pearson’s correlation coefficient is applied to select a sensitive mode that contains the most fault-related information. Finally, the MED is applied to enhance the fault-related impulses in the selected IMF. The fault signals of high-speed train axle-box bearing are applied to verify the effectiveness of the proposed method. Results show that the proposed method can effectively reveal axle-bearing defects’ fault information. The comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN. Further, we conducted comparisons that highlight the superiority of our proposed method over individual CCEEMDAN and MED methods and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.

102 citations

Journal ArticleDOI
TL;DR: The proposed improved deconvolution method for the fault detection of rolling element bearings solves the filter coefficients by the standard particle swarm optimization algorithm, assisted by a generalized spherical coordinate transformation.

87 citations

Journal ArticleDOI
TL;DR: The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconVolution methods.
Abstract: Extraction of the fault related impulses from the raw vibration signal is important for rolling element bearing fault diagnosis. Deconvolution techniques, such as minimum entropy deconvolution (MED), MED adjusted (MEDA) and maximum correlated kurtosis deconvolution (MCKD), optimal MED adjusted (OMEDA) and multipoint optimal MED adjusted (MOMEDA), are typical techniques for enhancing the impulse-like component in the fault signal. This paper introduces the particle swarm optimization (PSO) algorithm to solve the filter of deconvolution problem. The proposed approaches solve the filter coefficients of the deconvolution problems by the PSO algorithm, assisted by a generalized spherical coordinate transformation. Compared with MED, MEDA, and OMEDA, the proposed PSO-MED and PSO-OMEDA can effectively overcome the influence of large random impulses and tend to deconvolve a series of periodic impulses rather than a signal impulse. Compared with MCKD and MOMEDA, the proposed PSO-MCKD and PSO-MOMEDA can achieve good performances even when the fault period is inaccurate. The effectiveness of the proposed methods is validated by the simulated signals. The study of experimental bearing fault signal shows that the PSO based deconvolution methods delivered better performance for rolling element bearing fault detection than the traditional deconvolution methods. Additionally, the proposed methods are compared with the following two popular signal processing methods: the ensemble empirical mode decomposition (EEMD) and fast kurtogram, which are used to highlight the improved performance of the proposed methods.

74 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a comprehensive three-dimensional vehicle-track coupled dynamics model considering the traction drive system and axle box bearing, and developed a model that considers the dynamic interactions between the two components.
Abstract: In this study, we developed a comprehensive three-dimensional vehicle–track coupled dynamics model considering the traction drive system and axle box bearing. In this model, dynamic interactions be...

54 citations

Journal ArticleDOI
TL;DR: A new criterion called impulse-norm is proposed, a time-domain parameter defined as the ratio of the average amplitude of the first several maximum energy points to the energy of the entire signal, which can effectively identify the weak impulse fault feature of rolling element bearings.

52 citations


Cited by
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Journal ArticleDOI
TL;DR: A comprehensive review on the fault detection and diagnosis techniques for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years.
Abstract: High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. The first objective of this paper is to present a comprehensive review on the fault detection and diagnosis (FDD) techniques for high-speed trains. The second purpose of this work is, motivated by the pros and cons of the FDD methods for high-speed trains, to provide researchers and practitioners with informative guidance. Then, the application of FDD for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years. Finally, the challenges and promising issues are speculated for the future investigation.

239 citations

01 Mar 2005
TL;DR: In this paper, a new time-frequency technique, known as basis pursuit, was developed for detecting inner race and outer race faults in a rolling bearing with inner and outer races.
Abstract: The task of condition monitoring and fault diagnosis of rolling element bearing is often cumbersome and labour intensive. Various techniques have been proposed for rolling bearing fault detection and diagnosis. The challenge however, is to efficiently and accurately extract features from signals acquired from these elements, particularly in the time–frequency domain. A new time–frequency technique, known as basis pursuit, was recently developed. This paper presents an application of this new basis pursuit method in the extraction of features from signals collected from faulty rolling bearings with inner race and outer race faults. Results obtained using this new technique were compared with discrete wavelet packet analysis (DWPA) and the matching pursuit technique. Basis pursuit represents features with very fine resolution and sparsity in the time–frequency domain thus rendering easier interpretation of the analysed results. The technique also improves the signal to noise ratio so that subsequent fault detection and identification can be conducted with confidence.

165 citations

Journal ArticleDOI
TL;DR: The statistical analysis, stochastic analysis and frequency analysis are performed to make sense of the effect of the random track irregularities on the pantograph-catenary interaction, and the reliability of the pantographs shows a continuous decrease in the degradation of rail quality.

120 citations

Journal ArticleDOI
TL;DR: The results show that the proposed CEEMDAN method achieves a better performance in terms of SNR improvement and fault feature detection, it can successfully detect the fault features in the presence of Gaussian and non-Gaussian noises.

118 citations

Journal ArticleDOI
TL;DR: An adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed, aiming at the determination of cyclic frequency set estimation method based on autocorrelation function of morphological envelope and the validity of the method is verified.

107 citations