The Experts below are selected from a list of 23817 Experts worldwide ranked by ideXlab platform

Tianyang Wang - One of the best experts on this subject based on the ideXlab platform.

  • rolling element bearing fault diagnosis via fault characteristic order fco analysis
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Tianyang Wang, Ming Liang, Jianyong Li, Weidong Cheng
    Abstract:

    Abstract Order tracking based on time–frequency representation (TFR) is one of the most effective methods for gear fault detection under time-varying rotational speed without using a tachometer. However, for a rolling element bearing, the Signal components related to rotational speed usually cannot be directly extracted from the TFR. As such, we propose a new method to solve this problem. This method consists of four main steps: (a) Signal filtering via fast spectral kurtosis (SK) analysis – this together with the short time Fourier transform (STFT) leads to a TFR of the Filtered Signal with clear fault-revealing trend lines, (b) extraction of instantaneous fault characteristic frequency (IFCF) from the TFR using an amplitude-sum based spectral peak search algorithm, (c) Signal resampling based on the extracted IFCF to convert the non-stationary time-domain Signal into the stationary fault phase angle (FPA) domain Signal, and (d) transform of the FPA domain Signal into the domain of the fault characteristic order (FCO) and identification of fault type from the FCO spectrum. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration Signals.

Guangming Dong - One of the best experts on this subject based on the ideXlab platform.

  • the weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum
    Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2013
    Co-Authors: Ruilong Jiang, Jin Chen, Guangming Dong, Wenbing Xiao
    Abstract:

    In vibration analysis, weak fault detect and diagnosis is of great importance. A method based on minimum entropy deconvolution and envelop spectrum analysis is proposed in this article. Minimum entropy deconvolution technique searches for an optimal set of filter coefficients to enhance the impulse making the Filtered Signal to contain clearer fault information. When there is a fault in bearing, it can be obviously reflected by the power spectral density estimation of the Filtered Signal even if the power spectral density estimation of original Signal denies the information. The Filtered Signals are then analyzed by performing envelop spectrum analysis, where the bearing characteristic frequencies are quite clear for further diagnosis. The feasibility and validity of utilizing the minimum entropy deconvolution in weak fault diagnosis and condition monitoring is demonstrated by both simulation and experiments. The analysis of actual data from accelerated life test of rolling bearing shows that it can detec...

  • weak fault feature extraction of rolling bearing based on cyclic wiener filter and envelope spectrum
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Yang Ming, Jin Chen, Guangming Dong
    Abstract:

    In vibration analysis, weak fault feature extraction under strong background noise is of great importance. A method based on cyclic Wiener filter and envelope spectrum analysis is proposed. Cyclic Wiener filter exploits the spectral coherence theory induced by the second-order cyclostationary Signal. The original Signal is duplicated and shifted in the frequency domain by amounts corresponding to the cyclic frequencies. The noise component is optimally Filtered by a filter-bank. The Filtered Signal is analyzed by performing envelope spectrum. In the envelope spectrum, characteristic frequencies are quite clear. Then the most impactive part is effectively extracted for further fault diagnosis. The effectiveness of the method is demonstrated on both simulated Signal and actual data from rolling bearing accelerated life test.

Weidong Cheng - One of the best experts on this subject based on the ideXlab platform.

  • rolling element bearing fault diagnosis via fault characteristic order fco analysis
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Tianyang Wang, Ming Liang, Jianyong Li, Weidong Cheng
    Abstract:

    Abstract Order tracking based on time–frequency representation (TFR) is one of the most effective methods for gear fault detection under time-varying rotational speed without using a tachometer. However, for a rolling element bearing, the Signal components related to rotational speed usually cannot be directly extracted from the TFR. As such, we propose a new method to solve this problem. This method consists of four main steps: (a) Signal filtering via fast spectral kurtosis (SK) analysis – this together with the short time Fourier transform (STFT) leads to a TFR of the Filtered Signal with clear fault-revealing trend lines, (b) extraction of instantaneous fault characteristic frequency (IFCF) from the TFR using an amplitude-sum based spectral peak search algorithm, (c) Signal resampling based on the extracted IFCF to convert the non-stationary time-domain Signal into the stationary fault phase angle (FPA) domain Signal, and (d) transform of the FPA domain Signal into the domain of the fault characteristic order (FCO) and identification of fault type from the FCO spectrum. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration Signals.

Xiaojuan Wang - One of the best experts on this subject based on the ideXlab platform.

  • a joint sparse wavelet coefficient extraction and adaptive noise reduction method in recovery of weak bearing fault features from a multi component Signal mixture
    Applied Soft Computing, 2013
    Co-Authors: Dong Wang, Wei Guo, Xiaojuan Wang
    Abstract:

    Rolling element bearings are widely used to support rotating components of a machine. Due to close space locations of components in the machine, a vibration Signal caused by bearing localized defects is easily overwhelmed by other strong vibration Signals. Extracting the bearing fault Signal from a multi-component Signal mixture is thus significant to detect early bearing fault features and prevent machine breakdown. In this paper, a bearing fault diagnosis method, named cyclic spike detection method, is proposed to extract the weak bearing fault features from a multi-component Signal mixture. Firstly, the optimal center frequency and bandwidth of a complex Morlet wavelet filter are determined by a simplex-simulated annealing algorithm along with a maximum sparsity objective function. The Filtered Signal is then obtained by applying the optimal wavelet filter to the multi-component Signal mixture. After that, a new adaptive local maximum selection method is proposed to make the Filtered Signal succinct. Only a few spikes are retained to reveal potential cyclic intervals caused by bearing localized defects. Two multi-component Signal mixtures, including a simulated Signal and a real vibration Signal collected from an industrial machine, are used to validate the effectiveness of the proposed cyclic spike detection method. The results demonstrate that the proposed method can extract the weak bearing fault features from other strong masking vibration Signals and noise.

Qing Chen - One of the best experts on this subject based on the ideXlab platform.

  • enhanced frequency band entropy method for fault feature extraction of rolling element bearings
    IEEE Transactions on Industrial Informatics, 2020
    Co-Authors: Tao Liu, Qing Chen
    Abstract:

    Frequency band entropy (FBE) has been proved usable in the fault diagnosis of rolling bearings, but its performance is poor in the presence of non-Gaussian noise and a low Signal-to-noise ratio. In order to extract the transient impulsive Signals more effectively, wavelet packet transform (WPT) is considered as an alternative method for Signal decomposition. Therefore, by introducing WPT into FBE, this article introduces an enhanced FBE (EFBE) adopting WPT as the filter of FBE to overcome the shortcomings of the original FBE. Then, the depth of EFBE is optimized using adaptive resonance bandwidth and power amplitude spectrum entropy (PASE). Third, a novel method based on the indicator PASE is introduced to select the optimal node of EFBE. Finally, the Filtered Signal is combined with the envelope power spectrum to extract the fault feature frequency. In addition, an evaluation indicator is proposed to evaluate the performance of the EFBE. The simulation and cases are used to demonstrate the effectiveness and improved performance of the EFBE compared with the original FBE and other typical methods. The results show that the EFBE can detect various rolling bearing failures and implement its fault diagnosis effectively.