Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM

Chen, Wuge; Li, Junning; Wang, Qian; Han, Ka

doi:10.1016/j.measurement.2020.108901

Cited by 103 publications

(52 citation statements)

References 41 publications

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“…There are two steps in bearings diagnosis process. The first step is de-noising, which include narrowband filter, wavelet and EMD [9]. The second step is to extract the fault characteristic frequency.…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction of rolling element bearings based on short-time processing

Chen¹

2022

J. vibroeng.

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Fault diagnosis of bearings is a crucial part of the maintenance process of the rotary machinery. Extracting the cyclic characteristics of the impact force is of significant importance for the bearing diagnosis. To highlight the fault features from signals combined with heavy background noise, a novel approach for bearing fault diagnosis based on the short-time processing is proposed. Fault signals are regarded as periodic impulse response signals. Firstly, a vibration signal is band-pass filtered with a subsequent spectral analysis. Then we integrate the energy of the filtered signal with a constant length, and the natural logarithm is considered to obtain the energy curve. The energy curve is a straight decaying curve, and its spectral energy is more concentrated on the fault characteristic frequency compared with envelope. Finally, the fault characteristic frequency of the bearing is found by the spectral analysis of the energy curve. The effectiveness of the proposed method is verified by simulation and experiments. The harmonics and sidebands in logarithmic energy spectrum are suppressed well, and the fault characteristic frequency is highlighted. Comparison of the proposed method with Hilbert envelope method shows that the proposed method can highlight the fault characteristic frequency.

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Section: Introductionmentioning

confidence: 99%

Fault feature extraction of rolling element bearings based on short-time processing

Chen¹

2022

J. vibroeng.

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“…The key component of fault diagnosis research is to extract useful information from the data collected by sensors, and then the classifier is applied to obtain the diagnosis result [3], [4]. Traditional fault diagnosis methods on the basis of signal processing and expert knowledge have been applied many years ago [5], and with the development of artificial intelligence, much more intelligent algorithms have been frequently used to build fault diagnosis models in recent years.…”

Section: Introductionmentioning

confidence: 99%

Machinery New Emerge Fault Diagnosis Based on Deep Convolution Variational Autoencoder and Adaptive Label Propagation

She

Wang

2022

IEEE Access

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In the research field of mechanical equipment fault diagnosis, usually only the existing fault types are identified, and the new emerge class of the fault is ignored, however, the new emerge fault class may also occur actually. In order to solve the problem, a novel fault diagnosis model based on deep convolution variational autoencoder network and adaptive label propagation (DCVAN-ALP) is proposed. Firstly, the initial high dimensional features are constructed by using the double tree complex wavelet packet method as the input of the model. Secondly, the convolutional neural network architecture is applied to construct the variational autoencoder, and the local and non-local characteristics of samples are embedded into the loss function for training, which is considered to improve the identification of hidden layer features of the neural network. Finally, t-SNE and the improved label propagation algorithm are adopted to process the hidden features of the neural network, which can achieve the purpose of diagnosing the existing fault class and especially new emerge fault class. Experimental results show that the proposed model can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers in diagnosing new emerge fault class.

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“…Li mean square adaptive filter [17]. Chen et al proposed particle swarm optimization least squares support vector machine [18] . Babouri et al proposed a hybrid method based on adaptive noise fully integrated empirical mode decomposition, optimized wavelet multiresolution analysis, and Hilbert transform [19].…”

Section: Introductionmentioning

confidence: 99%

Research on Wheel-Rail Local Impact Identification Based on Axle Box Acceleration

Zeng

Sun

2022

Shock and Vibration

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Abnormal vibration signals of tramcar are mostly nonstationary and nonlinear signals. This study applied the Hilbert–Huang transform (HHT) to analyze abnormal vibration of the tramcar, aiming to overcome the limitations of some traditional time-frequency analysis methods, such as Fourier transform, in dealing with nonstationary and nonlinear signals. Additionally, to address mode aliasing produced during empirical mode decomposition (EMD) used in classical HHT, this study proposed to first use complete EMD with adaptive noise for the decomposition of original vibration data, then eliminate the trend-term components with the calculated correlation coefficients, and finally perform denoising on high-frequency noisy components using the wavelet threshold method. After weighted reconstruction using denoised high-frequency components and low-frequency information components, data processing was finally optimized via HHT. Taking a tramcar as an example, the Hilbert spectra of the vertical acceleration of axle box were plotted via HHT, and the time-instantaneous, frequency-instantaneous energy 3D relations were obtained for the location of impact points. Further, the vibration characteristics were analyzed and quality indexes were calculated in combination with the marginal spectra so as to judge the reasons for abnormal vibration and failure modes of the tramcar. The results revealed that the proposed method was feasible and effective in vibration feature extraction and vibration impact analysis for tramcars.

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Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM

Cited by 103 publications

References 41 publications

Fault feature extraction of rolling element bearings based on short-time processing

Fault feature extraction of rolling element bearings based on short-time processing

Machinery New Emerge Fault Diagnosis Based on Deep Convolution Variational Autoencoder and Adaptive Label Propagation

Research on Wheel-Rail Local Impact Identification Based on Axle Box Acceleration

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