Bearing Fault Detection Based on SVD and EMD

2012 ◽  
Vol 184-185 ◽  
pp. 70-74 ◽  
Author(s):  
Yan Long Chen ◽  
Pei Lin Zhang
Keyword(s):  
Singular Value Decomposition ◽  
Spectrum Analysis ◽  
Characteristic Frequency ◽  
Singular Value ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Value Decomposition ◽  
Fault Characteristic

While bearing fault signals are strongly interferenced by noise, diagnosis using EMD directly for bearings fault becomes incorrect. A scheme based on Singular Value Decomposition(SVD) and Empirical Mode Decomposition(EMD) is proposed for solving this problem. Aiming at bearing fault signal characteristics, SVD preprocesses sampled signals to denoise. Then preprocessed signals are analyzed by EMD. Fault characteristic frequency can be obtained by spectrum analysis for Intrinsic Mode Functions(IMFs). This method is useful to detect fault of bearings and a comparison is made between it and EMD. The results show that this scheme can diagnose fault correctly under strong noise.

Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition

2017 ◽  
Vol 46 (12) ◽  
pp. 1201003
Author(s):  
程知 CHENG Zhi ◽  
何枫 HE Feng ◽  
靖旭 JING Xu ◽  
张巳龙 ZHANG Si-long ◽  
侯再红 HOU Zai-hong
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Lidar Signal ◽  
Mode Decomposition ◽  
Value Decomposition

Microseismic denoising assessment by polarization histograms

Geophysics ◽  
2020 ◽  
pp. 1-46
Author(s):  
German I. Brunini ◽  
Juan I. Sabbione ◽  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Singular Value Decomposition ◽  
Quality Factor ◽  
Field Data ◽  
Synthetic Data ◽  
Singular Value ◽  
Mode Decomposition ◽  
Actual Field ◽  
Band Pass ◽  
Value Decomposition ◽  
Better Than

We present a comparison of microseismic data denoising methods based on their effect on the polarization attributes of 3C microseismic signals. The compared denoising methods include the classical band-pass filtering, and three recently proposed denoising techniques: restricted domain hyperbolic Radon transform denoising, singular value decomposition-based reduced-rank filtering, and empirical mode decomposition denoising. In order to draw the comparison, we have denoised 3C synthetic data contaminated with noise extracted from actual field data records, calculated their rectilinearity, azimuth, and dip polarization attributes, and arranged them into histograms. The comparison has been drawn by measuring the distances between the polarization histograms of the clean and denoised data, assuming that one method outperforms another if the aforementioned distance is smaller. This strategy allows to quantify the improvement in the calculated polarization attributes due to the different denoising processes. In addition, we have also calculated the quality factor of the denoised signals, which adds value and robustness to the comparison. Our results have indicated that the method based on singular value decomposition preserves the original polarization attributes better than the other techniques tested in this work. Moreover, it has also retrieved the denoised signal with the highest quality factor. Finally, we have tested the methods with field data and assessed their performance qualitatively on the basis of the insight gained from the numerical tests with synthetic data.

Absorption spectrum analysis based on singular value decomposition for photoisomerization and photodegradation in organic dyes

2015 ◽  
Author(s):  
Yutaka Kawabe ◽  
Toshio Yoshikawa ◽  
Toshifumi Chida ◽  
Kazuhiro Tada ◽  
Masuki Kawamoto ◽  
...  
Keyword(s):  
Absorption Spectrum ◽  
Singular Value Decomposition ◽  
Spectrum Analysis ◽  
Organic Dyes ◽  
Singular Value ◽  
Value Decomposition

Incipient Bearing Fault Feature Extraction Based on Minimum Entropy Deconvolution and K-Singular Value Decomposition

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Guangming Dong ◽  
Jin Chen ◽  
Fagang Zhao
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Machine Learning Techniques ◽  
Minimum Entropy ◽  
Bearing Fault ◽  
Machinery Condition Monitoring ◽  
Learning Techniques ◽  
Fault Features ◽  
Incipient Fault ◽  
Value Decomposition

Machinery condition monitoring and fault diagnosis are essential for early detection of equipment malfunctions or failures, which insure productivity, quality, and safety in the manufacturing process. This paper aims at extracting fault features of rolling element bearings at the incipient fault stage. K-singular value decomposition (K-SVD), one technique for sparse representation of signals, is used for study. In K-SVD, its dictionary is trained from data by machine learning techniques, which allows more flexibility to adapt to variation of real signals than the predefined dictionaries. Analysis on simulated bearing signals and real signals shows that K-SVD can give better bearing fault features than the predefined dictionaries such as wavelet dictionaries. However, during our simulation study, K-SVD was found to have large representation error under heavy noise. To reduce the noise effect, minimum entropy deconvolution (MED) is used as a prefilter. The combination of MED and K-SVD is proposed for incipient bearing fault detection. The method is verified by simulation and experimental study. It is shown that the proposed method can effectively extract the impulsive fault feature of the tested bearing at its incipient fault stage.

scholarly journals Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhiqiang Liao ◽  
Xuewei Song ◽  
Baozhu Jia ◽  
Peng Chen
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Extraction Method ◽  
Singular Values ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Fault Features ◽  
Value Decomposition ◽  
Fault Feature Extraction

Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.

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