scholarly journals The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang ◽  
Nanfei Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
The Difference ◽  
Fault Feature Extraction

Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

scholarly journals GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1946 ◽  
Author(s):  
Jiakai Ding ◽  
Liangpei Huang ◽  
Dongming Xiao ◽  
Xuejun Li
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Optimal Parameter ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Genetic Mutation ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction

The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.

scholarly journals Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Maohua Xiao ◽  
Kai Wen ◽  
Cunyi Zhang ◽  
Xiao Zhao ◽  
Weihua Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction ◽  
Fault Characteristic

Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

scholarly journals The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Yugang Fan ◽  
Xiaodong Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Dominant Frequency ◽  
Working Process ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Common Time ◽  
Local Mean ◽  
Fault Feature Extraction

Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

scholarly journals Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang
Keyword(s):  
Random Forest ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ar Model ◽  
Model Parameters ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Non Gaussian

Targeting the nonstationary and non-Gaussian characteristics of vibration signal from fault rolling bearing, this paper proposes a fault feature extraction method based on variational mode decomposition (VMD) and autoregressive (AR) model parameters. Firstly, VMD is applied to decompose vibration signals and a series of stationary component signals can be obtained. Secondly, AR model is established for each component mode. Thirdly, the parameters and remnant of AR model served as fault characteristic vectors. Finally, a novel random forest (RF) classifier is put forward for pattern recognition in the field of rolling bearing fault diagnosis. The validity and superiority of proposed method are verified by an experimental dataset. Analysis results show that this method based on VMD-AR model can extract fault features accurately and RF classifier has been proved to outperform comparative classifiers.

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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