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.

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 A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis

Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 212 ◽  
Author(s):  
Bin Ju ◽  
Haijiao Zhang ◽  
Yongbin Liu ◽  
Fang Liu ◽  
Siliang Lu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

scholarly journals A deep feature extraction method for bearing fault diagnosis based on empirical mode decomposition and kernel function

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879825 ◽  
Author(s):  
Fengtao Wang ◽  
Gang Deng ◽  
Chenxi Liu ◽  
Wensheng Su ◽  
Qingkai Han ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Deep Feature ◽  
Deep Feature Extraction

To avoid catastrophic failures in rotating machines, it is of great significance to continuously monitor and diagnose the running state of rolling bearings. In this article, a deep feature extraction method for rolling bearing fault diagnosis based on empirical mode decomposition and kernel function is proposed. First, the vibration signals under different states of rolling bearing are decomposed by empirical mode decomposition. Second, to extract more representative high-level features, the obtained intrinsic mode functions are preprocessed with singular value decomposition to acquire singular value parameters, which are regarded as the inputs of the proposed stacked kernel sparse autoencoder network. The proposed method does not depend on prior knowledge of fault diagnosis and even does not need the signal denoising processing, simplifying the traditional process of feature extraction of rolling bearing fault diagnosis. To validate the superiority of the proposed diagnosis network, experiments and comparisons have been made as well. The achieved results demonstrated that the proposed empirical mode decomposition and stacked kernel sparse autoencoder–based diagnosis method has a superior performance in rolling bearing fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document