Rolling Element Bearing Incipient Fault Feature Extraction Based on Optimal Wavelet Scales Cyclic Spectrum

2018 ◽  
Vol 54 (17) ◽  
pp. 208
Author(s):  
Rui YANG
Keyword(s):  
Feature Extraction ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Element Bearing ◽  
Incipient Fault ◽  
Fault Feature Extraction ◽  
Cyclic Spectrum

scholarly journals Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method

Measurement ◽  
2019 ◽  
Vol 139 ◽  
pp. 226-235 ◽  
Author(s):  
Junchao Guo ◽  
Dong Zhen ◽  
Haiyang Li ◽  
Zhanqun Shi ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Noise Reduction ◽  
Reduction Method ◽  
Rolling Element Bearing ◽  
Multi Stage ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

Weak feature extraction of rolling element bearing based on self-adaptive blind de-convolution and enhanced envelope spectral

2021 ◽  
pp. 107754632110507
Author(s):  
HongChao Wang ◽  
WenLiao Du ◽  
Haiyi Li ◽  
Zhiwei Li ◽  
Jiale Hu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Enhancement Effect ◽  
Minimum Entropy ◽  
Rolling Element ◽  
Element Bearing ◽  
Repetitive Impact ◽  
The Impact ◽  
Fault Feature Extraction

As the most commonly used support component in engineering, rolling element bearing is also the most prone-to-failure part. The vibration signal of faulty bearing will take on repetitive impact and modulation characteristics, and the two features are often difficult to be extracted by conventional fault feature extraction methods such as envelope spectral. The main reasons are due to the influence of strong background noise, the signal attenuation of the acquisition path, and the early weak failure characteristics. To solve the above problem, a weak fault feature extraction method of rolling element bearing by combing improved minimum entropy de-convolution with enhanced envelope spectral is proposed in the paper. The enhancement effect of improved minimum entropy de-convolution on impact features and the satisfactory extraction effect of EES on repetitive impact and modulation features are utilized comprehensively by the proposed method. Firstly, improved minimum entropy de-convolution is used to filter the vibration signal of faulty bearing to enhance the impact characteristics, and the influence of signal acquisition path on the attenuation of the signal characteristics is also weakened at the same time. Then, enhanced envelope spectral is performed on the filtered signal, and the repetitive impact and modulation characteristics of vibration signal are extracted synchronously. In order to solve the shortcomings of the current commonly used de-convolution methods, an improved minimum entropy de-convolution method based on D-norm is proposed, which can solve the interference caused by random impulsive signals effectively. In addition, compared with the conventional method such as envelope spectral, the enhanced envelope spectral method could extract the repetitive impact and modulation characteristics of the faulty signal simultaneously much more effectively. Effectiveness and superiority of the proposed method are verified through simulation, experiment, and engineering application.

scholarly journals An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1079
Author(s):  
Guoping An ◽  
Qingbin Tong ◽  
Yanan Zhang ◽  
Ruifang Liu ◽  
Weili Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optimization Problem ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Optimal Mode ◽  
Fault Feature Extraction

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

Hybrid fault-feature extraction of rolling element bearing via customized-lifting multi-wavelet packet transform

2013 ◽  
Vol 228 (12) ◽  
pp. 2204-2216 ◽  
Author(s):  
Qiang Liao ◽  
Xunbo Li ◽  
Bo Huang
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Rolling Element Bearing ◽  
Feature Extraction Method ◽  
Rolling Element ◽  
Localized Defects ◽  
Element Bearing ◽  
Diagnostic Framework ◽  
Fault Feature Extraction

The rolling element bearing is one of the most extensively used components in various rotating machinery, and it is therefore critical to develop a suitable online rolling element bearing fault-diagnostic framework to improve a rolling element bearing system’s failure protection during conditional operations. In this paper, a hybrid fault-feature extraction method by detecting localized defects and analyzing vibration signals of rolling element bearings via customized multi-wavelet packet transform is proposed, in which the swarm fish algorithm has been utilized for the minimization of signal residual to determine the adaptive prediction operator. With good properties of concurrent symmetry, orthogonality, short support and high-order vanishing moment, the multiple wavelet functions and scaling functions are defined for the hybrid fault-feature extraction, which match the diverse characteristics of hybrid fault and extract coupling features, and the proposed lifting scheme-based multi-wavelet packet transform is highly effective. Then, the proposed method is validated by rolling element bearing experimental results, which show that this approach can effectively extract the hybrid fault features of inner race and rolling element.

Roller Bearing Fault Feature Extraction Based on Compressive Sensing

2018 ◽  
Author(s):  
Huibin Lin ◽  
Jianmeng Tang ◽  
Chris Mechefske
Keyword(s):  
Feature Extraction ◽  
Compressive Sensing ◽  
Learning Algorithm ◽  
Sampling Rate ◽  
Roller Bearing ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

Compressive sensing (CS) theory allows measurement of sparse signals with a sampling rate far lower than the Nyquist sampling frequency. This could reduce the burden of local storage and remote transmitting. The periodic impacts generated in rolling element bearing local faults are obviously sparse in the time domain. According to this sparse feature, a rolling element bearing fault feature extraction method based on CS theory is proposed in the paper. Utilizing the shift invariant dictionary learning algorithm and the periodic presentation characteristic of local faults of roller bearings, a shift-invariant dictionary of which each atom contains only one impact pattern is constructed to represent the fault impact as sparsely as possible. The limited degree of sparsity is utilized to reconstruct the feature components based on compressive sampling matching pursuit (CoSaMP) method, realizing the diagnosis of the roller bearing impact fault. A simulation was used to analyze the effects of parameters such as sparsity, SNR and compressive rate on the proposed method and prove the effectiveness of the proposed method.

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