Simulation Research on Rolling Element Bearing Feature Extraction Based on Recursive Least-Squares Lattice-Ladder Algorithms

2014 ◽  
Vol 548-549 ◽  
pp. 481-486
Author(s):  
Jie Ping Zhu ◽  
Yong Xiang Zhang ◽  
Shuai Zhang ◽  
Xiao Lin Wang
Keyword(s):  
Feature Extraction ◽  
Least Squares ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Recursive Least Squares ◽  
Filter Method ◽  
Feature Extraction Method ◽  
Rolling Element ◽  
Element Bearing

In order to extract the weak fault information from complicated vibration signal of rolling element bearing, the Recursive Least-Squares (RLS) Lattice-Ladder Algorithms is introduced into the field of rolling bearing feature extraction. An adaptive feature extraction method is proposed. The RLS Lattice-Ladder algorithms and its adaptive filter property in the process of feature extraction were discussed. The rolling bearing vibration signal was refined by the RLS Lattice-Ladder filter method, and the refined vibration signal was demodulated by square envelope, then the rolling bearing’s characteristic fault frequency was identified by enveloped normalized amplitude-frequency spectrum. Simulation results show that compared with the LMS filter method, this method can identify fault frequency more quickly and more effectively.

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.

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.

scholarly journals Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Dezun Zhao ◽  
Jianyong Li ◽  
Weidong Cheng
Keyword(s):  
Feature Extraction ◽  
Rotational Speed ◽  
Vibration Signal ◽  
Rotational Frequency ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Auxiliary Equipment ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

In the field of rolling element bearing fault diagnosis, variable rotational speed and gear noise are main obstacles. Even though some effective algorithms have been proposed to solve the problems, their process is complicated and they may not work well without auxiliary equipment. So we proposed a method of faulty bearing feature extraction based on Instantaneous Dominant Meshing Multiply (IDMM) and Empirical Mode Decomposition (EMD). The new method mainly consists of three parts. Firstly, IDMM is extracted from time-frequency representation of original signal by peak searching algorithm, which can be used to substitute the bearing rotational frequency. Secondly, resampled signal is obtained by an IDMM-based resampling algorithm; then it is decomposed into a number of Intrinsic Mode Functions (IMFs) based on the EMD algorithm. Calculate kurtosis values of IMFs and an appropriate IMF with biggest kurtosis value is selected. Thirdly, the selected IMF is analyzed with envelope demodulation method which can describe the fault type of bearing. The effectiveness of the proposed method has been demonstrated by both simulated and experimental mixed signals which contain bearing and gear vibration signal.

Fault Feature Extraction Method of the Rolling Element Bearing Based on Reverse Order Tracking

2016 ◽  
Vol 693 ◽  
pp. 1361-1370
Author(s):  
De Zun Zhao ◽  
Wei Dong Cheng ◽  
Wei Gang Wen ◽  
Yang Liu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Rolling Element Bearing ◽  
Reverse Order ◽  
Time Signal ◽  
Feature Extraction Method ◽  
Order Tracking ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Feature Extraction

When dealing with the vibration analysis of the rolling element bearing under gear noise and time-varying speed condition, order tracking is always utilized to convert the time signal to angular domain. In this way, the smearing effect in the spectrum is avoided and the noise cancellation methods based on the periodicity of the gear signal can be reapplied. In this paper, the resonance frequency variation of the resampled signal is analyzed and its influence on the kurtogram algorithm based bandpass filtering procedure is studied through a simulation experiment and a fault feature extraction method of the rolling bearing based on reverse order tracking is proposed. Effectiveness of the proposed method is verified through the analysis of the signal measured from the test-rig.

Feature Extraction of Faulty Rolling Element Bearing Based on Time Synchronous Average and Cepstrum Edit

2014 ◽  
Vol 889-890 ◽  
pp. 666-670
Author(s):  
Zong Tao Li ◽  
Yan Gao ◽  
Xiang Zhou ◽  
Yu Guo
Keyword(s):  
Feature Extraction ◽  
Phase Spectrum ◽  
Rolling Element Bearing ◽  
The Real ◽  
Discrete Frequency ◽  
Rolling Element ◽  
Frequency Components ◽  
Element Bearing ◽  
Inverse Procedure

The cepstrum edit scheme for the vibration feature extraction of the faulty rolling element bearing (REB) is studied in this paper. By combined the time synchronous average (TSA) and the real cepstrum to localize and edit the cepstral lines of the original vibration, the unwanted discrete frequency components can be removed. Then, a corresponding inverse procedure is designed, in which the edited cepstrum and the original phase spectrum are employed to reconstruct the edited vibration for the REB feature extraction. Simulation verified the scheme positively.

Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect

2011 ◽  
Vol 291-294 ◽  
pp. 1469-1473
Author(s):  
Wei Ke ◽  
Yong Xiang Zhang ◽  
Lin Li
Keyword(s):  
Point Defect ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal ◽  
Inner Race ◽  
Cyclic Spectrum

Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

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
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