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.

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.

Research of Rolling Bearing Fault Feature Extraction Based on EMD and Choi-Williams

2013 ◽  
Vol 694-697 ◽  
pp. 1377-1381
Author(s):  
Xing Chun Wei ◽  
Yu Lin Tang ◽  
Tao Chen
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Outer Ring ◽  
Intrinsic Mode Functions ◽  
Linear Superposition ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Bearing Vibration

Aiming at rolling bearing fault signal of the non stationary feature, Apply a new method to the rolling bearing vibration signal of feature extraction, which is combined the Empirical Mode Decomposition (EMD) and the Choi-Williams distribution. Firstly, original signals were decomposed into a series of intrinsic mode functions (IMF) of different scales. To the decomposed each IMF component for Choi-Williams time-frequency analysis, Then take the linear superposition, finally obtained the rolling bearing vibration signal of Choi-Williams distribution. After the analyses of the rolling bearing inner ring, outer ring and rolling element fault signal ,the results show that this method can effectively suppress the frequency aliasing and interference caused by cross terms. And be able to accurately extract the fault frequency of the bearing inner ring, outer ring and rolling element, lay the foundation for the subsequent rolling bearing state recognition.

Time-Frequency Spectral Power Assessment of Rolling Element Bearing Faults Using Adaptive Modified Morlet Wavelet Transform

2020 ◽  
Vol 13 ◽  
Author(s):  
O. P. Yadav ◽  
G.L. Pahuja
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Bearing Defects ◽  
S Transform ◽  
Rolling Element ◽  
Element Bearing ◽  
Morlet Wavelet Transform

Objectives: The main objectives of this manuscript are to investigate and diagnose rolling element bearing defects in its inception time. Methods: Vibration signal generated by induction motor contains series of frequency components that have rich and viable information about bearing health conditions. Recently, maximum energy concentration (MEC) measure of time-frequency spectrum has been employed to investigate the small variations in low frequency biomedical signal spectrum. In this paper, the above technique has been modified and applied to study the bearing defects of induction motor using vibration signal and it is termed as adaptive modified Morlet wavelet (AMMW) transform. Initially, this proposed method was validated on two medium frequency synthetic time series signals in terms of MEC measurement at different signal to noise ratio (SNR). Results: The simulated results have depicted that AMMW method provides excellent time-frequency localization capability over other time-frequency methods like Morlet wavelet transform, modified Morlet wavelet transform, adaptive S-transform and adaptive modified S-transform. Then this method has been applied on standard database of vibration signal to determine of interquartile power for fault detection purpose and also fault index parameter termed as has been analyzed to detect small variation in vibration signals.

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.

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

scholarly journals Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time-Varying Speed Conditions

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Guang-Quan Hou ◽  
Chang-Myung Lee
Keyword(s):  
Research Work ◽  
Defect Size ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Time Varying ◽  
Entry And Exit ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

Fault diagnosis and failure prognostics for rolling element bearing are helpful for preventing equipment failure and predicting the remaining useful life (RUL) to avoid catastrophic failure. Spall size is an important fault feature for RUL prediction, and most research work has focused on estimating the fault size under constant speed conditions. However, estimation of the defect width under time-varying speed conditions is still a challenge. In this paper, a method is proposed to solve this problem. To enhance the entry and exit events, the edited cepstrum is used to remove the determined components. The preprocessed signal is resampled from the time domain to the angular domain to eliminate the effect of speed variation and measure the defect size of a rolling element bearing on outer race. Next, the transient impulse components are extracted by local mean decomposition. The entry and exit points when the roller passes over the defect width on the outer race were identified by further processing the extracted signal with time-frequency analysis based on the continuous wavelet transform. The defect size can be calculated with the angle duration, which is measured from the identified entry and exit points. The proposed method was validated experimentally.

Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

2001 ◽  
Vol 123 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Peter W. Tse ◽  
Y. H. Peng ◽  
Richard Yam
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Element Bearing ◽  
Frequency Spectra ◽  
Bearing Faults ◽  
Time Frequency ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Characteristic

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

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