Weak fault feature extraction of rolling element bearing based on variational mode extraction and multi-objective information fusion band-pass filter

Aiming at the problem that traditional demodulated resonance technology has the deficiency of difficulty to choose the parameters of band-pass filter, Kalman filter technology and fast spectral kurtosis were combined for fault feature extraction of rolling bearing. AR model was firstly built with gearbox original vibration signals, and then model order was ascertained with AIC formula, and finally model parameters were calculated with least-squares method. The original signals were pretreated by Kalman filter. Fast spectral kurtosis (FSK) was used to choose parameters of the best band-pass filter, and finally fault diagnosis was achieved by the energy operator demodulation spectrum analysis of band-pass filtered signal. The analysis result of engineering signals indicated that fault feature extraction method based on Kalman filter and fast spectral kurtosis can primely provide a new feature extraction method for rolling bearing’s week fault.

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

Journal of Mechanical Engineering ◽

10.3901/jme.2018.17.208 ◽

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

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

Journal of Vibration and Control ◽

10.1177/10775463211050726 ◽

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.

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Rolling element bearing fault feature extraction using an optimal chirplet

Measurement Science and Technology ◽

10.1088/1361-6501/aad8e8 ◽

2018 ◽

Vol 29 (10) ◽

pp. 105004

Author(s):

Hongkai Jiang ◽

Ying Lin ◽

Zhiyong Meng

Keyword(s):

Feature Extraction ◽

Rolling Element Bearing ◽

Bearing Fault ◽

Rolling Element ◽

Element Bearing ◽

Fault Feature Extraction

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

Energies ◽

10.3390/en14041079 ◽

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.

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