scholarly journals Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1111-1121 ◽  
Author(s):  
Yan Lu ◽  
Juan Du ◽  
Xian Tao
Keyword(s):  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Signal Decomposition ◽  
Sparse Signal ◽  
Q Factor ◽  
Component Mixture ◽  
Model Decomposition ◽  
Periodic Impulse ◽  
Sparse Signal Decomposition

When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor ( Q-factor) is proposed in this paper. In this method, the optimal Q-factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q-factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal.

scholarly journals Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing

2020 ◽  
Vol 53 (3-4) ◽  
pp. 601-612
Author(s):  
Du Juan ◽  
Lu Yan ◽  
Tao Xian ◽  
Zheng Yu ◽  
Chen Guo Chu
Keyword(s):  
Fault Detection ◽  
Harmonic Component ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Signal Decomposition ◽  
Center Frequency ◽  
Sparse Signal ◽  
Component Mixture ◽  
Periodic Impulse ◽  
Sparse Signal Decomposition

The main purpose of the paper is to propose a new method to achieve separating periodic impulse signal among multi-component mixture signal and its application to the fault detection of rolling bearing. In general, as local defects occur in a rotating machinery, the vibration signal always consists of periodic impulse components along with other components such as harmonic component and noise; impulse component reflects the condition of rolling bearing. However, different components of multi-component mixture signal may approximately have same center frequency and bandwidth coincides with each other that is difficult to disentangle by linear frequency-based filtering. In order to solve this problem, the author introduces a proposed method based on resonance-based sparse signal decomposition integrated with empirical mode decomposition and demodulation that can separate the impulse component from the signal, according to the different Q-factors of impulse component and harmonic component. Simulation and application examples have proved the effectiveness of the method to achieve fault detection of rolling bearing and signal preprocessing.

The Delayed Correlation Envelope Analysis Technique Based on Sparse Signal Decomposition Method and its Application to Bearing early Fault Diagnosis

2013 ◽  
Vol 819 ◽  
pp. 292-296 ◽  
Author(s):  
Dai Yi Mo ◽  
Ling Li Cui ◽  
Jin Wang ◽  
Yong Gang Xu
Keyword(s):  
Decomposition Method ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Residual Energy ◽  
Signal Decomposition ◽  
Sparse Signal ◽  
Analysis Technique ◽  
Weak Fault ◽  
Fault Information ◽  
Sparse Signal Decomposition

In order to extract the early weak fault information submerged in strong background noise of the bearing vibration signal, a delayed correlation envelope technique based on sparse signal decomposition method is proposed. This method can improve the signal to noise and extract the fault information efficiently. For the strong noise problem in the early fault, based on D-value of adjacent residual energy as the termination condition of the iterative method, reducing the noise effectively, and combine it with delayed correlation to enhance the denoising effect. The analysis results of roller bearing experimental data confirm the feasibility and validity of this method.

scholarly journals Research on Rolling Bearing Fault Feature Extraction Based on Singular Value Decomposition considering the Singular Component Accumulative Effect and Teager Energy Operator

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Longlong Li ◽  
Yahui Cui ◽  
Runlin Chen ◽  
Lingping Chen ◽  
Lihua Wang
Keyword(s):  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Teager Energy Operator ◽  
Singular Component ◽  
Value Decomposition ◽  
Teager Energy

The extraction of impulsive signatures from a vibration signal is vital for fault diagnosis of rolling element bearings, which are always whelmed by noise, especially in the early stage of defect development. Aiming at the weak defect diagnosis, kurtosis of Teager energy operator (KTEO) spectrum is employed to indicate the fault information capacity of a spectrum, and considering the accumulative effect of a singular component, accumulative kurtosis of TEO (AKTEO) is firstly proposed to determine the proper signal reconstructed order during vibration signal processing using singular value decomposition (SVD). Then, a vibration processing scheme named SVD-AKTEO is designed where an iteration is employed to reflect an accumulative singular effect by kurtosis of TEO spectrum. Finally, the fault diagnosis results can be extracted from the TEO spectrum output by SVD-AKTEO. Simulation data and real data from a run-to-failure experiment of a rolling bearing are adopted to validate the efficiency, and comparative analysis demonstrates the feasibility to detect the early defect of the rolling bearing.

scholarly journals Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jiaquan Yan ◽  
Haixin Sun ◽  
En Cheng ◽  
Xiaoyan Kuai ◽  
Xiaoliang Zhang
Keyword(s):  
Feature Extraction ◽  
Low Frequency ◽  
Signal Decomposition ◽  
Sparse Signal ◽  
Support Vector ◽  
Analysis Method ◽  
Radiated Noise ◽  
High Resonance ◽  
Residual Component ◽  
Sparse Signal Decomposition

Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.

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