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.

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 Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

2019 ◽  
Vol 9 (20) ◽  
pp. 4465 ◽  
Author(s):  
Jiesi Luo ◽  
Shaohui Zhang
Keyword(s):  
Fault Detection ◽  
Singular Value Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Singular Value ◽  
Incipient Fault ◽  
Periodic Impulse ◽  
Value Decomposition ◽  
Impulse Characteristics ◽  
Incipient Fault Detection

The periodic impulse characteristics caused by rolling bearing damage are weak in the incipient failure stage. Thus, these characteristics are always drowned out by background noise and other harmonic interference. A novel approach based on multi-resolution singular value decomposition (MRSVD) is proposed in order to extract the periodic impulse characteristics for incipient fault detection. With the MRSVD method, the vibration signal is first decomposed to obtain a group of approximate signals and detailed signals with different resolutions. The first detail signal is mainly composed of noise and the last approximate signal is mainly composed of harmonic interference. Combined with the kurtosis index, the hidden periodic impulse signal will be extracted from the detail signals (in addition to the first detail signal). Thus, the incipient fault detection of a rolling bearing can be fulfilled according to the envelope demodulation spectrum of the extracted periodic impulse signal. The effectiveness of the proposed method has been demonstrated with both simulation and experimental analyses.

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 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.

Sign in / Sign up

Export Citation Format

Share Document