scholarly journals Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2008
Author(s):  
Bingbing Hu ◽  
Shuai Zhang ◽  
Ming Peng ◽  
Jie Liu ◽  
Shanhui Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Stochastic Resonance ◽  
Periodic Potential ◽  
Experimental Tests ◽  
Potential Well ◽  
Noise Background ◽  
Comparison Results ◽  
Gear Fault ◽  
Weak Fault ◽  
Envelope Signal

The enhancement of the detection of weak signals against a strong noise background is a key problem in local gear fault diagnosis. Because the periodic impact signal generated by local gear damage is often modulated by high-frequency components, fault information is submerged in its envelope signal when demodulating the fault signal. However, the traditional bistable stochastic resonance (BSR) system cannot accurately match the asymmetric characteristics of the envelope signal because of its symmetrical potential well, which weakens the detection performance for weak faults. In order to overcome this problem, a novel method based on underdamped asymmetric periodic potential stochastic resonance (UAPPSR) is proposed to enhance the weak feature extraction of the local gear damage. The main advantage of this method is that it can better match the characteristics of the envelope signal by using the asymmetry of its potential well in the UAPPSR system and it can effectively enhance the extraction effect of periodic impact signals. Furthermore, the proposed method enjoys a good anti-noise capability and robustness and can strengthen weak fault characteristics under different noise levels. Thirdly, by reasonably adjusting the system parameters of the UAPPSR, the effective detection of input signals with different frequencies can be realized. Numerical simulations and experimental tests are performed on a gear with a local root crack, and the vibration signals are analyzed to validate the effectiveness of the proposed method. The comparison results show that the proposed method possesses a better resonance output effect and is more suitable for weak fault feature extraction under a strong noise background.

scholarly journals Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huaitao Shi ◽  
Yangyang Li ◽  
Peng Zhou ◽  
Shenghao Tong ◽  
Liang Guo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Barrier Height ◽  
Stochastic Resonance ◽  
Working Conditions ◽  
Potential Well ◽  
Rolling Bearings ◽  
Input Signals ◽  
Weak Fault ◽  
Fault Feature Extraction

The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

scholarly journals Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD-AMD

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Dongying Han ◽  
Xiao Su ◽  
Peiming Shi
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Signal Amplitude ◽  
Rolling Bearing ◽  
Signal Frequency ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Weak Fault ◽  
Noisy Background

For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency-shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm’s accuracy, but also effectively detect weak multifrequency signals submerged by noise.

scholarly journals A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

2019 ◽  
Author(s):  
Zhixing Li ◽  
Xiandong Liu ◽  
Tian He ◽  
Yingchun Shan
Keyword(s):  
Stochastic Resonance ◽  
Background Noise ◽  
Ant Colony Algorithm ◽  
Periodic Potential ◽  
Potential Model ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Noise Energy ◽  
Gear Fault ◽  
Potential Structure

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

scholarly journals Feature Extraction Method for Weak Faults Based on Time-Delayed Feedback Mixed Potential Stochastic Resonance

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jiachen Tang ◽  
Boqiang Shi ◽  
Zhixing Li
Keyword(s):  
Feature Extraction ◽  
Stochastic Resonance ◽  
Ant Colony Algorithm ◽  
Delayed Feedback ◽  
Mixed Potential ◽  
Feature Extraction Method ◽  
Delay Term ◽  
Short Memory ◽  
Weak Fault ◽  
Time Delayed Feedback

To extract weak faults under strong noise, a method for feature extraction of weak faults with time-delayed feedback mixed potential stochastic resonance (TFMSR) is proposed. This method not only overcomes the saturation characteristics of classical bistable stochastic resonance (CBSR), but also verifies a new potential function model. Based on this model, considering the short memory characteristics of the CBSR method, a method is proposed that can add historical information to the negative feedback process of the stochastic resonance (SR). Through the combination of the above two methods, the weak fault extraction under strong background noise is realized. The article analyzes the effects of the delay term, feedback term, and system parameter on the effect of SR and uses the ant colony algorithm (ACA) to optimize the above parameters. Finally, through simulated and engineering experimental results, it is proved that the proposed method has more advantages than the CBSR method in weak fault feature extraction.

scholarly journals Weak fault feature extraction of gear based on KVMD and singular value difference spectrum

2018 ◽  
Vol 211 ◽  
pp. 08001
Author(s):  
Hongkun Li ◽  
Chaoge Wang ◽  
Mengfan Hou ◽  
Rui Yang ◽  
Daolong Tang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Difference Spectrum ◽  
Singular Value ◽  
Intrinsic Mode Functions ◽  
Correct Judgment ◽  
Reconstructed Signal ◽  
Gear Fault ◽  
Weak Fault ◽  
Fault Feature Extraction

Gearbox is an important component of many industrial applications. When the gear fault occurs, the vibration signal is characterized by multi-component, multi-frequency modulation, low signal to noise ratio, weak fault characteristics and difficult to extract. This paper proposes a gear fault feature extraction method based on improved variational mode decomposition(VMD) and singular value difference spectrum. Firstly, the method is optimized for the decomposition level K of the VMD algorithm, and an improved method of VMD decomposition layer number K for central frequency screening (KVMD) is proposed. Then, the gear fault vibration signal is decomposed into a series of bandlimited intrinsic mode functions using KVMD. Due to the interference of the noise, it is difficult to make the correct judgment of fault in the spectrum of each mode component. According to the correlation coefficient criterion, the components with larger correlation coefficients are chosen to singular value decomposition. The singular value difference spectrum is obtained, and the effective order of the reconstructed signal is determined from the difference spectrum to denoise the signal; Finally, the processed signal is analyzed by Hilbert envelope. The fault characteristic frequency can be extracted accurately from the envelope spectrum. Through the analysis of the experimental data of gear fault, the results show that the method can effectively reduce the influence of the noise, and accurately realize the extraction of gear fault feature information.

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