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 Research and application of weak fault diagnosis method based on asymmetric potential stochastic resonance

2019 ◽  
Vol 52 (5-6) ◽  
pp. 625-633 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi ◽  
Xueping Ren ◽  
Wenyan Zhu
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Bearing Failure ◽  
Asymmetric System ◽  
Symmetric Potential ◽  
Frequency Detection ◽  
Detection Technology ◽  
Potential Structure

Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.

scholarly journals Asymmetric second-order stochastic resonance weak fault feature extraction method

2020 ◽  
Vol 53 (5-6) ◽  
pp. 788-795
Author(s):  
Jiachen Tang ◽  
Boqiang Shi
Keyword(s):  
Stochastic Resonance ◽  
Output Signal ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Second Order ◽  
Resonance System ◽  
Signal To Noise ◽  
Weak Fault ◽  
Noise Ratio

To solve the problem that the weak fault signal is difficult to extract under strong background noise, an asymmetric second-order stochastic resonance method is proposed. By adjusting the damping factor and the asymmetry, weak signals, noise, and potential wells are matched to each other to achieve the best stochastic resonance state so that weak fault characteristics can be effectively extracted in strong background noise. Under adiabatic approximation, the effects of damping coefficient, noise intensity, and asymmetry on the output signal-to-noise ratio are discussed based on the two-state model theory. Under the same parameters, the output signal-to-noise ratio of the asymmetric second-order stochastic resonance system is better than that of the underdamped second-order stochastic resonance system. The bearing fault and field engineering experimental results are provided to justify the comparative advantage of the proposed method over the underdamped second-order stochastic resonance method.

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

2014 ◽  
Vol 618 ◽  
pp. 458-462
Author(s):  
Gang Yu ◽  
Ye Chen
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Rotating Machinery ◽  
Sliding Windows ◽  
Alpha Stable Distribution ◽  
Early Fault Detection ◽  
The Impact

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

scholarly journals Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lina He ◽  
Chuan Jiang
Keyword(s):  
Genetic Algorithm ◽  
Fault Detection ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Piecewise Linear ◽  
Poor Performance ◽  
System Model ◽  
Adaptive Genetic Algorithm ◽  
Resonance System ◽  
Noise Energy

The stochastic resonance system has the advantage of making the noise energy transfer to the signal energy. Because the existing stochastic resonance system model has the problem of poor performance, an asymmetric piecewise linear stochastic resonance system model is proposed, and the parameters of the model are optimized by a genetic algorithm. The signal-to-noise ratio formula of the model is derived and analyzed, and the theoretical basis for better performance of the model is given. The influence of the asymmetric coefficient on system performance is studied, which provides guidance for the selection of initial optimization range when a genetic algorithm is used. At the same time, the formula is verified and analyzed by numerical simulation, and the correctness of the formula is proved. Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system. The results show that the model has an excellent detection effect, which proves that the model has great potential in fault detection.

A Novel Adaptive Stochastic Resonance Method Based on Tristable System and its Applications

2020 ◽  
pp. 2150004
Author(s):  
Gang Zhang ◽  
Chuan Jiang ◽  
Tian Qi Zhang
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Piecewise Linear ◽  
Correlation Coefficients ◽  
Resonance Method ◽  
Resonance System ◽  
Signal Energy ◽  
Noise Energy ◽  
Novel Model ◽  
Output Snr

Stochastic resonance systems have the advantages of converting noise energy into signal energy, and have great potential in the field of signal detection and extraction. Aiming at the problems of the performance of classical stochastic resonance system whose model is not perfect enough and the correlation coefficients between parameters is too large to be optimized by algorithm, then a novel model of the tristable potential stochastic resonance system is proposed. The output SNR formula of the model is derived and analyzed, and the influence of its parameters on the model is clarified. Compared with the piecewise linear model by numerical simulation, the correctness of the formula and the superiority of the model are verified. Finally, the model and the classical tristable model are applied to bearing fault detection in which the genetic algorithm is used to optimize the parameters of the two systems. The results show that the model has better detection effects, which prove that the model has a strong potential in the field of signal detection.

scholarly journals Application of continuous potential function stochastic resonance in early fault diagnosis of rolling bearings

2020 ◽  
Vol 53 (5-6) ◽  
pp. 767-777
Author(s):  
Xueping Ren ◽  
Jian Kang ◽  
Zhixing Li ◽  
Jianguo Wang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Potential Function ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Signal To Noise ◽  
Rolling Bearings ◽  
Continuous Potential ◽  
Potential Parameters

The early fault signal of rolling bearings is very weak, and when analyzed under strong background noise, the traditional signal processing method is not ideal. To extract fault characteristic information more clearly, the second-order UCPSR method is applied to the early fault diagnosis of rolling bearings. The continuous potential function itself is a continuous sinusoidal function. The particle transition is smooth and the output is better. Because of its three parameters, the potential structure is more comprehensive and has more abundant characteristics. When the periodic signal, noise and potential function are the best match, the system exhibits better denoise compared to that of other methods. This paper discusses the influence of potential parameters on the motion state of particles between potential wells in combination with the potential parameter variation diagrams discussed. Then, the formula of output signal-to-noise ratio is derived to further study the relationships among potential parameters, and then the ant colony algorithm is used to optimize potential parameters in order to obtain the optimal output signal-to-noise ratio. Finally, an early weak fault diagnosis method for bearings based on the underdamped continuous potential stochastic resonance model is proposed. Through simulation and experimental verification, the underdamped continuous potential stochastic resonance results are compared with those of the time-delayed feedback stochastic resonance method, which proves the validity of the underdamped continuous potential stochastic resonance method.

An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis

2017 ◽  
Vol 232 (13) ◽  
pp. 2352-2368 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Fault Simulation ◽  
Resonance Method ◽  
Bistable System ◽  
Scale Transformation ◽  
High Frequency Signal ◽  
Bearing Fault ◽  
Feature Information

The general scale transformation (GST) method is used in the bistable system to deal with the weak high-frequency signal submerged into the strong noisy background. Then, an adaptive stochastic resonance (ASR) method with the GST is put forward and realized by the quantum particle swarm optimization (QPSO) algorithm. Through the bearing fault simulation signal, the ASR method with the GST is compared with the normalized scale transformation (NST) stochastic resonance (SR). The results show that the efficiency of the GST method is higher than the NST in recognizing bearing fault feature information. In order to simulate the actual engineering environment, both the adaptive GST and the NST methods are implemented to deal with the same experimental signal, respectively. The signal-to-noise ratio (SNR) of the output is obviously improved by the GST method. Specifically, the efficiency is improved greatly to extract the weak high-frequency bearing fault feature information. Moreover, under different noise intensities, although the SNR is decreased versus the increase of the noise intensity, the ASR method with the GST is still better than the traditional NST SR. The proposed GST method and the related results might have referenced value in the problem of weak high-frequency feature extraction in engineering fields.

scholarly journals Adaptive Unsaturated Bistable Stochastic Resonance Multi-Frequency Signals Detection Based on Preprocessing

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2055
Author(s):  
Lin Cui ◽  
Junan Yang ◽  
Lunwen Wang ◽  
Hui Liu
Keyword(s):  
Stochastic Resonance ◽  
Background Noise ◽  
Detection Method ◽  
Single Frequency ◽  
Complex Signal ◽  
Weak Signals ◽  
Signal Energy ◽  
Mode Decomposition ◽  
Noise Energy ◽  
Periodic Signals

Stochastic resonance (SR) has been widely used for extracting single-frequency weak periodic signals. For multi-frequency weak signals, empirical mode decomposition (EMD) can adaptively decompose the complex signal, but this method also suffers from mode mixing, which affects the accuracy of detection. SR can convert part of the noise energy into signal energy, which compensates for the defects of EMD. According to the advantages of SR and EMD, we constructed a multi-frequency signals detection method using adaptive unsaturated bistable SR based on EMD (EMD-AUBSR). In this study, we avoid the inherent saturation of SR by reconstructing the potential function and improve the multi-frequency signals detection ability by adding the preprocessing element. For strong background noise, the experimental results show that this proposed can effectively detect multi-frequency weak signals and decrease signal aliasing, whereas EMD alone cannot.

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