scholarly journals Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan Ren ◽  
Jin Huang ◽  
Lei-Ming Hu ◽  
Hong-Ping Chen ◽  
Xiao-Kai Li
Keyword(s):  
Stochastic Resonance ◽  
Decomposition Method ◽  
Signal To Noise Ratio ◽  
Engineering Application ◽  
Fourier Decomposition ◽  
Noise Interference ◽  
Nonstationary Vibration ◽  
Reconstructed Signal ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

In order to effectively extract the characteristics of nonstationary vibration signals from hydropower units under noise interference, an adaptive stochastic resonance and Fourier decomposition method (FDM) based on genetic algorithm (GA) are proposed in this paper. Firstly, GA is used to optimize the resonance parameters so that the signal can reach the optimal resonance and the signal-to-noise ratio (SNR) can be improved. Secondly, FDM is used to process the signal and the appropriate frequency band function is selected for reconstruction. Finally, Hilbert envelope demodulation analysis was performed on the reconstructed signal to obtain the fault characteristics from the envelope spectrum. In order to prove the effectiveness and superiority of the proposed method, comparative experiments are designed by using the simulated signal and the measured swing signal of a hydropower unit. The results show that this method can effectively remove the noise interference and improve the SNR and extract the characteristic frequency of the signal, which has the extensive engineering application value to the fault diagnosis of hydropower units.

Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition

2019 ◽  
Vol 234 (6) ◽  
pp. 848-861 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Shuai Wang ◽  
Yiqun Qian
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Variational Mode Decomposition ◽  
Variable Speed ◽  
Noise Interference ◽  
Order Spectrum ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Non Stationary Signal ◽  
Fault Feature Extraction

This paper proposes a study on gearbox fault feature extraction of wind turbine under variable speed condition using improved adaptive variational mode decomposition (VMD). Frequent changes in wind speed and critical noise interference make the vibration signal exhibit non-stationary characteristics. Although computational order tracking can transform non-stationary signals in the time domain into stationary angular signals, and then extract fault features from order spectrum obtained by FFT, the obtained order spectrum is liable to be polluted by noise or to appear order aliasing. To avoid order aliasing and eliminate noise interference, the original non-stationary signal is firstly processed using the improved adaptive VMD method called adaptive differential evolution – VMD (ADE-VMD). ADE-VMD can not only utilise the advantages of traditional VMD but also adaptively select narrow-band intrinsic mode function (NBIMF) to construct the reconstructed signal with less noise and without order aliasing. In the experiment, we compared the ADE-VMD method with other VMD methods such as GA-VMD, PSO-VMD and DE-VMD, and the results showed that ADE-VMD has excellent adaptive processing ability, and its convergence and optimisation speed are more remarkable. ADE-VMD can effectively filter the noise inference and avoid the order aliasing, so it is well suitable for fault feature extraction under variable speed.

scholarly journals Rotating Elements Fault Diagnosis Method Based on Stochastic Resonance with Triple-well Potential System of Genetic Algorithm

2020 ◽  
Author(s):  
Liu ZiWen ◽  
Bao JinSong ◽  
Xiao Lei ◽  
Wang BoBo
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Engineering Application ◽  
Resonance Method ◽  
Rotating Body ◽  
Potential System ◽  
Diagnosis Method

Abstract In engineering applications, the fault signal of rotating elements is easily submerged in the background noise. In order to solve this problem, a rotating body fault diagnosis method based on stochastic resonance with triple-well potential system of genetic algorithm is proposed. In this method, the signal-to-noise ratio(SNR) of the Triple-well potential system is used as the fitness function of the genetic algorithm, and several parameters of the system are optimized at the same time, which effectively improves the feature extraction effect of the weak fault of the rotating body. The results of simulation and engineering experiments show that this method has better detection effect than bistable stochastic resonance method, and can effectively detect the fault signal submerged by noise, which has a good engineering application prospect.

Research and application of Stochastic Resonance in Quad-stable Potential System

2021 ◽  
Author(s):  
Li-Fang He ◽  
Qiu-Ling Liu ◽  
Tian-Qi Zhang
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
First Passage Time ◽  
Engineering Application ◽  
Reference Value ◽  
Passage Time ◽  
Signal To Noise ◽  
Stationary Probability Distribution ◽  
Potential System ◽  
Practical Engineering

Abstract To solve the problem of low weak signal enhancement performance in the quad-stable system, a new Quad-stable potential Stochastic Resonance (QSR) is proposed. Firstly, under the condition of adiabatic approximation theory, the Stationary Probability Distribution (SPD), the Mean First Passage Time (MFPT), the Work (W) and the power Spectrum Amplification Factor (SAF) are derived, and the impacts of system parameters on them are also deeply analyzed. Secondly, numerical simulations are performed to compare QSR with the Classical Tri-stable Stochastic Resonance (CTSR) by using the Genetic Algorithm (GA) and the fourth-order Runge-Kutta algorithm. It shows that the Signal-to-Noise Ratio (SNR) and Mean Signal-to-Noise Increase (MSNRI) of QSR are higher than CTSR, which indicates that QSR has superior noise immunity than CTSR. Finally, the two systems are applied in the detection on real bearing faults. The experimental results show that QSR is superior to CTSR, which provides a better theoretical significance and reference value for practical engineering application.

scholarly journals Stochastic resonance in periodically driven bistable systems subjected to anomalous diffusion

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
F. Naha Nzoupe ◽  
Alain M. Dikandé
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Planck Equation ◽  
Relevant Information ◽  
Periodic Signal ◽  
Signal To Noise ◽  
Periodically Driven ◽  
Loop Area ◽  
Bistable Systems ◽  
Noise Ratio

AbstractThe occurrence of stochastic resonance in bistable systems undergoing anomalous diffusions, which arise from density-dependent fluctuations, is investigated with an emphasis on the analytical formulation of the problem as well as a possible analytical derivation of key quantifiers of stochastic resonance. The nonlinear Fokker–Planck equation describing the system dynamics, together with the corresponding Ito–Langevin equation, is formulated. In the linear response regime, analytical expressions of the spectral amplification, of the signal-to-noise ratio and of the hysteresis loop area are derived as quantifiers of stochastic resonance. These quantifiers are found to be strongly dependent on the parameters controlling the type of diffusion; in particular, the peak characterizing the signal-to-noise ratio occurs only in close ranges of parameters. Results introduce the relevant information that, taking into consideration the interactions of anomalous diffusive systems with a periodic signal, can provide a better understanding of the physics of stochastic resonance in bistable systems driven by periodic forces.

Vibration analysis and combustion parameter evaluation of CI engine based on Fourier Decomposition Method

2021 ◽  
pp. 146808742098819
Author(s):  
Wang Yang ◽  
Cheng Yong
Keyword(s):  
Vibration Analysis ◽  
Decomposition Method ◽  
Combustion Process ◽  
Pressure Rise ◽  
Maximum Pressure ◽  
Vibration Signals ◽  
Loop Control ◽  
Fourier Decomposition ◽  
Surface Vibration ◽  
Rise Rate

As a non-intrusive method for engine working condition detection, the engine surface vibration contains rich information about the combustion process and has great potential for the closed-loop control of engines. However, the measured engine surface vibration signals are usually induced by combustion as well as non-combustion excitations and are difficult to be utilized directly. To evaluate some combustion parameters from engine surface vibration, the tests were carried out on a single-cylinder diesel engine and a new method called Fourier Decomposition Method (FDM) was used to extract combustion induced vibration. Simulated and test results verified the ability of the FDM for engine vibration analysis. Based on the extracted vibration signals, the methods for identifying start of combustion, location of maximum pressure rise rate, and location of peak pressure were proposed. The cycle-by-cycle analysis of the results show that the parameters identified based on vibration and in-cylinder pressure have the similar trends, and it suggests that the proposed FDM-based methods can be used for extracting combustion induced vibrations and identifying the combustion parameters.

A new fault diagnosis method based on adaptive spectrum mode extraction

2021 ◽  
pp. 147592172098694
Author(s):  
Zhijian Wang ◽  
Ningning Yang ◽  
Naipeng Li ◽  
Wenhua Du ◽  
Junyuan Wang
Keyword(s):  
Scale Space ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Mode Extraction ◽  
Non Stationary Signal ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

Adaptive power spectrum Fourier decomposition method with application in fault diagnosis for rolling bearing

Measurement ◽  
2021 ◽  
pp. 109837
Author(s):  
Jinde Zheng ◽  
Siqi Huang ◽  
Haiyang Pan ◽  
Jinyu Tong ◽  
Chengjun Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Decomposition Method ◽  
Rolling Bearing ◽  
Fourier Decomposition ◽  
Adaptive Power

QUANTIFYING STOCHASTIC RESONANCE IN A SINGLE THRESHOLD DETECTOR FOR RANDOM APERIODIC SIGNALS

2004 ◽  
Vol 04 (02) ◽  
pp. L247-L265 ◽  
Author(s):  
ARUNEEMA DAS ◽  
N. G. STOCKS ◽  
A. NIKITIN ◽  
E. L. HINES
Keyword(s):  
Stochastic Resonance ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Probability Distributions ◽  
Probability Density Functions ◽  
Surprising Result ◽  
Threshold Detector ◽  
Gaussian Form ◽  
Aperiodic Signal

We explore stochastic resonance (SR) effects in a single comparator (threshold detector) driven by either a Gaussian or exponentially distributed aperiodic signal. The behaviour of different performance measures, namely the cross-correlation coefficient (CCC), signal-to-noise ratio (SNR) and mutual information, I, has been investigated. The signals were added to Gaussian noise before being passed through the threshold detector. For the two signals tested, we observe the perhaps surprising result that the SNR never displays SR. However, SR is displayed by both the CCC and I for Gaussian signals. For exponential signals SR is not displayed by any of the measures. By generating signals whose probability distributions have the generalized Gaussian form Ae-|βx|n it is possible to demonstrate that SR ceases to occur if n<1.7. We conclude that SR is only observable in threshold based systems for certain types of aperiodic signal. Specifically, SR is not expected to occur for signals whose probability density functions have long, slowly decaying, tails. We discuss the implication of these results for the role of SR in biological sensory systems.

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