scholarly journals Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 275
Author(s):  
Di Xu ◽  
Jianghua Ge ◽  
Yaping Wang ◽  
Junpeng Shao
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Frequency Noise ◽  
Weak Signal ◽  
Resonance System ◽  
Weak Fault ◽  
High Pass ◽  
Fault Characteristic

In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.

Aerodynamic noise from an asymmetric airfoil with perforated extension plates at the trailing edge

2020 ◽  
pp. 1475472X2097838
Author(s):  
CK Sumesh ◽  
TJS Jothi
Keyword(s):  
High Frequency ◽  
Sound Pressure ◽  
Pore Diameter ◽  
Low Frequency ◽  
Aerodynamic Noise ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Trailing Edge ◽  
High Frequency Noise ◽  
Displacement Thickness

This paper investigates the noise emissions from NACA 6412 asymmetric airfoil with different perforated extension plates at the trailing edge. The length of the extension plate is 10 mm, and the pore diameters ( D) considered for the study are in the range of 0.689 to 1.665 mm. The experiments are carried out in the flow velocity ( U∞) range of 20 to 45 m/s, and geometric angles of attack ( αg) values of −10° to +10°. Perforated extensions have an overwhelming response in reducing the low frequency noise (<1.5 kHz), and a reduction of up to 6 dB is observed with an increase in the pore diameter. Contrastingly, the higher frequency noise (>4 kHz) is observed to increase with an increase in the pore diameter. The dominant reduction in the low frequency noise for perforated model airfoils is within the Strouhal number (based on the displacement thickness) of 0.11. The overall sound pressure levels of perforated model airfoils are observed to reduce by a maximum of 2 dB compared to the base airfoil. Finally, by varying the geometric angle of attack from −10° to +10°, the lower frequency noise is seen to increase, while the high frequency noise is observed to decrease.

scholarly journals Surface EMG signal normalisation and filtering improves sensitivity of equine gait analysis

2019 ◽  
Vol 15 (3) ◽  
pp. 173-185 ◽  
Author(s):  
L. St. George ◽  
S.H. Roy ◽  
J. Richards ◽  
J. Sinclair ◽  
S.J. Hobbs
Keyword(s):  
Repeated Measures ◽  
Low Frequency ◽  
Biceps Femoris ◽  
Surface Emg ◽  
Frequency Noise ◽  
Vertical Loading ◽  
Stifle Joint ◽  
Emg Signal ◽  
Stride Duration ◽  
High Pass

Low-frequency noise attenuation and normalisation are fundamental signal processing (SP) methods for surface electromyography (sEMG), but are absent, or not consistently applied, in equine biomechanics. The purpose of this study was to examine the effect of different band-pass filtering and normalisation conventions on sensitivity for identifying differences in sEMG amplitude-related measures, calculated from leading (LdH) and trailing hindlimb (TrH) during canter, where between-limb differences in vertical loading are known. sEMG and 3D-kinematic data were collected from the right Biceps Femoris in 10 horses during both canter leads. Peak hip and stifle joint angle and angular velocity were calculated during stance to verify between-limb biomechanical differences. Four SP methods, with and without normalisation and high-pass filtering, were applied to raw sEMG data. Methods 1 (M1) to 4 (M4) included DC-offset removal and full-wave rectification. Method 2 (M2) included additional normalisation relative to maximum sEMG across all strides. Method 3 (M3) included additional high-pass filtering (Butterworth 4th order, 40 Hz cut-off), for artefact attenuation. M4 included the addition of high-pass filtering and normalisation. Integrated EMG (iEMG) and average rectified value (ARV) were calculated using processed sEMG data from M1 – M4, with stride duration as the temporal domain. sEMG parameters, within M1 – M4, and kinematic parameters were grouped by LdH and TrH and compared using repeated measures ANOVA. Significant between-limb differences for hip and stifle joint kinematics were found, indicating functional differences in hindlimb movement. M2 and M4, revealed significantly greater iEMG and ARV for LdH than TrH (P<0.01), with M4 producing the lowest P-values and largest effect sizes. Significant between-limb differences in sEMG parameters were not observed with M1 and M3. The results indicate that equine sEMG SP should include normalisation and high-pass filtering to improve sensitivity for identifying differences in muscle function associated with biomechanical changes during equine gait.

Paper 14: Acoustic Detection of Cavitation

1966 ◽  
Vol 181 (1) ◽  
pp. 1-8 ◽  
Author(s):  
I. S. Pearsall
Keyword(s):  
High Frequency ◽  
Critical Frequency ◽  
Low Frequency ◽  
Francis Turbine ◽  
Frequency Noise ◽  
Performance Tests ◽  
Acoustic Detection ◽  
Cavitation Noise ◽  
Further Development ◽  
Onset Of Cavitation

The onset of cavitation in a hydraulic machine can be determined visually and its effect on performance by performance tests. It would be convenient to have an alternative method that required neither transparent sections nor expensive tests. Initial tests have been made measuring noise over a frequency range of 20 c/s-20 kc/s in one-third octave bands, on a number of pumps and turbines. An accelerometer attached to the casing was used. The tests indicated that, generally, the onset of cavitation was accompanied by a rise in the high-frequency noise, whilst the low-frequency noise increased as the cavitation developed. A maximum of cavitation noise was reached before the efficiency and load fell off. In some cases difficulty was experienced because blade cavitation was drowned by noise caused by other cavitation, such as the vortex in a Francis turbine. It also appears that the noise following the onset of cavitation is at the frequency which is used as a critical frequency in accelerated erosion tests. Further development of techniques is required, but the initial tests are encouraging.

NovelWoods–Saxon stochastic resonance system for weak signal detection

2020 ◽  
Vol 29 (4) ◽  
pp. 040503 ◽  
Author(s):  
Yong-Hui Zhou ◽  
Xue-Mei Xu ◽  
Lin-Zi Yin ◽  
Yi-Peng Ding ◽  
Jia-Feng Ding ◽  
...  
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Resonance System

scholarly journals Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance

2020 ◽  
Vol 10 (6) ◽  
pp. 2048 ◽  
Author(s):  
Yang Jiang ◽  
Bo He ◽  
Jia Guo ◽  
Pengfei Lv ◽  
Xiaokai Mu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Ant Colony ◽  
Resonance System ◽  
Resonance Model ◽  
Weak Fault

The autonomous underwater vehicle (AUV) is mainly used in the development and exploration of the ocean. As an important module of the AUV, the actuator plays an important role in the normal execution of the AUV. Therefore, the fault diagnosis of the actuator is particularly important. At present, the research on the strong faults, such as the winding of the actuator, has achieved good results, but the research on the weak fault diagnosis is relatively rare. In this paper, the tri-stable stochastic resonance model is analyzed, and the ant colony tri-stable stochastic resonance model is used to diagnose the weak fault. The system accurately diagnoses the fault of the actuator collision and verifies the adaptive tri-stable stochastic resonance system. This model has better diagnostic results than the bi-stable stochastic resonance system.

An improved adaptive stochastic resonance with general scale transformation to extract high-frequency characteristics in strong noise

2018 ◽  
Vol 32 (15) ◽  
pp. 1850185 ◽  
Author(s):  
Dawen Huang ◽  
Jianhua Yang ◽  
Jingling Zhang ◽  
Houguang Liu
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Scale Transformation ◽  
Weak Signal ◽  
Signal Features ◽  
Signal Characteristics ◽  
Periodic Signals ◽  
Sr Method ◽  
General Scale

The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics.

scholarly journals The Scaling LInear Macroweather model (SLIM): using scaling to forecast global scale macroweather from months to decades

2015 ◽  
Vol 6 (1) ◽  
pp. 489-545 ◽  
Author(s):  
S. Lovejoy ◽  
L. del Rio Amador ◽  
R. Hébert
Keyword(s):  
Stochastic Models ◽  
High Frequency ◽  
Low Frequency ◽  
Global Scale ◽  
Forecast Skill ◽  
Previous Attempt ◽  
Frequency Noise ◽  
Forecast Errors ◽  
System Memory ◽  
Two Parameter

Abstract. At scales of ≈ 10 days (the lifetime of planetary scale structures), there is a drastic transition from high frequency weather to low frequency macroweather. This scale is close to the predictability limits of deterministic atmospheric models; so that in GCM macroweather forecasts, the weather is a high frequency noise. But neither the GCM noise nor the GCM climate is fully realistic. In this paper we show how simple stochastic models can be developped that use empirical data to force the statistics and climate to be realistic so that even a two parameter model can outperform GCM's for annual global temperature forecasts. The key is to exploit the scaling of the dynamics and the enormous stochastic memories that it implies. Since macroweather intermittency is low, we propose using the simplest model based on fractional Gaussian noise (fGn): the Scaling LInear Macroweather model (SLIM). SLIM is based on a stochastic ordinary differential equations, differing from usual linear stochastic models (such as the Linear Inverse Modelling, LIM) in that it is of fractional rather than integer order. Whereas LIM implicitly assumes there is no low frequency memory, SLIM has a huge memory that can be exploited. Although the basic mathematical forecast problem for fGn has been solved, we approach the problem in an original manner notably using the method of innovations to obtain simpler results on forecast skill and on the size of the effective system memory. A key to successful forecasts of natural macroweather variability is to first remove the low frequency anthropogenic component. A previous attempt to use fGn for forecasts had poor results because this was not done. We validate our theory using hindcasts of global and Northern Hemisphere temperatures at monthly and annual resolutions. Several nondimensional measures of forecast skill – with no adjustable parameters – show excellent agreement with hindcasts and these show some skill even at decadal scales. We also compare our forecast errors with those of several GCM experiments (with and without initialization), and with other stochastic forecasts showing that even this simplest two parameter SLIM model is somewhat superior. In future, using a space–time (regionalized) generalization of SLIM we expect to be able to exploiting the system memory more extensively and obtain even more realistic forecasts.

Interactive Effects of High-Pass Filtering and Masking Noise on Word Recognition

2005 ◽  
Vol 114 (11) ◽  
pp. 867-878 ◽  
Author(s):  
Saravanan Elangovan ◽  
Andrew Stuart
Keyword(s):  
Hearing Loss ◽  
Word Recognition ◽  
Hearing Impairment ◽  
High Frequency ◽  
Cutoff Frequency ◽  
Low Frequency ◽  
Interactive Effects ◽  
Signal To Noise ◽  
Noise Condition ◽  
High Pass

Objectives: This study sought to examine the word recognition performance in noise of individuals with a simulated low-frequency hearing loss. The goal was to understand how low-frequency hearing impairment affects performance on tasks that challenge temporal processing skills. Methods: Twenty-two normal-hearing young adults participated. Monosyllabic words were presented in continuous and interrupted noise at 3 signal-to-noise ratios of −10, 0, and +10 dB. High-pass filtering of the stimuli at 3 different cutoff frequencies (ie, 1,000, 1,250, and 1,500 Hz) simulated the low-frequency hearing impairment. Results: In general, performance decreased with increasing cutoff frequency, was higher for more favorable signal-to-noise ratios, and was superior in the interrupted condition relative to the continuous noise condition. One important revelation was that the magnitude of the performance superiority observed in the interrupted noise condition did not diminish with high-pass filtering; ie, the release from masking in interrupted noise was preserved. Conclusions: The results of the present study complement previous findings in which this paradigm was used with low-pass filtering to simulate a high-frequency hearing loss. That is to say, low-frequency hearing channels are inherently poorer than high-frequency channels in temporal resolution.

Effects of Noise Frequency on Performance and Annoyance for Women and Men

1981 ◽  
Vol 52 (2) ◽  
pp. 435-441 ◽  
Author(s):  
Kelli F. Key ◽  
M. Carr Payne
Keyword(s):  
Sex Differences ◽  
Magnitude Estimation ◽  
High Frequency ◽  
Ambient Noise ◽  
Direct Relationship ◽  
Low Frequency ◽  
Frequency Noise ◽  
Complex Task ◽  
High Frequency Noise ◽  
Noise Frequency

Effects of noise frequencies on both performance on a complex psychomotor task and annoyance were investigated for men ( n = 30) and women ( n = 30). Each subject performed a complex psychomotor task for 50 min. in the presence of low frequency noise, high frequency noise, or ambient noise. Women and men learned the task at different rates. Little effect of noise was shown. Annoyance ratings were subsequently obtained from each subject for noises of various frequencies by the method of magnitude estimation. High frequency noises were more annoying than low frequency noises regardless of sex and immediate prior exposure to noise. Sex differences in annoyance did not occur. No direct relationship between learning to perform a complex task while exposed to noise and annoyance by that noise was demonstrated.

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