Enhancement of the low-level components of milling vibration signals by stochastic resonance

2004 ◽  
Vol 218 (1) ◽  
pp. 33-41 ◽  
Author(s):  
B E Klamecki
Keyword(s):  
Stochastic Resonance ◽  
Vibration Signals ◽  
Low Level ◽  
Milling Vibration

scholarly journals Structural Low-Level Dynamic Response Analysis Using Deviations of Idealized Edge Profiles and Video Acceleration Magnification

2019 ◽  
Vol 9 (4) ◽  
pp. 712
Author(s):  
Dashan Zhang ◽  
Liangfei Fang ◽  
Ye Wei ◽  
Jie Guo ◽  
Bo Tian
Keyword(s):  
High Frequency ◽  
Vibration Characteristics ◽  
Response Analysis ◽  
Test Beam ◽  
Structural Dynamic ◽  
Vibration Signals ◽  
High Frequency Vibration ◽  
Low Level ◽  
Beam Experiment ◽  
Noise Barrier

The development of high-speed camera systems and image processing techniques has promoted the use of vision-based methods as a practical alternative for the analysis of non-contact structural dynamic responses. In this study, a deviation extraction method is introduced to obtain deviation signals from structural idealized edge profiles. Given that the deviation temporal variations can reflect the structural vibration characteristics, a method based on singular-value decomposition (SVD) is proposed to extract valuable vibration signals from the matrix composed of deviations from all video frames. However, this method exhibits limitations when handling low-level motions that reflect high-frequency vibration components. Hence, a video acceleration magnification algorithm is employed to enhance low-level deviation variations before the extraction. The enhancement of low-level deviation variations is validated by a light-weight cantilever beam experiment and a noise barrier field test. From the extracted waveforms and their spectrums from the original and magnified videos, subtle deviations of the selected straight-line edge profiles are magnified in the reconstructed videos, and low-level high-frequency vibration signals are successfully enhanced in the final extraction results. Vibration characteristics of the test beam and the noise barrier are then analyzed using signals obtained by the proposed method.

scholarly journals The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6348
Author(s):  
Chao Zhang ◽  
Haoran Duan ◽  
Yu Xue ◽  
Biao Zhang ◽  
Bin Fan ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Stochastic Resonance ◽  
Operating Conditions ◽  
Detection Accuracy ◽  
Wind Turbine Gearbox ◽  
Resonance Model ◽  
Function Type ◽  
Vibration Signals ◽  
Fourier Decomposition ◽  
Bearing Fault

As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.

An enhanced stochastic resonance method for weak feature extraction from vibration signals in bearing fault detection

2013 ◽  
Vol 228 (5) ◽  
pp. 815-827 ◽  
Author(s):  
Yaguo Lei ◽  
Jing Lin ◽  
Dong Han ◽  
Zhengjia He
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Stochastic Resonance ◽  
Industrial Applications ◽  
Wheel Pair ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Fault Features ◽  
Bearing Vibration

Rolling element bearings are widely used in modern machinery and play an important role in industrial applications. Tough environments under which they work make them subject to failure. The classical strategy is to collect bearing vibration signals and denoise the signals to detect fault features by using signal processing techniques. Although the noise is reduced with this strategy, the fault features may be weakened or even destroyed as well. Different from the classical denoising techniques, stochastic resonance is able to extract weak features embedded in heavy noise by utilizing noise instead of eliminating noise. The single stochastic resonance, however, fails to extract the fault features when the signal-to-noise ratio of the bearing vibration signals is very low. To address this problem, this paper investigates the enhancement methods of stochastic resonance and develops a cascaded stochastic resonance-based weak feature extraction method for bearing fault detection. Two sets of vibration signals collected respectively from an experimental bearing and a bearing inside a train wheel pair are utilized to demonstrate the proposed method. The results show that the method is superior to the other enhancement methods in extracting weak features of bearing faults.

A Numerical and Experimental Investigation of Period-n Bifurcations in Milling

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Andrew Honeycutt ◽  
Tony Schmitz
Keyword(s):  
Bifurcation Diagrams ◽  
Poincaré Maps ◽  
Vibration Signals ◽  
Stability Behavior ◽  
Poincare Maps ◽  
Numerical Studies ◽  
Stability Maps ◽  
The Stability ◽  
Milling Vibration ◽  
Equations Of Motions

Numerical and experimental analyses of milling bifurcations, or instabilities, are detailed. The time-delay equations of motions that describe milling behavior are solved numerically and once-per-tooth period sampling is used to generate Poincaré maps. These maps are subsequently used to study the stability behavior, including period-n bifurcations. Once-per-tooth period sampling is also used to generate bifurcation diagrams and stability maps. The numerical studies are combined with experiments, where milling vibration amplitudes are measured for both stable and unstable conditions. The vibration signals are sampled once-per-tooth period to construct experimental Poincaré maps and bifurcation diagrams. The results are compared to numerical stability predictions. The sensitivity of milling bifurcations to changes in natural frequency and damping is also predicted and observed.

scholarly journals A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings

2020 ◽  
Vol 10 (7) ◽  
pp. 2602
Author(s):  
Lu Lu ◽  
Yu Yuan ◽  
Chen Chen ◽  
Wu Deng
Keyword(s):  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Second Order ◽  
The Other ◽  
Mechanical Equipment ◽  
Periodic Signal ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Controlling System

In mechanical equipment, rolling bearings analyze and monitor their fault based on their vibration signals. Vibration signals obtained are usually weak because the machine works in a noisy background that makes it very difficult to extract its feature. To address this problem, a second-order coupled step-varying stochastic resonance (SCSSR) system is proposed. The system couples two second-order stochastic resonance (SR) systems into a multistable system, one of which is a controlled system and the other of which is a controlling system that uses the output of one system to adjust the output of the other system to enhance the weak signal. In this method, we apply the seeker optimization algorithm (SOA), which uses the output signal-to-noise ratio (SNR) as the estimating function and combines the twice-sampling technology to adaptively select the parameters of the coupled SR system to achieve feature enhancement and collection of the weak periodic signal. The simulation and real fault data of a bearing prove that this method has better results in detecting weak signals, and the system output SNR is higher than the traditional SR method.

Research on Mechanical Fault Diagnosis Based on Cascaded Bistable Stochastic Resonance and Fractal Box Dimension

2011 ◽  
Vol 141 ◽  
pp. 434-437
Author(s):  
Yan Hao ◽  
Jing Chuan Dong ◽  
Tai Yong Wang ◽  
Jian Wan ◽  
Pan Zhang
Keyword(s):  
Fractal Dimension ◽  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
High Frequency ◽  
Mechanical Vibration ◽  
Box Dimension ◽  
Frequency Noise ◽  
Vibration Signals ◽  
Measurement Capability ◽  
Non Linear

Combining the filtering performance of cascaded bistable stochastic resonance with measurement capability of fractal box dimension for non-linear characteristics of signals, a method of mechanical fault diagnosis based on cascaded bistable stochastic resonance and fractal box dimension was presented. The experiment results showed that this method removed high frequency noise efficiently and obtained precise fractal dimension. In order to implement mechanical fault diagnosis, the non-linear characteristics of mechanical vibration signals were measured by fractal dimension accurately.

Tryptophan-Niacin Metabolism in Rat with Puromycin Aminonucleoside-Induced Nephrosis

2006 ◽  
Vol 76 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Yukari Egashira ◽  
Shin Nagaki ◽  
Hiroo Sanada
Keyword(s):  
Body Weight ◽  
Urinary Excretion ◽  
Enzyme Activities ◽  
Treated Group ◽  
Control Group ◽  
Puromycin Aminonucleoside ◽  
Low Level ◽  
Key Enzyme

We investigated the change of tryptophan-niacin metabolism in rats with puromycin aminonucleoside PAN-induced nephrosis, the mechanisms responsible for their change of urinary excretion of nicotinamide and its metabolites, and the role of the kidney in tryptophan-niacin conversion. PAN-treated rats were intraperitoneally injected once with a 1.0% (w/v) solution of PAN at a dose of 100 mg/kg body weight. The collection of 24-hour urine was conducted 8 days after PAN injection. Daily urinary excretion of nicotinamide and its metabolites, liver and blood NAD, and key enzyme activities of tryptophan-niacin metabolism were determined. In PAN-treated rats, the sum of urinary excretion of nicotinamide and its metabolites was significantly lower compared with controls. The kidneyα-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (ACMSD) activity in the PAN-treated group was significantly decreased by 50%, compared with the control group. Although kidney ACMSD activity was reduced, the conversion of tryptophan to niacin tended to be lower in the PAN-treated rats. A decrease in urinary excretion of niacin and the conversion of tryptophan to niacin in nephrotic rats may contribute to a low level of blood tryptophan. The role of kidney ACMSD activity may be minimal concerning tryptophan-niacin conversion under this experimental condition.

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