scholarly journals Image recognition and diagnosis for vibration characteristics of cone valve core

2020 ◽  
Vol 12 (4) ◽  
pp. 168781402091638
Author(s):  
Beibei Li ◽  
Jingwei Yan ◽  
Qiao Zhao ◽  
Jie He ◽  
Ruirui Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Frequency Spectrum ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Liquid System ◽  
Vibration Characteristics ◽  
Corner Detection ◽  
Signal Spectrum ◽  
Similar Frequency ◽  
Contour Fitting

Research on vibration characteristics of hydraulic valves is helpful to improve the performance of the hydraulic valve. In this article, a visualization experimental method is designed to capture the cone valve core vibration, and the image sequence of the vibration is obtained. And the least-squares contour fitting of the valve core is proposed to analyze the vibration characteristics of the valve core, which could eliminate noise in the vibration signal and improve accuracy. The difference of the least-squares variance between adjacent data points obtained by least-squares contour fitting is one order of magnitude smaller than corner detection method, while the signal-to-noise ratio is more than twice the corner detection. Furthermore, the frequency spectrum and amplitude of the valve core vibration are also discussed. The deviation of the valve core from the coordinate origin will increase with increasing of the pressure difference and decreasing of the pre-compression of the spring and the amplitude of vibration increases too. The vibration signal of the valve core under different conditions has similar frequency spectrum, which is mainly composed of the vibration signal spectrum caused by the fluctuation of oil pressure and turbulence of the liquid system.

scholarly journals Wavelet Packet Transform-Assisted Least Squares Support Vector Machine for Gear Wear Degree Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hongmin Wang ◽  
Liang Chan
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Speed ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Least Square ◽  
Signal Spectrum ◽  
Support Vector ◽  
Particle Swarm Algorithm

Wear degree detection of gears is an effective way to prevent faults. However, due to the interference of high-speed meshing vibration and environmental noise, the weak vibration signal generated by the gear is easily covered by the noise, which makes it difficult to detect the degree of wear. To address this issue, this paper proposes a novel gear wear degree diagnosis method based on local weighted scatter smoothing method (LOWESS), wavelet packet transform (WPT), and least square support vector machine (APSO-LSSVM) optimized by adaptive particle swarm algorithm. According to the low signal-to-noise ratio characteristic of gear vibration signal, LOWESS is first used to preprocess the signal spectrum. Then, the characteristic parameters used to characterize gear wear are extracted from different decomposition depths by WPT and, finally, combined with APSO-SVM to diagnose the degree of gear wear. Compared with the basic least squares support vector machine, the improved method has better performance in sample classification. The experimental results show that the method in this paper can effectively reduce the diagnosis error caused by background noise, and the diagnosis accuracy reaches 98.33%, which can provide a solution for the health status monitoring of gears.

Research on Shearer Rocker Gearbox Fault Location Based on Wavelet Envelope Demodulation

2014 ◽  
Vol 528 ◽  
pp. 210-216
Author(s):  
Zeng Qiang Wang ◽  
Hong Wei Ma ◽  
Mei Hua Tao ◽  
Xu Hui Zhang ◽  
Qing Hua Mao
Keyword(s):  
Fourier Transform ◽  
Frequency Spectrum ◽  
Spectrum Analysis ◽  
Fault Location ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Frequency Spectrum Analysis ◽  
Mechanical Faults ◽  
Faults Diagnosis ◽  
Feature Frequency

To solve the problem of faults location for shearer rocker gearbox, the multiple sites vibration signal of faulty rocker gearbox are collected, as well as the Morlet wavelet envelope demodulation is applied to demodulate vibration signal and Fourier transform is used to carry out frequency spectrum analysis of vibration signal. Experimental results show that this method can effectively extract the faults feature frequency from complex vibration signal. The faults location result is consistent with actual faults part. This mean realizes to locate faults accurately. It provides an effective method for mechanical faults diagnosis of shearer.

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

scholarly journals Vibration Characteristics of Compression Ignition Engines Fueled with Blended Petro-Diesel and Fischer-Tropsch Diesel Fuel from Coal Fuels

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2043 ◽  
Author(s):  
Tiantian Yang ◽  
Tie Wang ◽  
Guoxing Li ◽  
Jinhong Shi ◽  
Xiuquan Sun
Keyword(s):  
Diesel Fuel ◽  
Pressure Oscillation ◽  
Vibration Signal ◽  
Vibration Characteristics ◽  
Compression Ignition ◽  
Fischer Tropsch ◽  
Frequency Bands ◽  
Transient Combustion ◽  
Block Based ◽  
Ci Engines

Fischer-Tropsch diesel fuel synthesized from coal (CFT) is an alternative fuel that gives excellent emission performance in compression ignition (CI) engines. In order to study the vibration characteristics, which are important for determining the applicability of the fuel, CFT-diesel blends were tested on a CI engine to acquire vibration signals from the engine head and block. Based on the FFT and continuous wavelet transformation (CWT) analysis, the influence of CFT on the vibration was studied. The results showed that the root mean square (RMS) values of the vibration signal decrease as the proportion of CFT in the blends increases. The CWT results indicated that the vibration energy areas motivated by the pressure shock of transient combustion were weak with increasing CFT proportion for the different frequency bands. The blend of 90% pure petro-diesel and 10% CFT registered the largest RMS value for piston side thrust response, and the RMS of high-frequency pressure oscillation response is greater than that of the main response of combustion, for FT30. Therefore, CFT has the potential to reduce the combustion vibration of the engine at all frequency bands, and there is a possibility that the proportion of blended fuel can be modified to satisfy the vibration characteristics requirements in different frequency bands.

Cyclic Spectrum Analysis on Rolling-Element Bearing with Inner-Race Point Defect

2011 ◽  
Vol 291-294 ◽  
pp. 1469-1473
Author(s):  
Wei Ke ◽  
Yong Xiang Zhang ◽  
Lin Li
Keyword(s):  
Point Defect ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal ◽  
Inner Race ◽  
Cyclic Spectrum

Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

The Wavelet Analysis and Processing of Weak Vibration Signal

2011 ◽  
Vol 189-193 ◽  
pp. 1426-1431
Author(s):  
Ze Ning Xu ◽  
Hong Yu Liu ◽  
Yong Guo Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Basic Feature ◽  
Wavelet Function ◽  
Fault Analysis ◽  
Bearing Fault Diagnosis ◽  
Gear Fault ◽  
Vibratory Signals

Signal measuring is an important link in machine fault diagnosis. Accurate and reliable fault signals can be achieved by reasonable signal measuring. When the distance between sensor and measuring gear or bearing is comparatively far, the collected signals became weak and disturbed by other vibratory signals in equipments on bearing and gear fault analysis. Useful signals often were submerged in powerful noise, so caused difficult in extracting fault feature. In this paper, according to the feature of vibratory signals in machine test, wavelet analysis basic theory was applied on researching basic feature of wavelet analysis. By selecting suitable wavelet function and applying wavelet elimination noise technology the signal to noise ratio of signal was raised, thus the vibratory impact component can be measured in weak signals. Finally, wavelet analysis was applied on bearing fault diagnosis.

scholarly journals Planetary bearing defect detection in a commercial helicopter main gearbox with vibration and acoustic emission

2018 ◽  
Vol 17 (5) ◽  
pp. 1192-1212 ◽  
Author(s):  
Faris Elasha ◽  
Matthew Greaves ◽  
David Mba
Keyword(s):  
Acoustic Emission ◽  
Signal To Noise Ratio ◽  
Acoustic Emissions ◽  
Research Programme ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Broadband Wireless ◽  
Bearing Faults ◽  
Spectral Kurtosis ◽  
Signal Suppression

Helicopter gearboxes significantly differ from other transmission types and exhibit unique behaviours that reduce the effectiveness of traditional fault diagnostics methods. In addition, due to lack of redundancy, helicopter transmission failure can lead to catastrophic accidents. Bearing faults in helicopter gearboxes are difficult to discriminate due to the low signal-to-noise ratio in the presence of gear vibration. In addition, the vibration response from the planet gear bearings must be transmitted via a time-varying path through the ring gear to externally mounted accelerometers, which cause yet further bearing vibration signal suppression. This research programme has resulted in the successful proof of concept of a broadband wireless transmission sensor that incorporates power scavenging while operating within a helicopter gearbox. In addition, this article investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using vibration and acoustic emissions. It compares their effectiveness for various operating conditions. Three signal processing techniques, including an adaptive filter, spectral kurtosis and envelope analysis, were combined for this investigation. In addition, this research discusses the feasibility of using acoustic emission for helicopter gearbox monitoring.

scholarly journals Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Dan Ma ◽  
Yixiang Lu ◽  
Yushun Zhang ◽  
Hua Bao ◽  
Xueming Peng
Keyword(s):  
Learning Strategy ◽  
Vibration Signal ◽  
Collaborative Representation ◽  
Rolling Bearings ◽  
Frequency Spectra ◽  
Similar Frequency ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Learned Dictionary

In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.

A Novel Approach for Detection of Bearing Defects from Noisy Vibration Signal

2014 ◽  
Vol 592-594 ◽  
pp. 2001-2005
Author(s):  
Deepak Paliwal ◽  
Achintya Choudhury ◽  
T. Govardhan ◽  
Saurabh Singh Chandrawat
Keyword(s):  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
High Background ◽  
Related Information ◽  
Bearing Defects ◽  
Novel Approach ◽  
Deep Groove ◽  
Deep Groove Ball Bearing ◽  
High Background Noise ◽  
Inner Race

Vibration signal of a defective bearing carries fault related information. The aim of this paper is to develop a signal processing methodology that identifies the presence of defect from bearing vibration signal subjected high background noise. A simulated vibration signal considering inner race defect in a deep groove ball bearing with low signal to noise ratio has been generated and investigated. A technique involving CWT of vibration signal and post-processing though FFT has been adopted to analyze the signal. Results show that proposed methodology can yield the presence of inner race defect prominently from a noisy vibration signal.

Simultaneous Multicomponent Quantitative Analysis by Infrared Absorption Spectroscopy

1984 ◽  
Vol 38 (5) ◽  
pp. 663-668 ◽  
Author(s):  
Lesia L. Tyson ◽  
Yong-Chien Ling ◽  
Charles K. Mann
Keyword(s):  
Least Squares ◽  
Infrared Absorption ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Data Handling ◽  
Peak Overlap ◽  
Lipid Mixtures ◽  
Applicable Range ◽  
Better Than

Two data-handling techniques, least-squares fitting and cross-correlation, have been used for three-component analysis under comparable conditions with the use of both simulated and real data Factors considered are the effect of variation in degree of peak overlap, signal-to-noise ratio, the effect of peak width variations when peak maxima occur at the same position, and the effect of varying peak intensities A series of lipid mixtures was analyzed by each method with the use of infrared absorption This permits comparison of these results with earlier reports Both least-squares and cross-correlation can be used with samples that are outside the applicable range of the earlier work In this comparison, the least-squares results are somewhat better than those from cross-correlation

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