Analysis about Effect of Hollow on Time-Frequency Characteristic of Surface Vibration Signal

2014 ◽  
Vol 533 ◽  
pp. 181-186
Author(s):  
Ming Sheng Zhao ◽  
En An Chi ◽  
Qiang Kang ◽  
Tie Jun Tao
Keyword(s):  
Vibration Signal ◽  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Distribution Method ◽  
Vibration Signals ◽  
Band Energy ◽  
Surface Vibration ◽  
Time Frequency ◽  
Working Face ◽  
Blasting Excavation

In blasting excavation of shallow tunnel, the surface vibration of excavated tunnel can be amplified due to effect of hollow. This effect is an important factor for safety of surface buildings. Based on the measured data of one tunnel excavation project, combining wavelet analysis and AOK time-frequency distribution method, the surface vibration signals in front and rear position of working face are processed into different frequency bands. Taking PPV, dominant frequency, d7 (7.8125-15.625 Hz) band energy ratio and d7 (7.8125-15.625 Hz) band energy duration as indexes, the effect of hollow on time-frequency characteristics of surface vibration signal is studied in this article. The results show that, affected by the hollow in excavated region, the PPV and dominant frequency increase, and the d7 (7.8125-15.625 Hz) band energy shows fluctuant ratio of total energy and an increase of band energy duration. The results show that the hollow influence on the frequency characteristics of the surface vibration signals comprehensively, and also provide an analytical basis for anti-vibration and vibration reduction study from the angle of energy.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

scholarly journals Experimental Investigation on Time-Frequency Characteristics of Microseismic Signals in the Damage Evolution Process of Coal and Rock

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 809
Author(s):  
Wei Yang ◽  
Chengwu Li ◽  
Rui Xu ◽  
Xunchang Li
Keyword(s):  
Damage Evolution ◽  
Resonance Effect ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Evolution Process ◽  
Deformation And Failure ◽  
Time Frequency ◽  
Rock Materials ◽  
Damage Degree

The deformation and failure of coal and rock materials is the primary cause of many engineering disasters. How to accurately and effectively monitor and forecast the damage evolution process of coal and rock mass, and form a set of prediction methods and prediction indicators is an urgent engineering problems to be solved in the field of rock mechanics and engineering. As a form of energy dissipation in the deformation process of coal and rock, microseismic (MS) can indirectly reflect the damage of coal and rock. In order to analyze the relationship between the damage degree of coal and rock and time-frequency characteristics of MS, the deformation and fracture process of coal and rock materials under different loading modes was tested. The time-frequency characteristics and generation mechanism of MS were analyzed under different loading stages. Meanwhile, the influences of properties of coal and rock materials on MS signals were studied. Results show that there is an evident mode cutoff point between high-frequency and low-frequency MS signals. The properties of coal and rock, such as the development degree of the original fracture, particle size and dense degree have a decisive influence on the amplitude, frequency, energy and other characteristic parameters of MS signals. The change of MS parameters is closely related to material damage, but has no strong relation with the loading rate. The richness of MS signals before the main fracture depends on the homogeneity of materials. With the increase of damage, the energy release rate increases, which can lead to the widening of MS signals spectrum. The stiffness and natural frequency of specimens decreases correspondingly. Meanwhile, the main reason that the dominant frequency of MS detected by sensors installed on the surface of coal and rock materials is mainly low-frequency is friction loss and the resonance effect. In addition, the spectrum and energy evolution of MS can be used as a characterization method of the damage degree of coal and rock materials. Furthermore, the results can provide important reference for prediction and early warning of some rock engineering disasters.

Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ravikumar KN ◽  
Hemantha Kumar ◽  
Kumar GN ◽  
Gangadharan KV
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Internal Combustion ◽  
Frequency Characteristics ◽  
Ic Engine ◽  
Vibration Signals ◽  
Content Type ◽  
Time Frequency ◽  
Gear Fault

PurposeThe purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.Design/methodology/approachVibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.FindingsThe obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.Practical implicationsThe proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.Originality/valueIn this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

scholarly journals Application of Choi—Williams Reduced Interference Time Frequency Distribution to Machinery Diagnostics

1995 ◽  
Vol 2 (6) ◽  
pp. 437-444 ◽  
Author(s):  
Howard A. Gaberson
Keyword(s):  
Fourier Transform ◽  
Vibration Signal ◽  
Test Cases ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Time Frequency ◽  
Machinery Diagnostics ◽  
Short Time ◽  
Amplitude And Frequency Modulation ◽  
Time Frequency Distribution

This article discusses time frequency analysis of machinery diagnostic vibration signals. The short time Fourier transform, the Wigner, and the Choi–Williams distributions are explained and illustrated with test cases. Examples of Choi—Williams analyses of machinery vibration signals are presented. The analyses detect discontinuities in the signals and their timing, amplitude and frequency modulation, and the presence of different components in a vibration signal.

Gearbox Systems Dynamic Modelling for Diagnostic Fault Detection

2003 ◽  
Author(s):  
Walter Bartelmus ◽  
Radosław Zimroz
Keyword(s):  
Computer Simulation ◽  
Mathematical Modelling ◽  
Dynamic Modelling ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Time Frequency ◽  
Network Training ◽  
Condition Change ◽  
Tooth Stiffness ◽  
Distributed Faults

The paper deals with mathematical modelling and computer simulation of a gearbox driving system with a double stage gearbox. Mathematical modelling and computer simulations are used for supporting diagnostic inference. Vibration is thought of as a signal of gear condition. It is stressed that vibration generated by gears is influenced by many factors. These factors are divided into four groups: design, production technology, operational, condition change. The condition change of a gearbox is given by gear faults that are divided into single faults such as a tooth crack or breakage or distributed faults as pitting, scuffing, and erosion. The faults are modelled in the case of a crack as a change of tooth stiffness in the case of distributed faults they are given multi-parameter functions. Simulated signals undergo signal analysis by spectrum, cepstrum, time-frequency spectrogram. It has been shown by computer simulation that single and distributed faults are identified by cepstrum. For explicit fault identification time-frequency spectrogram has to be additionally used. The computer simulation results are confirmed by analysis of measured vibration signals received from a gearbox wall/housing. The aim of mathematical modelling and computer simulation, besides finding the relationship between gear condition and vibration signal is in the future to give vibration signals for neural network training.

Blasting Vibration Signals Time-Frequency Analysis Based on S-Transform

2012 ◽  
Vol 588-589 ◽  
pp. 2013-2017
Author(s):  
Dong Tao Li ◽  
Jing Long Yan ◽  
Le Zhang
Keyword(s):  
Frequency Analysis ◽  
Vibration Signal ◽  
Transformation Method ◽  
Blasting Vibration ◽  
Time Duration ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Vibration Wave

Introduced the theory of S-transform, designed simulation experiment and the frequency components distribution versus time was, verified that the S-transformation method is suitable for blasting vibration signal time-frequency analyzed. Applied it to the time-frequency analysis of measured blasting vibration signals at situ, the results show that S-transform has excellent time-frequency representation ability and higher resolution, reveals the detail information of blasting vibration wave changing with time and frequency, and provides a new way for blasting vibration research. Determined the desired delay intervals through comparing the energy of signal and the time duration of the waveform at characteristic frequency between two-hole blasting vibration signals with different delay intervals.

scholarly journals Time-Frequency Characteristics Analysis on Vibration Signals of a Three-Supported Rotor System with Misalignment

2011 ◽  
Vol 2-3 ◽  
pp. 717-721 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Mei Ling Wang ◽  
Li Jing Lin ◽  
Jian Wei Ji ◽  
Qing Kai Han
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Characteristics Analysis ◽  
Novel Method ◽  
Misalignment Fault

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

Time Frequency Analysis of Blasting Vibration Superposing Signal under Different Millisecond Intervals

2012 ◽  
Vol 170-173 ◽  
pp. 3097-3101
Author(s):  
Wei Zhang ◽  
Shi Hai Chen
Keyword(s):  
Vibration Reduction ◽  
Maximum Amplitude ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Single Stage ◽  
Blasting Vibration ◽  
Reduction Theory ◽  
Time Frequency ◽  
Vibration Effect ◽  
Signal Time

Based on the measured single-stage blasting vibration signal, time-frequency characteristics of two single-stage superposing signals were analyzed under the condition of different millisecond intervals ranging from 1ms to 350ms, also, variation laws of dominant frequency, amplitude and energy of the blasting vibration superposing signal with the delay time and the determination method of rational millisecond interval of similar engineering were put forward. Then, changing laws of the millisecond interval with the interference effect was obtained. It is found that, millisecond delay blasting does not follow the disturbance vibration reduction theory strictly that the vibration effect is weakened when interval is (2n-1)T/2 and strengthened when interval is nT, and the more similar the vibration characteristics of single-stage signals are, the larger the maximum amplitude declining rate of the obtained superposing signal is.

Cutting Institutions Running State Estimation Strategy Based on Wavelet Packet Analysis Technology

2014 ◽  
Vol 574 ◽  
pp. 696-701
Author(s):  
Fu Zhong Wang ◽  
Xiao Ying Tian
Keyword(s):  
Control Strategy ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Frequency Characteristics ◽  
Wavelet Packet Analysis ◽  
Cutting Mechanism ◽  
Estimation Strategy ◽  
Working Face ◽  
Signal Characteristics ◽  
Mine Roadway

During robotic heading machine working, it often encounters the coal seam hardness changing tremendously. So the cutting mechanism will be easy to break down. For this problem, put forward a strategy, which using wavelet packet technology to study the vibration signal of cutting mechanism. Filtered the signal with wavelet packet; extracted vibration signal characteristics; established the energy eigenvector; used the Hilbert technology to extract frequency characteristics. According to the energy eigenvector and the frequency characteristics, estimated the cutting mechanism running status. The simulation in MATLAB proves that the control strategy can estimate the running state of cutting mechanism real-time, and lay an important foundation for the realization of coal mine roadway drivage unmanned working face.

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