Investigation on Frictional Vibration Behavior of Tribological Pairs Under Different Wear States

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Di Sun ◽  
Guobin Li ◽  
Haijun Wei ◽  
Haifeng Liao ◽  
Ting Liu
Keyword(s):  
Wavelet Packet ◽  
Cylinder Liner ◽  
Characteristic Parameter ◽  
Wear Process ◽  
Time Frequency ◽  
Vibration Behavior ◽  
Harmonic Wavelet ◽  
Value Decomposition ◽  
Frequency Features ◽  
Frictional Vibration

In this paper, the frictional vibration behavior under different wear states was investigated by the friction and wear experiments of the piston ring against the cylinder liner of marine diesel engine on CFT-I tester. The time-frequency features of frictional vibration were analyzed by harmonic wavelet packet transform (HWPT) and the variation of frictional vibration from running-in wear to steady wear and violent wear states was studied by defining characteristics parameter K using singular value decomposition (SVD). The result shows that the time-frequency features of frictional vibration vary with the wear time and can reflect the wear states of tribological pairs. The variation of characteristic parameter K of the frictional vibration is accordingly consistent with that of the friction coefficient and indicates that the wear progress of the tribological pair goes through various stages, namely, running-in wear, steady wear, and violent wear. Therefore, the frictional vibration can be used to predict the wear process and identify the wear states of tribological pairs.

scholarly journals Experimental Investigation on the Friction-Induced Vibration with Periodic Characteristics in a Running-In Process under Lubrication

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Di Sun ◽  
Pengfei Xing ◽  
Guobin Li ◽  
Hongtao Gao ◽  
Sifan Yang ◽  
...  
Keyword(s):  
Experimental Investigation ◽  
Friction Pair ◽  
Wavelet Packet ◽  
Mean Square ◽  
Oil Lubrication ◽  
Composite Surface ◽  
Wear Process ◽  
Time Frequency ◽  
Harmonic Wavelet ◽  
Friction Induced Vibration

This paper investigated the friction-induced vibration (FIV) behavior under the running-in process with oil lubrication. The FIV signal with periodic characteristics under lubrication was identified with the help of the squeal signal induced in an oil-free wear experiment and then extracted by the harmonic wavelet packet transform (HWPT). The variation of the FIV signal from running-in wear stage to steady wear stage was studied by its root mean square (RMS) values. The result indicates that the time-frequency characteristics of the FIV signals evolve with the wear process and can reflect the wear stages of the friction pairs. The RMS evolution of the FIV signal is in the same trend to the composite surface roughness and demonstrates that the friction pair goes through the running-in wear stage and the steady wear stage. Therefore, the FIV signal with periodic characteristics can describe the evolution of the running-in process and distinguish the running-in wear stage and the stable wear stage of the friction pair.

The Fault Diagnosis Method for Roller Bearing with Harmonic Wavelet Packet and DT-SVM

2015 ◽  
Vol 724 ◽  
pp. 238-241
Author(s):  
Rui Pan ◽  
Tao Xu ◽  
Yong Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Support Vector ◽  
Time Frequency ◽  
Vibration Data ◽  
Electric Engineering ◽  
Harmonic Wavelet ◽  
Diagnosis Method

This paper studies the roller bearing fault diagnosis method with harmonic wavelet packet and Decision Tree-Support Vector Machine (DT-SVM). The harmonic wavelet packet possesses better performances for its box-shaped spectrum and unlimited subdivision compared with the conventional time-frequency feature exaction method. Firstly, the proposed method decomposes the roller bearing vibration signal with harmonic wavelet packet and extracts the feature energy with coefficients of each spectrum. After the feature energy is normalized, feature vector are available. Based on multi-level binary tree, this paper designs the multi-classification SVM model due to its superior nonlinear mapping capability. Three two-classifications are incorporated to diagnosis the roller bearing faults. Finally, the proposed method is illustrated with the vibration data from the roller bearing stand of electric engineering lab in case western reserve university. Experimental results illustrate the higher accuracy of the proposed method compared with conventional method.

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

scholarly journals Time–Frequency Domain Characteristics of Acoustic Emission Signals and Critical Fracture Precursor Signals in the Deep Granite Deformation Process

2021 ◽  
Vol 11 (17) ◽  
pp. 8236
Author(s):  
Le Zhang ◽  
Hongguang Ji ◽  
Liyuan Liu ◽  
Jiwei Zhao
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
High Frequency ◽  
Wavelet Packet ◽  
Confining Pressure ◽  
Wavelet Packet Decomposition ◽  
Evolution Law ◽  
Time Frequency ◽  
Characteristic Signal ◽  
Failure Precursor

To study the crack evolution law and failure precursory characteristics of deep granite rocks in the process of deformation and failure under high confining pressure, granite samples obtained from a depth of 1150 m are tested using a TAW-2000 triaxial hydraulic servo testing machine and a PCI-II acoustic emission monitoring system. Based on the stress–strain curve and IET function, the loading process of the sample is divided into five stages: crack closure, linear elastic deformation, microcrack generation and development, macroscopic fracture generation and energy surge, and post-peak failure. The evolution trend and fracture evolution law of the acoustic emission signal event interval function in different stages are analyzed. In particular, the signals with an amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz are defined as the failure precursor signals before the rock reaches the peak stress. The defined precursor signal conditions agree well with the experimental results. The time–frequency analysis and wavelet packet decomposition of the precursor signal are performed on the extracted characteristic signal of the failure precursor. The results show that the time-domain signal is in the form of a continuous waveform, and the frequency-domain waveform has multi-peak coexistence that is mainly concentrated in the high-frequency region. The energy distribution obtained by the wavelet packet decomposition of the characteristic signal is verified with the frequency-domain waveform. The energy distribution of the signal is mainly concentrated in the 343.75–375 kHz frequency band, followed by the 281.25–312.5 kHz frequency band. The energy proportion of the high-frequency signal increases with the confining pressure.

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