Study on Characteristics Location of Pantograph–Catenary Contact Force Signal Based on Wavelet Transform

2019 ◽  
Vol 68 (2) ◽  
pp. 402-411 ◽  
Author(s):  
Jian Zhang ◽  
Wenzheng Liu ◽  
Zongfang Zhang
Keyword(s):  
Wavelet Transform ◽  
Contact Force ◽  
Force Signal

scholarly journals Reconstruction of an informative railway wheel defect signal from wheel–rail contact signals measured by multiple wayside sensors

2018 ◽  
Vol 233 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Alireza Alemi ◽  
Francesco Corman ◽  
Yusong Pang ◽  
Gabriel Lodewijks
Keyword(s):  
Contact Force ◽  
Ad Hoc ◽  
Impact Load ◽  
Fusion Model ◽  
Informative Signal ◽  
Railway Systems ◽  
Multiple Sensors ◽  
Reconstructed Signal ◽  
Force Signal ◽  
Defect Signal

Wheel impact load detectors are widespread railway systems used for measuring the wheel–rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.

Experimental Study of Normal Contact Force Between a Rolling Pneumatic Tyre and a Single Asperity

2017 ◽  
Vol 09 (06) ◽  
pp. 1750081 ◽  
Author(s):  
Yuan-Fang Zhang ◽  
Julien Cesbron ◽  
Hai-Ping Yin ◽  
Michel Bérengier
Keyword(s):  
Contact Force ◽  
Numerical Models ◽  
Force Transducer ◽  
Contact Forces ◽  
Exterior Surface ◽  
Normal Contact ◽  
Single Asperity ◽  
Direct Measurements ◽  
Time Signals ◽  
Force Signal

This paper proposes a novel experimental test apparatus that permits direct measurements of tyre/asperity normal contact forces under rolling conditions without interfacial layer. A reduced-sized pneumatic tyre is set rolling on the exterior surface of a cylindrical test rig simulating a smooth road surface except a single asperity of simple geometric shape connected to an embedded force transducer. Distinct asperity geometries lead to similar shapes of force signal but different magnitudes whose relationships with the indentation have exponents close to those in classical analytical solutions. By analyzing the time signals of the contact force and their frequency contents for different rolling speeds, the quasi-static nature of the contact, commonly assumed in numerical models, is verified.

SWT-BASED FORCE SENSOR DE-NOISING FOR THE NEUROSURGICAL ROBOT

2009 ◽  
Vol 06 (02) ◽  
pp. 73-90 ◽  
Author(s):  
WEIMIN SHEN ◽  
JASON GU
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Mechanical Vibration ◽  
Force Sensor ◽  
Critical Importance ◽  
Information Analysis ◽  
Stationary Wavelet Transform ◽  
Remote Site ◽  
Force Signal ◽  
Neurosurgical Robot

When our proposed neurosurgical robot is applied, the neurosurgeon usually wants to sense the force on the remote site to operate on patients. The force signal analysis is of critical importance for neurosurgeons to perform stable, reliable, and safe operations. In this paper, based on the stationary wavelet transform (SWT), force information analysis and process is designed. Since force sampled by the JR3 sensor contains noise from the sensor and mechanical vibration when drilling, to smooth the force signal sent to the operator, SWT-based force information de-noising is proposed to reduce the noise significantly, especially for the force along the x and y axes. Simulations and experiments further verified the proposed research.

Wavelet Transform Denoise of PCB Drilling Force Signal

2012 ◽  
Vol 500 ◽  
pp. 26-31 ◽  
Author(s):  
Yun Peng Qu ◽  
Cheng Yong Wang ◽  
Li Juan Zheng ◽  
Yue Xian Song
Keyword(s):  
Wavelet Transform ◽  
White Noise ◽  
Wavelet Packet ◽  
Simple Calculation ◽  
Drilling Force ◽  
Noise Interference ◽  
Force Signal ◽  
Micro Drilling ◽  
Non Stationary Signal ◽  
Micro Drill

In the PCB micro drilling, because the force signal is tiny, and when the micro-drill drill to a certain degree of multilayer PCB, alternate force signals will not appear obvious, through to the drilling force signal analysis, we can know the drill bit position and the materials to the influence of the drill failure, so the drilling force signals denoise seems extremely important. In the processing of the non-stationary signal, traditional signal processing method has a certain extent of insufficient, using the wavelet packet decomposition signal, the white noise variance and amplitude decrease with the increase of wavelet scales, but the signal variance and amplitude has nothing to do with the wavelet transform. According to the view of the signal energy, first of all, we make the multiscale decomposition of the signal, then, by using some of the wavelet packet that has efficient energy to reconstruct the original signal. Comparing with the traditional threshold denoising ,using this method in the test signal to deal with the noise can effectively eliminate the white noise interference, and has good denoising effects besides the simple calculation.

scholarly journals A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yongzhi Wang ◽  
Sicheng Zhu ◽  
Qian Zhang ◽  
Ran Zhou ◽  
Rutong Dou ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Contact Force ◽  
Robotic System ◽  
Working Environment ◽  
Human Intervention ◽  
Training Process ◽  
Assembly Time ◽  
Research Designs ◽  
Force Signal ◽  
Attitude Alignment

The adjustment times of the attitude alignment are fluctuated due to the fluctuation of the contact force signal caused by the disturbing moments in the compliant peg-in-hole assembly. However, these fluctuations are difficult to accurately measure or definition as a result of many uncertain factors in the working environment. It is worth noting that gravitational disturbing moments and inertia moments significantly impact these fluctuations, in which the changes of the peg concerning the mass and the length have a crucial influence on them. In this paper, a visual grasping strategy based on deep reinforcement learning is proposed for peg-in-hole assembly. Firstly, the disturbing moments of assembly are analyzed to investigate the factors for the fluctuation of assembly time. Then, this research designs a visual grasping strategy, which establishes a mapping relationship between the grasping position and the assembly time to improve the assembly efficiency. Finally, a robotic system for the assembly was built in V-REP to verify the effectiveness of the proposed method, and the robot can complete the training independently without human intervention and manual labeling in the grasping training process. The simulated results show that this method can improve assembly efficiency by 13.83%. And, when the mass and the length of the peg change, the proposed method is still effective for the improvement of assembly efficiency.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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