scholarly journals Adaptive online terrain classification method for mobile robot based on vibration signals

2021 ◽  
Vol 18 (6) ◽  
pp. 172988142110620
Author(s):  
Mingming Wang ◽  
Liming Ye ◽  
Xiaoyun Sun
Keyword(s):  
Mobile Robot ◽  
Vibration Signal ◽  
Classification Model ◽  
Classification Method ◽  
Terrain Classification ◽  
Vibration Signals ◽  
Time Frequency ◽  
Combined Features ◽  
Different Dimensions ◽  
The Time Domain

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.

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 Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Toni Prahasto ◽  
Gang-Min Lim ◽  
Byeong-Keun Choi
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Thermal Images ◽  
Outer Race ◽  
Intelligent Machine ◽  
Machine Fault ◽  
The Time Domain

This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

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.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

Research of the Transient Signal Recognition Based on Empirical Mode Decomposition

2014 ◽  
Vol 548-549 ◽  
pp. 1173-1178
Author(s):  
Wan Jin Wang ◽  
Kui Feng Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Transient Signal ◽  
Transient Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Time Frequency Localization ◽  
Stationary Vibration ◽  
The Time Domain

This paper introduces the empirical mode decomposition (EMD) method of the basic theory, problems and means to solve. Apply the approach to mechanical vibration signal containing a transient pulse processing and analysis carried out, and the wavelet time-frequency analysis methods are compared, the results show that it can effectively decompose nonlinear and non-stationary vibration signals, and has a self-adaptive, and in the time domain and frequency domain have better resolution capabilities, and the component with a more clear physical meaning. Due to its diversity of showing the results, you can make further precise analysis of a single component, and the transient signals can be effectively recognized, and can locate mutation point in time, describing the time-frequency localization properties. EMD, transient signals, mechanical vibration

Mixed-Mode Signal Detection of Road Vehicle Vibration Using Hilbert-Huang Transform

2015 ◽  
Author(s):  
Julien Lepine ◽  
Michael Sek ◽  
Vincent Rouillard
Keyword(s):  
Mixed Mode ◽  
Vibration Signal ◽  
Significance Test ◽  
Mathematical Framework ◽  
Vehicle Speed ◽  
Road Vehicle ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Vibration

The Hilbert-Huang Transform (HHT) is a fully adaptive time-frequency analysis method which is applicable to nonlinear and nonstationary processes. However, this promising method is fairly new and its range of applications is not well known. Furthermore, its mathematical framework is not yet fully developed. So far, the HHT has yielded interesting results for many applications such as biomedical, geophysical, meteorological and health monitoring, but there is no evidence of its application on complex mixed-mode vibration signals. To fill that gap, this paper investigates the application of the HHT to detect the different modes of road vehicle vibration signals. These modes originate from road roughness variation and vehicle speed which create nonstationary random vibration. Other modes are due to road surface aberrations which create transient events and the engine and drive train system of the vehicle which create harmonic vibrations. The energy density/average period significance test based on the HHT is assessed to detect these modes. The results, based on purposefully created synthetic test signals, reveal the limitations and shortcomings of the HHT based technique to detect and separate the various components of the mixed-mode vibration signals such as vehicle vibration signal.

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.

Gearbox Fault Diagnosis Using Vibration Signal with Wavelet De-Noising

2011 ◽  
Vol 86 ◽  
pp. 735-738
Author(s):  
Zhi Feng Dong ◽  
Hui Cheng ◽  
Hui Jia Yang ◽  
Wei Fu ◽  
Ji Wei Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Sensor Data ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Fault Features ◽  
Vibration Signal Analysis ◽  
Simulation Signal ◽  
The Time Domain

This paper dealt with the gearbox fault diagnosis with vibration signal analysis. The vibration signals from experiment contained a lot of noises which result from motor, gears, bears and box, and were collected through accelerate sensor, data collector and computer. The wavelet de-noising stratification was used to de-noise the vibration signals before the frequency-domain analysis was done. The effects of the simulation signal de-noising was contrasted, and the noise cancellation the power spectrum estimation was carried out. The experimental and analytical results show that the different features are indicated with vibration signal of the normal gearbox and the signal with bolts loosened of the gearbox. The gearbox fault with bolts loosened can be diagnosed by extracting the time-domain fault features of vibration signals.

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