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.

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 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.

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.

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.

scholarly journals A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Guoping An ◽  
Qingbin Tong ◽  
Yanan Zhang ◽  
Ruifang Liu ◽  
Weili Li ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Mode Number ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Bandwidth Control ◽  
Bearing Vibration ◽  
Optimal Mode

Reliable fault diagnosis of the rolling element bearings highly relies on the correct extraction of fault-related features from vibration signals in time-frequency analysis. However, considering the nonlinear, nonstationary characteristics of vibration signals, the extraction of fault features hidden in the heavy noise has become a challenging task. Variable mode decomposition (VMD) is an adaptive, completely nonrecursive method of mode variation and signal processing. This paper analyzes the advantages of VMD compared with EMD in robustness of against noise, overcoming the end effect and mode aliasing. The signal decomposition performance of VMD algorithm largely depends on the selection of mode number k and bandwidth control parameter α. To realize the adaptability of influence parameters and the improvement of decomposition accuracy, a parameter-optimized VMD method is presented. The random frog leaping algorithm (SFLA) is used to search the optimal combination of influence parameters, and the mode number and bandwidth control parameters are set according to the search results. A multiobjective evaluation function is constructed to select the optimal mode component. The envelope spectrum technique is used to analyze the optimal mode component. The proposed method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

scholarly journals A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Quanbo Lu ◽  
Mei Li
Keyword(s):  
Noise Reduction ◽  
Fractal Analysis ◽  
Single Channel ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Vibration Signals ◽  
Time Frequency ◽  
Engineering Vibration ◽  
Ica Algorithm ◽  
Vibration Noise

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

Identification and Modeling of Vibration Signals in Startup Stage of Electric Driven Reciprocating Pump

2012 ◽  
Vol 184-185 ◽  
pp. 256-262
Author(s):  
Miao Rong Lv ◽  
Mei Li ◽  
Shi Gang Shen ◽  
Bao Jian Wei
Keyword(s):  
Time Consistency ◽  
Vibration Signal ◽  
Good Time ◽  
Vibration Signals ◽  
Time Frequency ◽  
Characteristic Extraction ◽  
Signal Simulation ◽  
Vibration Signal Analysis ◽  
Reciprocating Pump ◽  
Moving Parts

How to realize signal modeling and vibration signal characteristic extraction is a very significant topic. A large amount of drilling pump vibration signals were acquired from the indoor tests. The startup signals with time consistency were segmented from these measurement signals and analyzed in detail. There are mainly such five types of vibrations in the startup signals as the pump body’s vibration, whistle, shocks in moving parts, and impacts of the value lifted off or dropped on the seat, friction or grinding between moving parts. The pump body’s vibration and whistle have good time-frequency characteristics and change very regularity, which are defined as the startup vibration in this paper. The pump body’s vibration signals are modeled by OFMM method. After to exclude the OFMM modeling signal, the remaining signal was separated into different integrated components according to their vibration sources by PFM method, a HMM whistle vibration model based on PFM parameters was achieved. Furthermore, A combination of OFMM and HMM model is used to describe the pump startup vibration. Realistic simulation on the pump’s startup vibration has been achieved. Signal simulation was also carried out by use of this combination model. This approach is expected to become a powerful tool for drilling pump’s startup vibration signal analysis and modeling.

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.

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