A Method of Measurement and Monitoring to the Train's Vibration Frequency Spectrum-Varying Fault

2010 ◽  
Vol 36 ◽  
pp. 96-102 ◽  
Author(s):  
Jing Wang ◽  
Te Fang Chen ◽  
Cai Lun Huang ◽  
Qian Cheng Zhao
Keyword(s):  
Frequency Spectrum ◽  
Vibration Frequency ◽  
Sampling Method ◽  
Vibration Signal ◽  
Periodic Signal ◽  
Signal Acquisition ◽  
Speed Fluctuation ◽  
Spectral Analysis Method ◽  
Spectral Smearing ◽  
Vibration Frequency Spectrum

In condition monitoring of train, because of load change, speed fluctuation and other effect factors, it may cause vibration frequency spectrum migration. If using the common vibration signal acquisition and spectral analysis method to this fault, there will be a result of characteristic spectral smearing, which will lead to misdiagnosis or missed diagnosis. To solve this problem, the vibration mechanism of spectrum-varying fault was discussed. This paper studies a pulse-triggered sampling method of integral periodic signal, the equal angle re-sampling method and other appropriate signal processing method. The feasibility and validity of this method and technology has been verified by the application in Liuzhou Railway Bureau .

scholarly journals Indonesian Wood as Material for Acoustic Guitars and Violins

2017 ◽  
Vol 3 (1) ◽  
pp. 11-17
Author(s):  
Indraswari Kusumaningtyas ◽  
Subagio Subagio
Keyword(s):  
Frequency Spectrum ◽  
Vibration Frequency ◽  
Sound Radiation ◽  
Damping Factor ◽  
Suitable Alternative ◽  
Acoustical Properties ◽  
Local Material ◽  
Radiation Coefficient ◽  
Back Plate ◽  
Vibration Frequency Spectrum

Traditionally, acoustic guitars and violins are made from European woods. Spruce is most preferred for the top plate (soundboard), whereas maple, sycamore and rosewood are often used for the back plate. However, these woods are not easily available in Indonesia. In this paper, we present a study on the suitability of a selection of Indonesian woods, namely acacia, mahogany, pine, sengon and sonokembang, as materials for acoustic guitars and violins. The most important acoustical properties for selecting materials for musical instruments, i.e. the speed of sound, the sound radiation coefficient and the damping factor, were investigated. Furthermore, the performance of pine and mahogany were tested by making them into a violin and a guitar. The vibration frequency spectrum and the damping factor of the top plate were measured. The results show that the acoustical characteristics of mahogany are very close to those of maple and still quite close to those of Indian rosewood, which makes it a very suitable local material for back plates. Pine has quite similar acoustical characteristics to spruce. Although its sound radiation coefficient is slightly lower, its aesthetic appeal and workability makes pine a suitable alternative for top plates. However, instruments with pine top plates exhibit different tonal colour compared to instruments with spruce top plates, due to some differences in the vibration frequency spectrum. Furthermore, the generally higher damping factors of pine and mahogany compared to those of the European woods should be taken into account, because they affect the sustain-time of the generated sound.

Experiment and Test Signal Analysis Based on Speed Fluctuation of Torsional Vibration of Large-Scale Rotary Machinery

2011 ◽  
Vol 346 ◽  
pp. 501-507
Author(s):  
Wu Zhao ◽  
Dan Huang
Keyword(s):  
Frequency Spectrum ◽  
Torsional Vibration ◽  
Large Scale ◽  
Fourier Transforms ◽  
Stable State ◽  
Signal Acquisition ◽  
Rotating Speed ◽  
Rotary Machinery ◽  
Speed Fluctuation ◽  
Short Time

A new mode of fault monitoring and controling methods on rotating speed fluctuation was proposed in this study. Torsional vibration model and identification equation of speed fluctuation of large-scale rotary machinery was established based on a two-mass motor driving model. Tachogenerator was adopted to measure speed fluctuation in torsional vibration experiment of large-scale rotary machinery. According to the short time fourier transforms method, the non-steady cyclical or quasi-cyclical characteristics signal of rotating speed fluctuation on elastic shafts were transformed into steady signal to study in a fixed time window function. The methods of monitoring rotating speed fluctuation developed nonlinear stable state signal processing into linear short time fourier transforms signal. The real rotating speed fluctuation solution could be obtained after the data of signal acquisition post-processing by the methods of frequency spectrum analysis and modal analysis. Based on data of signal acquisition, using the methods of fourier phase frequency spectrum, logarithm amplitude frequency spectrum, and self-power spectrum, the quantitative expression under the quantitative analysis stable state was obtained. Through the introduction of realtime signal on rotating speed fluctuation to feedback control system, it is easy to program to realize the real-time on-line torsional vibration monitor of the complex mechanism transmission system on the large scale rotating machinery.

Research on Shearer Rocker Gearbox Fault Location Based on Wavelet Envelope Demodulation

2014 ◽  
Vol 528 ◽  
pp. 210-216
Author(s):  
Zeng Qiang Wang ◽  
Hong Wei Ma ◽  
Mei Hua Tao ◽  
Xu Hui Zhang ◽  
Qing Hua Mao
Keyword(s):  
Fourier Transform ◽  
Frequency Spectrum ◽  
Spectrum Analysis ◽  
Fault Location ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Frequency Spectrum Analysis ◽  
Mechanical Faults ◽  
Faults Diagnosis ◽  
Feature Frequency

To solve the problem of faults location for shearer rocker gearbox, the multiple sites vibration signal of faulty rocker gearbox are collected, as well as the Morlet wavelet envelope demodulation is applied to demodulate vibration signal and Fourier transform is used to carry out frequency spectrum analysis of vibration signal. Experimental results show that this method can effectively extract the faults feature frequency from complex vibration signal. The faults location result is consistent with actual faults part. This mean realizes to locate faults accurately. It provides an effective method for mechanical faults diagnosis of shearer.

scholarly journals Learning to See the Vibration: A Neural Network for Vibration Frequency Prediction

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2530 ◽  
Author(s):  
Jiantao Liu ◽  
Xiaoxiang Yang
Keyword(s):  
Neural Network ◽  
Vibration Frequency ◽  
Frequency Estimation ◽  
Vibration Signal ◽  
Frequency Measurement ◽  
Optical Methods ◽  
Vibration Measurement ◽  
Vibration Signals ◽  
Engineering Practices ◽  
To Receive

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

Rotor Crack Detection Based on Multi-Vibration Signal Fusion Collected from the Basement of Machinery Using SVM and Statistical Characteristics Methodology

2010 ◽  
Vol 34-35 ◽  
pp. 1000-1004
Author(s):  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang ◽  
T. Zhang
Keyword(s):  
Fault Diagnosis ◽  
Crack Detection ◽  
Feature Fusion ◽  
Vibration Signal ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Signal Acquisition ◽  
Multiple Sensors ◽  
Signal Fusion ◽  
Diagnosis Method

As the poor generability of special sensor support frame and the inconvenience of signal acquisition in the process of common fault diagnosis for cracked rotor, a new fault diagnosis method is presented in this paper. this method takes the basement of rotor test rig as the monitoring objects and makes feature fusion for time-domain statistics of multiple sensors using SVM (support vector machine). The result of experiment showed that the method using the multi-sensor signal fusion technology collected from the basement of machinery has the advantages of better diagnostic precision for rotor crack diagnosis, furthermore, it supplies a new way for rotor fault diagnosis.

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