Embedded Sensing Design of Gearboxes for Vibration Monitoring

2010 ◽  
Vol 29-32 ◽  
pp. 264-268
Author(s):  
Z.S. Chen ◽  
Yong Min Yang ◽  
Z.X. Ge ◽  
C. Li
Keyword(s):  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Embedded Sensors ◽  
Analysis Method ◽  
Signal To Noise ◽  
Normal Functions ◽  
Embedded Sensing ◽  
Vibration Signal Analysis

Vibration signal analysis is one of the most effective ways for condition monitoring of gearboxes. Traditional way is often to mount additional accelerometer sensors on their cases, which has two unavoidable defects: signal-to-noise ratio is often low due to long signal travel paths and it may be not allowable due to space limitations. While embedded diagnostics (ED) can solve these two problems well by embedding sensors close to fault sources. However, embedded sensing design is a great challenge of ED because embedded sensors must have effects on the structure integrity of a gearbox. So it is necessary to determine how to embed sensors in order to ensure normal functions of a gearbox. In this paper, a finite element-based structure analysis method was proposed to perform embedded sensing design of bearings and gears to determine the optimal modified structure size.

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

The Damped Eigenfrequency Analysis for an Elastic Disk Clamped by a Water-Filled Cylinder

2012 ◽  
Vol 490-495 ◽  
pp. 3742-3747
Author(s):  
Zhi Xi Yang
Keyword(s):  
Signal Analysis ◽  
Effective Means ◽  
Three Dimensional ◽  
Vibration Signal ◽  
Analysis Method ◽  
Obvious Effect ◽  
Coupled Numerical Model ◽  
Elastic Disk ◽  
Vibration Signal Analysis ◽  
Signal Analysis Method

A vibroacoustic testing model in laboratory for the damped eigenfrequencies and eigenmodes is introduced in this paper. The unsymmetric (u, p) variational formulas are implemented for three dimensional structures based on the elastodynamic displacement field u and the fluid acoustic pressure field p. The damping coefficients of materials seem to have no obvious effect on the coupled numerical model. Then the damped eigenfrequencies can alternately be obtained by vibration signal analysis method. The Fast Fourier Transform for the spectrum domain analysis illustrates an effective means to evaluate the damped eigenfrequencies.

Research on a vibration signal analysis method for motor bearing

Optik ◽  
2016 ◽  
Vol 127 (20) ◽  
pp. 10014-10023 ◽  
Author(s):  
Huimin Zhao ◽  
Wu Deng ◽  
Xinhua Yang ◽  
Xiumei Li
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Analysis Method ◽  
Vibration Signal Analysis ◽  
Signal Analysis Method ◽  
Motor Bearing

Progress in the Application of Wavelet Theory to Vibration Analysis

1995 ◽  
Author(s):  
David E. Newland
Keyword(s):  
Wavelet Analysis ◽  
Signal Analysis ◽  
Numerical Algorithms ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Transient Signal ◽  
Frequency Content ◽  
Wavelet Theory ◽  
Local Functions ◽  
Vibration Signal Analysis

Abstract For vibration signal analysis, the objective is usually to extract frequency data from a signal and study how the signal’s frequency content changes with time. Because wavelets are local functions of time, each with a predetermined frequency content, wavelet analysis provides a good means of doing this. As a result, practical wavelet analysis is growing rapidly. There are many different wavelets to use but no accepted procedure for choosing between them. This paper discusses various alternative wavelets for practical calculations and describes two of the key numerical algorithms. Examples of recent applications using these algorithms are reviewed, including vibration monitoring and detection, transient signal analysis and denoising.

A Bearing Fault Diagnosis Using Wavelet Envelope Spectrum Based on Full Vector Spectrum Technology

2012 ◽  
Vol 190-191 ◽  
pp. 873-879 ◽  
Author(s):  
Xiao Yun Gong ◽  
Jie Han ◽  
Hong Chen ◽  
Wen Ping Lei
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Bearing Vibration ◽  
Vibration Signal Analysis ◽  
Envelope Spectrum ◽  
Signal Analysis Method ◽  
Full Vector

Wavelet envelope demodulation method can distinguish the fault information from complex bearing vibration signal. However, traditional signal analysis method, which is solely based on a single source data, is imperfect. In this paper, an approach to wavelet packet and envelope analysis based on full vector spectrum technology was proposed. Firstly, two different data from the same source were respectively decomposed and recomposed by wavelet packet transform. Then, in order to improve the accuracy of detecting fault, the recomposed signals were merged by using the full vector spectrum method. Compared to the traditional signal analysis method, the advantage of the new method is presented by showing their application to bearings. Finally, results from the bearing vibration signal analysis show that the new approach is more effective because of its inheritance and all-sided feature.

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