Study of Time-Frequency Order Tracking of Vibration Signals of Rotating Machinery in Changing State

2008 ◽  
Author(s):  
Xiaoping Zhao ◽  
Qingpeng Kong ◽  
Qingtao Guo
Keyword(s):  
Rotating Machinery ◽  
Vibration Signals ◽  
Time Frequency ◽  
Order Tracking

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

Multi-Dimensional Directional Spectrograms and Campbell (ZMOD) Diagrams for Rotating Machinery

1995 ◽  
Author(s):  
I. Bucher ◽  
D. J. Ewins ◽  
D. A. Robb ◽  
P. Schmiechen
Keyword(s):  
Rotating Machinery ◽  
Experimental Results ◽  
New Method ◽  
Frequency Content ◽  
Rotating Disc ◽  
Vibration Signals ◽  
Time Frequency ◽  
Spatial Response ◽  
Spatial Domains ◽  
Vibration Pattern

Abstract A new method for decomposition of vibration signals measured on rotating machinery is presented in this paper. The method uses a signal measured from a number of sensors to decompose the spatial response according to the direction of the progression of motion, the frequency content and the various wavelengths. For the simple case of shaft vibration, two sensors, horizontal and vertical, are used to separate the vibration pattern into forward and backward progressing components. For the case of a rotating disc, more sensors are required to further decompose the response into different wavelengths. This allows one to monitor and to identify potentially dangerous vibration patterns exhibiting large backward components. The method is shown to provide better resolution in the time-frequency (speed of rotation) and spatial domains by separating several, usually overlapping patterns. Several analytical and experimental results demonstrate the usefulness of the proposed method.

Fault Identification for Rotating Machinery Using Adaptive Signal Processing and Time-Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Signal Processing ◽  
Background Noise ◽  
Combustion Engine ◽  
Adaptive Signal Processing ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Abstract Impulsive sound and vibration signals in rotating machinery are often associated with faults which lead to due to irregular impacting. Thus these impulsive sound and vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive sounds embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within internal combustion engine and industrial gear.

Application of Harmonic Wavelet Analysis to Rubbing Vibration Signals for Rotating Machinery Fault Diagnosis

2013 ◽  
Vol 321-324 ◽  
pp. 1245-1248
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Wavelet Transform ◽  
Signal Analysis ◽  
Rotating Machinery ◽  
The Other ◽  
Vibration Signals ◽  
Time Frequency ◽  
Vibration Data ◽  
Turbo Generator ◽  
Harmonic Wavelet ◽  
Machinery Fault Diagnosis

Harmonic wavelet transform (HWT)and harmonic wavelet time-frequency profile plot (TFPP) is introduced firstly in practice to identify weak singularity in a signal with noise clearly. With TFPP method, emulational signal and vibration data of the rubbing of the large practical turbo-generator units are analyzed successfully, which prove that the method is effectively extract the rubbing signal feature which is can not gained by the other signal analysis methods, and the rubbing of the turbo-generator units is identified effectively.

scholarly journals The Rational Spline Interpolation Based-LOD Method and Its Application to Rotating Machinery Fault Diagnosis

2020 ◽  
Vol 10 (4) ◽  
pp. 1259
Author(s):  
Xiaorui Niu ◽  
Kang Zhang ◽  
Chao Wan ◽  
Xiangmin Chen ◽  
Lida Liao ◽  
...  
Keyword(s):  
Linear Transformation ◽  
Spline Interpolation ◽  
Piecewise Linear ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Time Frequency ◽  
Rational Spline ◽  
Characteristic Decomposition ◽  
Oscillatory Characteristic ◽  
Piecewise Linear Transformation

Local oscillatory-characteristic decomposition (LOD) is a relatively new self-adaptive time-frequency analysis methodology. The method, based on local oscillatory characteristics of the signal itself uses three mathematical operations such as differential, coordinate domain transform, and piecewise linear transform to decompose the multi-component signal into a series of mono-oscillation components (MOCs), which is very suitable for processing multi-component signals. However, in the LOD method, the computational efficiency and real-time processing performance of the algorithm can be significantly improved by the use of piecewise linear transformation, but the MOC component lacks smoothness, resulting in distortion. In order to overcome the disadvantages mentioned above, the rational spline function that spline shape can be adjusted and controlled is introduced into the LOD method instead of the piecewise linear transformation, and the rational spline-local oscillatory-characteristic decomposition (RS-LOD) method is proposed in this paper. Based on the detailed illustration of the principle of RS-LOD method, the RS-LOD, LOD, and empirical mode decomposition (EMD) are compared and analyzed by simulation signals. The results show that the RS-LOD method can significantly improve the problem of poor smoothness of the MOC component in the original LOD method. Moreover, the RS-LOD method is applied to the fault feature extraction of rotating machinery for the multi-component modulation characteristics of rotating machinery fault vibration signals. The analysis results of the rolling bearing and fan gearbox fault vibration signals show that the RS-LOD method can effectively extract the fault feature of the rotating mechanical vibration signals.

Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
T. Y. Wu ◽  
Y. L. Chung ◽  
C. H. Liu
Keyword(s):  
Significance Test ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Test Bed ◽  
Statistical Concept ◽  
Transform Method ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Fault Indicator

The objective of this research in this paper is to investigate the feasibility of utilizing the Hilbert–Huang transform method for diagnosing the looseness faults of rotating machinery. The complicated vibration signals of rotating machinery are decomposed into finite number of intrinsic mode functions (IMFs) by integrated ensemble empirical mode decomposition technique. Through the significance test, the information-contained IMFs are selected to form the neat time-frequency Hilbert spectra and the corresponding marginal Hilbert spectra. The looseness faults at different components of the rotating machinery can be diagnosed by measuring the similarities among the information-contained marginal Hilbert spectra. The fault indicator index is defined to measure the similarities among the information-contained marginal Hilbert spectra of vibration signals. By combining the statistical concept of Mahalanobis distance and cosine index, the fault indicator indices can render the similarities among the marginal Hilbert spectra to enhanced and distinguishable quantities. A test bed of rotor-bearing system is performed to illustrate the looseness faults at different mechanical components. The effectiveness of the proposed approach is evaluated by measuring the fault indicator indices among the marginal Hilbert spectra of different looseness types. The results show that the proposed diagnosis method is capable of classifying the distinction among the marginal Hilbert spectra distributions and thus identify the type of looseness fault at machinery.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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