scholarly journals Wavelet Transform in Vibration Analysis for Mechanical Fault Diagnosis

1996 ◽  
Vol 3 (1) ◽  
pp. 17-26 ◽  
Author(s):  
W.J. Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Vibration Analysis ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Vibration Signals ◽  
Time Frequency ◽  
Special Cases ◽  
Mechanical Faults ◽  
Short Time

The wavelet transform is introduced to indicate short-time fault effects in associated vibration signals. The time-frequency and time-scale representations are unified in a general form of a three-dimensional wavelet transform, from which two-dimensional transforms with different advantages are treated as special cases derived by fixing either the scale or frequency variable. The Gaussian enveloped oscillating wavelet is recommended to extract different sizes of features from the signal. It is shown that the time-frequency and time-scale distributions generated by the wavelet transform are effective in identifying mechanical faults.

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.

Decomposition of Event-Related Brain Potentials into Multiple Functional Components Using Wavelet Transform

2001 ◽  
Vol 32 (3) ◽  
pp. 122-138 ◽  
Author(s):  
Tamer Demiralp ◽  
Ahmet Ademoglu
Keyword(s):  
Wavelet Transform ◽  
Three Dimensional ◽  
Experimental Session ◽  
Brain Potentials ◽  
Time Frequency ◽  
Similar Characterization ◽  
Component Identification ◽  
Frequency Components ◽  
Neurophysiological Mechanisms ◽  
Functional Components

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

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 Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization

2019 ◽  
Vol 9 (18) ◽  
pp. 3642
Author(s):  
Lin Liang ◽  
Haobin Wen ◽  
Fei Liu ◽  
Guang Li ◽  
Maolin Li
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Mechanical Equipment ◽  
Tensor Factorization ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Value Decomposition ◽  
Short Time

The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional feature extraction techniques, such as bandpass filtering and time-frequency analysis, are suitable for matrix processing but challenged by the higher-order data. To tackle these problems, a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization is presented in this paper. Primarily, the phase space reconstruction and the short time Fourier transform are successively employed to convert the original signal into time-frequency distributions, which are further arranged into a three-way tensor to obtain a time-frequency multi-aspect array. The tensor is decomposed by sparse non-negative tensor factorization via hierarchical alternating least squares algorithm, after which the latent components are reconstructed from the factors by the inverse short time Fourier transform and eventually help extract the impulse feature through envelope analysis. For performance verification, the experimental analysis on the bearing datasets and the swashplate piston pump has confirmed the effectiveness of the proposed method. Comparisons to the traditional methods, including maximum correlated kurtosis deconvolution, singular value decomposition, and maximum spectrum kurtosis, also suggest its better performance of feature extraction.

Stationarity test of vibration signals with surrogate data and time–frequency analysis

2016 ◽  
Vol 20 (8) ◽  
pp. 1143-1154
Author(s):  
Zuo-Cai Wang ◽  
Feng Wu ◽  
Wei-Xin Ren
Keyword(s):  
Frequency Analysis ◽  
Surrogate Data ◽  
Transform Method ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Stationarity Test ◽  
Modulated Signals ◽  
Short Time

The stationarity test of vibration signals is critical for the extraction of the signal features. In this article, the surrogate data with various time–frequency analysis methods are proposed for stationary test of vibration signals. The surrogate data are first generated from the Fourier spectrum of the original signal with keeping the magnitude of the spectrum unchanged and replacing its phase by a random sequence. The local and global spectra of the original signal and the surrogate data are then estimated by four time–frequency analysis methods, which are short-time Fourier transform, multitaper spectrograms, wavelet transform, and S-transform methods. The index of nonstationarity is then defined based on the distances between the local and global spectra. Three kinds of synthetic signals, which are stationary signals, frequency-modulated signals, and amplitude-modulated signals, are tested to compare the efficiency of the four time–frequency analysis methods as mentioned. The results show that with a certain observation scale value, the index of nonstationarity based on the short-time Fourier transform or wavelet transform method may fail to test the stationarity of the signal. The parametric studies and sensitivity analysis of the observation scale and noise-level effect are also extensively conducted. The results show that the index of nonstationarity calculated using the multitaper spectrograms’ method is more suitable for stationarity test of frequency-modulated signals, while the index of nonstationarity calculated using the S-transform method is more suitable for stationarity test of amplitude-modulated signals. The results also show that the noise has a significant effect on the stationarity test results. Finally, the stationarity of a real vibration signal measured from a cable is tested, and the results show that the proposed index of nonstationarity can effectively test the stationarity of real 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.

Three-Dimensional Mesh Model Watermarking Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1481-1486
Author(s):  
Xin Li ◽  
Yi Ping Tian
Keyword(s):  
Wavelet Transform ◽  
Copyright Protection ◽  
Control Point ◽  
Three Dimensional ◽  
Digital Watermark ◽  
Two Dimensional ◽  
Mesh Model ◽  
Watermark Embedding ◽  
Three Dimensional Models ◽  
Watermark Algorithm

Watermark information is embedded in three-dimensional mesh model through three-dimensional watermarking algorithm for effective copyright protection. The widely use of three-dimensional grid model attracts more attention on the copyright protection. The digital watermark algorithm with the NURBS model based on the wavelet transform aims to get the virtual grayscale images using the control point coordinate. Then we can embed the watermark into the virtual gray image watermark. It can change the three-dimensional models into two-dimensional images. And this algorithm can enhance the operability and simplicity of the watermark embedding. Experiments show that the proposed algorithm is easy to implement, simple in principle, and the extracted watermark is clearly visible, moreover, the model does not need to be directly modified, so it has good robustness. Watermarked model does not change in the visual, it has good invisibility.

Spectral decomposition of seismic data with continuous-wavelet transform

Geophysics ◽  
2005 ◽  
Vol 70 (6) ◽  
pp. P19-P25 ◽  
Author(s):  
Satish Sinha ◽  
Partha S. Routh ◽  
Phil D. Anno ◽  
John P. Castagna
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Scale ◽  
Fixed Time ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Window Length ◽  
Time Frequency ◽  
Frequency Map

This paper presents a new methodology for computing a time-frequency map for nonstationary signals using the continuous-wavelet transform (CWT). The conventional method of producing a time-frequency map using the short time Fourier transform (STFT) limits time-frequency resolution by a predefined window length. In contrast, the CWT method does not require preselecting a window length and does not have a fixed time-frequency resolution over the time-frequency space. CWT uses dilation and translation of a wavelet to produce a time-scale map. A single scale encompasses a frequency band and is inversely proportional to the time support of the dilated wavelet. Previous workers have converted a time-scale map into a time-frequency map by taking the center frequencies of each scale. We transform the time-scale map by taking the Fourier transform of the inverse CWT to produce a time-frequency map. Thus, a time-scale map is converted into a time-frequency map in which the amplitudes of individual frequencies rather than frequency bands are represented. We refer to such a map as the time-frequency CWT (TFCWT). We validate our approach with a nonstationary synthetic example and compare the results with the STFT and a typical CWT spectrum. Two field examples illustrate that the TFCWT potentially can be used to detect frequency shadows caused by hydrocarbons and to identify subtle stratigraphic features for reservoir characterization.

Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery

2014 ◽  
Vol 684 ◽  
pp. 124-130
Author(s):  
Hong Li ◽  
Qing He ◽  
Zhao Zhang
Keyword(s):  
Frequency Analysis ◽  
Local Characteristic ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Short Time

There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.

Selection of Mother Wavelet for Effective Wavelet Transform of Bearing Vibration Signals

2014 ◽  
Vol 1039 ◽  
pp. 169-176 ◽  
Author(s):  
H.S. Kumar ◽  
P. Srinivasa Pai ◽  
N.S. Sriram ◽  
G.S. Vijay
Keyword(s):  
Wavelet Transform ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Variable Load ◽  
Mother Wavelet ◽  
Vibration Signals ◽  
Time Frequency ◽  
Deep Groove ◽  
Wide Range ◽  
Bearing Vibration

Condition monitoring (CM) and fault diagnosis of equipments has gained greater attention in recent years, due to the need to reduce the down time and enhance the life/ condition of the equipments. The rolling element bearings (REB) are the most critical components in rotary machines. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance activity. Vibration signal analysis provides wide range of information for analysis. So in this paper, vibration signals for four conditions of a deep groove ball bearing namely Normal (N), bearing with defect on inner race (IR), bearing with defect on ball (B), and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under maximum speed and variable load conditions. Depending on the machinery operating conditions and the extent of bearing defect severity, the measured vibration signals are non-stationary in nature. Non-stationary signals are effectively analyzed by wavelet transform technique, which is a popular and widely used time-frequency technique. The focus of this paper is to select a best possible mother wavelet for applying WT on bearing vibration signals. The two selection criteria includes minimum Shannon entropycriteria(MSEC) and Maximum Energy to Shannon Entropy Ratio criteriaR(s). This helps in effective bearing CM using WT.

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