Wavelet domain principal feature analysis for spindle health diagnosis

2011 ◽  
Vol 10 (6) ◽  
pp. 631-642 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X Gao
Keyword(s):  
Wavelet Packet ◽  
Principal Component ◽  
Test System ◽  
Wavelet Packet Transform ◽  
Sensor Data ◽  
Feature Analysis ◽  
Statistical Parameters ◽  
Signal Processing Technique ◽  
Frequency Bands ◽  
Principal Feature

This article introduces a hybrid signal processing technique for spindle health monitoring and diagnosis, through the integration of wavelet packet transform and principal feature analysis. Vibration signals measured from a spindle test system with different defect conditions are first decomposed into multiple sub-frequency bands by means of the wavelet packet transform. Statistical parameters such as energy and Kurtosis of these sub-frequency bands are then calculated. Subsequently, Principal Feature Analysis, which is an extension of the Principal Component Analysis, is performed on the statistical parameters to aid in the selection of the most representative features, which can be distinctively separated from each other, as inputs to a diagnostic classifier. Experimental analysis of sensor data measured from the spindle test system has verified the effectiveness of the developed technique.

Generation of critical aftershocks using stochastic neural networks and wavelet packet transform

2019 ◽  
Vol 26 (5-6) ◽  
pp. 331-351
Author(s):  
Elham Rajabi ◽  
Gholamreza Ghodrati Amiri
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Response Spectrum ◽  
Wavelet Packet ◽  
Principal Component ◽  
Response Spectra ◽  
Component Analysis ◽  
Wavelet Packet Transform ◽  
Stochastic Neural Networks ◽  
Transform Coefficients

This paper proposes a methodology using wavelet packet transform, principal component analysis, and neural networks in order to generate artificial critical aftershock accelerograms which are compatible with the response spectra. This procedure uses the learning abilities of neural networks, principal component analysis as a dimension reduction technique, and decomposing capabilities of wavelet packet transform on consecutive earthquakes. In fact, the proposed methodology consists of two steps and expands the knowledge of the inverse mapping from mainshock response spectrum to aftershock response spectrum and aftershock response spectrum to wavelet packet transform coefficients of the aftershocks. This procedure results in a stochastic ensemble of response spectra of aftershock (first step) and corresponding wavelet packet transform coefficients (second step) which are then used to generate the aftershocks through applying the inverse wavelet packet transform. Finally, in order to demonstrate the effectiveness of the proposed method, three examples are presented in which recorded critical successive ground motions are used to train and test the neural networks.

Non-Stationary Harmonics Analysis Based on Wavelet Packet Transform and Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 1733-1737
Author(s):  
Wen Liang Zhao ◽  
Hong Song ◽  
Quan Pan ◽  
Ling Tang
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Frequency Spectrum ◽  
Wavelet Packet ◽  
Effective Means ◽  
Wavelet Packet Transform ◽  
Analysis Method ◽  
Software Simulation ◽  
Frequency Bands ◽  
Noise Interference

The method for analysis of stationary harmonics in power system is FFT, but it is unsuitable for non-stationary harmonics. Because of the feature that non-stationary harmonics’ frequency spectrum has a certain bandwidth and with some noise interference usually. A new method for detection, based on wavelet packet transform and neural network was presented in this paper. This method improved the traditional wavelet analysis method. The non-stationary harmonics were decomposed in different frequency bands by wavelet packet transform at first, and then complete the analysis of the non-stationary harmonic in different frequency bands. Through software simulation, the analysis results show that, the method has better accuracy, and provided an effective means for analyzing non-stationary harmonics.

Wavelet Packet Transform Method and its Application in Analysing Ocean Signals

2012 ◽  
Vol 239-240 ◽  
pp. 1142-1147
Author(s):  
Shi Jun He ◽  
Wen Jun Zhou
Keyword(s):  
Storm Surge ◽  
Wavelet Packet ◽  
Linear Method ◽  
Wavelet Packet Transform ◽  
Suggested Method ◽  
Tidal Level ◽  
Transform Method ◽  
Frequency Bands ◽  
Wavelet Packet Transformation ◽  
Meaningful Data

In the marine study field, it always analysis the kinds of acquisition signals to extract the meaningful data. In this paper, a method of wavelet packet transformation (WPT) is suggested to the analytical ocean signals, such as the tidal level in the event of typhoon. It's known that the tidal level contains different frequency of astronomical tidal components and storm surge components, so it can be decomposed by using WPT to separate them into serials of frequency bands signals. Specifically, the storm surge process curve can be extracted independently by reconstructing the exclusive decomposed wavelet packet coefficients for our deeply research in the future. Taking one of typhoon measured tidal levelas an example to validation the opinion, the storm surge is calculated by using the presented method.Compared with the value calculated by traditional linear method, it shows that the suggested method can preferably separate storm surge and also has applied significance in analysising the ocean signals.

A Hybrid Signal Processing Technique for Bearing Defect Severity Estimation

2005 ◽  
Author(s):  
Robert X. Gao ◽  
Ruqiang Yan
Keyword(s):  
Signal Processing ◽  
Wavelet Packet ◽  
Principal Component ◽  
Processing Technique ◽  
Statistical Features ◽  
Signal Processing Technique ◽  
Severity Estimation ◽  
Bearing Defect ◽  
Multiple Bearings ◽  
Health Diagnosis

This paper presents a hybrid signal processing technique for bearing defect feature extraction and severity estimation. This is achieved by decomposing vibration signals measured on multiple bearings with different defect conditions into multiple sub-bands by means of the wavelet packet transform (WPT). Representative statistical features for each sub-band are then calculated. Subsequently, Principal Component Analysis (PCA) is performed on the statistical features to choose the best-suited representative features as inputs to a diagnostic classifier for bearing health diagnosis.

scholarly journals An Improved Hilbert–Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles

2021 ◽  
Vol 11 (7) ◽  
pp. 2974
Author(s):  
Ipshita Das ◽  
Mohammad Taufiqul Arif ◽  
Aman Maung Than Oo ◽  
Mahbube Subhani
Keyword(s):  
Wavelet Packet ◽  
Wood Properties ◽  
Dominant Frequency ◽  
Processing Technique ◽  
Natural Material ◽  
Signal Processing Technique ◽  
Frequency Bands ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reconstructed Signal

In this study, vibration based non-destructive testing (NDT) technique is adopted for assessing the condition of in-service timber pole. Timber is a natural material, and hence the captured broadband signal (induced from impact using modal hammer) is greatly affected by the uncertainty on wood properties, structure, and environment. Therefore, advanced signal processing technique is essential in order to extract features associated with the health condition of timber poles. In this study, Hilbert–Huang Transform (HHT) and Wavelet Packet Transform (WPT) are implemented to conduct time-frequency analysis on the acquired signal related to three in-service poles and three unserviceable poles. Firstly, mother wavelet is selected for WPT using maximum energy to Shannon entropy ratio. Then, the raw signal is divided into different frequency bands using WPT, followed by reconstructing the signal using wavelet coefficients in the dominant frequency bands. The reconstructed signal is then further decomposed into mono-component signals by Empirical Mode Decomposition (EMD), known as Intrinsic Mode Function (IMF). Dominant IMFs are selected using correlation coefficient method and instantaneous frequencies of those dominant IMFs are generated using HHT. Finally, the anomalies in the instantaneous frequency plots are efficiently utilised to determine vital features related to pole condition. The results of the study showed that HHT with WPT as pre-processor has a great potential for the condition assessment of utility timber poles.

New texture-based pansharpening method using wavelet packet transform and PCA

2020 ◽  
Vol 18 (04) ◽  
pp. 2050025
Author(s):  
Asma Abdolahpoor ◽  
Peyman Kabiri
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Wavelet Packet ◽  
Principal Component ◽  
Wavelet Packet Transform ◽  
Multispectral Image ◽  
Multispectral Images ◽  
Low Resolution ◽  
Panchromatic Image

Image fusion is an important concept in remote sensing. Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. Pansharpening is aimed on fusion of a low-resolution multispectral image with a high-resolution panchromatic image. Because of this fusion, a multispectral image with high spatial and spectral resolution is generated. This paper reports a new method to improve spatial resolution of the final multispectral image. The reported work proposes an image fusion method using wavelet packet transform (WPT) and principal component analysis (PCA) methods based on the textures of the panchromatic image. Initially, adaptive PCA (APCA) is applied to both multispectral and panchromatic images. Consequently, WPT is used to decompose the first principal component of multispectral and panchromatic images. Using WPT, high frequency details of both panchromatic and multispectral images are extracted. In areas with similar texture, extracted spatial details from the panchromatic image are injected into the multispectral image. Experimental results show that the proposed method can provide promising results in fusing multispectral images with high-spatial resolution panchromatic image. Moreover, results show that the proposed method can successfully improve spectral features of the multispectral image.

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