New Evaluation Method on Gear Dynamics Using Continuous and Discrete Wavelet Transforms

2003 ◽  
Vol 125 (3) ◽  
pp. 274-281 ◽  
Author(s):  
Yuji Ohue ◽  
Akira Yoshida
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Evaluation Method ◽  
Dynamic Condition ◽  
Testing Machine ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Gear Dynamics ◽  
Time Frequency ◽  
The Difference

The aim of this study is to propose a new evaluation method of gear dynamics using the continuous and discrete wavelet transforms. The wavelet transform (WT) is a method for the time-frequency analysis of signals. In order to evaluate the difference in the gear dynamics due to the gear materials, which are sintered and steel ones, the dynamic characteristics of gears were measured using a power circulating gear testing machine. The gear dynamics were analyzed in a time-frequency domain by the continuous and discrete WTs. The new evaluation method using the WTs proposed in this paper was more useful compared with the conventional one to investigate the damping characteristic and the dynamic condition of the gear equipment.

New Evaluation Method on Gear Dynamics Using Continuous and Discrete Wavelet Transforms

2000 ◽  
Author(s):  
Yuji Ohue ◽  
Akira Yoshida
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Evaluation Method ◽  
Dynamic Condition ◽  
Testing Machine ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Gear Dynamics ◽  
Time Frequency ◽  
The Difference

Abstract The aim of this study is to propose a new evaluation method of gear dynamics using continuos and discrete wavelet transforms. The Wavelet Transform (WT) is a method for the time-frequency analysis of signals. In order to evaluate the difference in the gear dynamics due to the gear material, the dynamic characteristics of gear were measured using a power circulating gear testing machine. The gear dynamics were analyzed in a time-frequency domain by the continuos and discrete WTs. The new evaluation method using the WTs proposed in this paper was very useful compared with the conventional one to investigate the damping characteristic and the dynamic condition of the gear equipment.

scholarly journals Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Timur Düzenli ◽  
Nalan Özkurt
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Vanishing Moments ◽  
Zero Crossings ◽  
Time Frequency ◽  
Common Time ◽  
Classification Tool

The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

2020 ◽  
Vol 18 (06) ◽  
pp. 2030001 ◽  
Author(s):  
Rodrigo Capobianco Guido ◽  
Fernando Pedroso ◽  
André Furlan ◽  
Rodrigo Colnago Contreras ◽  
Luiz Gustavo Caobianco ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Time ◽  
Wavelet Transforms ◽  
Signal Analysis ◽  
Applied Mathematics ◽  
Discrete Wavelet ◽  
Frequency Signal ◽  
Time Frequency ◽  
Subsequent Investigation ◽  
Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Image Weight Models Based on Discrete Wavelet Transforms

2021 ◽  
Author(s):  
Maya M. Lyasheva ◽  
Stella A. Lyasheva ◽  
Mikhail P. Shleymovich
Keyword(s):  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms
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