129 Directional selection of Complex Wavelet Packet Transform and application to defect inspection of semiconductor circuit

2012 ◽  
Vol 2012.61 (0) ◽  
pp. _129-1_-_129-2_
Author(s):  
Takeshi KATO ◽  
Zhong ZHANG
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Directional Selection ◽  
Defect Inspection ◽  
Complex Wavelet ◽  
Selection Of

A NOVEL DESIGN METHOD FOR DIRECTIONAL SELECTION BASED ON 2-DIMENSIONAL COMPLEX WAVELET PACKET TRANSFORM

2013 ◽  
Vol 11 (04) ◽  
pp. 1360010 ◽  
Author(s):  
TAKESHI KATO ◽  
ZHONG ZHANG ◽  
HIROSHI TODA ◽  
TAKASHI IMAMURA ◽  
TETSUO MIYAKE
Keyword(s):  
Wavelet Transforms ◽  
Design Method ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Directional Selection ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Semiconductor Wafer ◽  
Directional Filters ◽  
Complex Wavelet

In this paper, we propose a design method for directional selection in the two-dimensional complex wavelet packet transform (2D-CWPT). Current two-dimensional complex discrete wavelet transforms (2D-CDWT) can extract directional components from images, but the number of directions is small, and the directions and resolutions are fixed. Thus the current 2D-CDWTs are not flexible enough. In this study, we propose a new design method of the directional filters that can detect desirable direction components. Additionally flexible directional selection is achieved because the directional filters are added to the 2D-CWPT. Finally, the proposed method is applied to defect detection in semiconductor wafer circuits and an encouraging result is obtained.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

2004 ◽  
Vol 17 (S1) ◽  
pp. 117-122 ◽  
Author(s):  
Zhou-min Xie ◽  
En-fu Wang ◽  
Guo-hong Zhang ◽  
Guo-cun Zhao ◽  
Xu-geng Chen
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Seismic Signal ◽  
Wavelet Packet Transform ◽  
Complex Wavelet

257 A Study on 2D Complex Wavelet Packet Transform and Its characteristic of edge detection

2011 ◽  
Vol 2011.60 (0) ◽  
pp. _257-1_-_257-2_
Author(s):  
Takeshi Kato ◽  
Zhong Zhang ◽  
Hiroshi Toda ◽  
Takashi Imamura ◽  
Tetsuo Miyake
Keyword(s):  
Edge Detection ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Complex Wavelet

scholarly journals An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Wuqiang Liu ◽  
Xiaoqiang Yang ◽  
Shen Jinxing
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Good Time ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Nonlinear Dimensionality Reduction ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Fault Type ◽  
Complex Wavelet

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

Sign in / Sign up

Export Citation Format

Share Document