Application of Frequency-Domain Blind Deconvolution in Mechanical Fault Detection

2011 ◽  
Vol 130-134 ◽  
pp. 2128-2132
Author(s):  
Nan Pan ◽  
Wu Xing ◽  
Yi Lin Chi ◽  
Liu Chang ◽  
Xiao Qin Liu
Keyword(s):  
Fault Detection ◽  
Frequency Domain ◽  
Blind Deconvolution ◽  
Rolling Bearing ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Fastica Algorithm ◽  
Amplitude Correlation ◽  
Signal Processing Methods ◽  
Acceleration Signals

On the basis of summing up the Frequency-Domain Blind Deconvolution (FDBD), a method combine Complex-Domain FastICA algorithm and amplitude correlation was proposed to extract the typical defect signals from mechanical equipment. The application in combined failure rolling bearing acceleration signals demonstrate that, comparing with the existing Time-Domain Blind Signal Processing methods, FDBD has more advantages and better prospects in mechanical fault detection.

Closed-Form Expressions of Some Stochastic Adapting Equations for Nonlinear Adaptive Activation Function Neurons

2003 ◽  
Vol 15 (12) ◽  
pp. 2909-2929 ◽  
Author(s):  
Simone Fiori
Keyword(s):  
Differential Equation ◽  
Signal Processing ◽  
Blind Deconvolution ◽  
Activation Function ◽  
Activation Functions ◽  
Neuron Models ◽  
Artificial Neuron ◽  
Information Theoretic ◽  
Blind Signal Processing ◽  
Blind Signal

In recent work, we introduced nonlinear adaptive activation function (FAN) artificial neuron models, which learn their activation functions in an unsupervised way by information-theoretic adapting rules. We also applied networks of these neurons to some blind signal processing problems, such as independent component analysis and blind deconvolution. The aim of this letter is to study some fundamental aspects of FAN units' learning by investigating the properties of the associated learning differential equation systems.

scholarly journals Application of RSSD-OCYCBD Strategy in Enhanced Fault Detection of Rolling Bearing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-24 ◽  
Author(s):  
Xiaolong Wang ◽  
Guiji Tang ◽  
Yuling He
Keyword(s):  
Fault Detection ◽  
Blind Deconvolution ◽  
Search Algorithm ◽  
Morphological Difference ◽  
Rolling Bearing ◽  
Morphological Transformation ◽  
Singular Spectrum ◽  
Difference Operation ◽  
Novel Strategy ◽  
Envelope Spectrum

The defect characteristics of rolling bearing are difficult to excavate at the incipient injury phase; in order to effectively solve this issue, an original strategy fusing recursive singular spectrum decomposition (RSSD) with optimized cyclostationary blind deconvolution (OCYCBD) is put forward to achieve fault characteristic enhanced detection. In this diagnosis strategy, the data-driven RSSD method without predetermined component number is proposed. In addition, a new morphological difference operation entropy (MDOE) indicator, which takes advantage of morphological transformation and Shannon entropy, is developed for confirming the influencing parameters of cyclostationary blind deconvolution (CYCBD). During the process of fault detection, RSSD is firstly adopted to preprocess the original signal, and the most sensitive singular spectrum component (SSC) is selected by the envelope spectrum peak (ESP) indicator. Then, the grid search algorithm is adopted to precisely confirm the optimal parameters and OCYCBD is further performed as a postprocessing technology on the most sensitive component to suppress the residual interferences and amplify the fault signatures. Finally, the enhanced fault detection of rolling bearing is able to achieve by analyzing the envelope spectrum of deconvolution signal. The feasibility of the proposed strategy is verified by the simulated and the measured signals, respectively, and its superiority is also demonstrated through several comparison methods. The results manifest this novel strategy has praisable advantages on weak characteristic extraction and intensification.

Frequency Domain Seismic Blind Deconvolution Based on ICA with High Order Statistics Constraint

2013 ◽  
Vol 321-324 ◽  
pp. 1827-1830 ◽  
Author(s):  
Wei Gao ◽  
Huai Shan Liu ◽  
Jian Ye Sun
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Fourier Transforms ◽  
Blind Deconvolution ◽  
Seismic Wavelet ◽  
Fastica Algorithm ◽  
Components Analysis ◽  
The Time Domain ◽  
Short Time ◽  
High Order Statistics

Independent components analysis (ICA) with constraint of seismic wavelet estimated from bispectrum of seismic traces is combined with short time Fourier transforms (STFT) to improve the traditional frequency domain seismic deconvolution. Neglecting noise, the seismic record is changed from time domain to frequency domain with STFT in order to transform the common seismic model to the basic ICA model. By applying FastICA algorithm with constraint of seismic wavelet estimated from bispectrum of seismic traces, reflectivity series and the seismic wavelet can be produced in frequency domain and changed back to the time domain subsequently. The model and real seismic data numerical examples all show the algorithm valid.

Application of Blind Signal Processing in Image Watermarking Systems

2020 ◽  
Author(s):  
Chuen-Yau Chen ◽  
Cheng-Yuan Lin ◽  
Yi-Ze Zou ◽  
Hung-Ming Hsiao ◽  
Yen-Ting Chen
Keyword(s):  
Signal Processing ◽  
Image Watermarking ◽  
Blind Signal Processing ◽  
Blind Signal

Application of C Sharp and MATLAB Mixed Programming Based on .Net Assembly in Blind Source Separation

2014 ◽  
Vol 599-601 ◽  
pp. 1407-1410
Author(s):  
Xu Liang ◽  
Ke Ming Wang ◽  
Gui Yu Xin
Keyword(s):  
Numerical Calculation ◽  
Programming Languages ◽  
Blind Source Separation ◽  
Source Separation ◽  
Processing System ◽  
Signal Processing System ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Mixed Programming ◽  
High Level

Comparing with other High-level programming languages, C Sharp (C#) is more efficient in software development. While MATLAB language provides a series of powerful functions of numerical calculation that facilitate adoption of algorithms, which are widely applied in blind source separation (BSS). Combining the advantages of the two languages, this paper presents an implementation of mixed programming and the development of a simplified blind signal processing system. Application results show the system developed by mixed programming is successful.

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