Fault feature extraction of rotating machinery based on wavelet transform and Self-organizing map network

2010 ◽  
Author(s):  
Maofa Gong ◽  
Xiaoming Zhang ◽  
Qingxue Liu ◽  
Zidong Zhao ◽  
Xiaoli Zhang
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Rotating Machinery ◽  
Self Organizing Map ◽  
Fault Feature Extraction ◽  
Self Organizing

Fault Diagnosis Method for Rectifier Circuit Based on Self-Organizing Map

2013 ◽  
Vol 321-324 ◽  
pp. 1930-1933 ◽  
Author(s):  
Run Xia Shen ◽  
Yi Min Lu ◽  
Qian Qian Liang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Self Organizing Map ◽  
Rectifier Circuit ◽  
Three Phase ◽  
Bridge Rectifier ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Relevant Experiment ◽  
Self Organizing

Fault feature extraction and recognition play crucial role in fault diagnosis. In this paper, a fault diagnosis method for three-phase fully-controlled bridge rectifier circuit based on Self-Organizing Map network is proposed. The method utilized the three phase AC input current as fault detection data. Then, perform spectrum analysis with the FFT, the fault data is trained through a Self-Organizing Map network for diagnosis. Simulation and relevant experiment verifying the proposed algorithm can classify various types of power electronics device faults accurately and rapidly.

Wavelet Algorithm in Rotating Machinery Fault Feature Extraction

2013 ◽  
Vol 823 ◽  
pp. 451-455
Author(s):  
Dong Song Luo ◽  
Zheng Fan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Spectral Characteristics ◽  
Rotating Machinery ◽  
Frequency Resolution ◽  
Fault Mechanism ◽  
Fault Feature Extraction ◽  
Fault Information ◽  
Acceleration Signals ◽  
Wavelet Algorithm

A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet transform is used to be decompose the vibration acceleration signals of bearing faults into different frequency bands, Which is then used to achieve accurate fault information by Hilbert demodulation. The result shows the method can effectively improve the frequency resolution and realize accurate extraction of fault feature, and it has certain practical value for industrial production of rotating machinery faults diagnosis when applied to the production industry. Key words: Rotating Machinery; bearings; Wavelet algorithm; Hilbert demodulation

scholarly journals Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

2019 ◽  
Vol 9 (9) ◽  
pp. 1876 ◽  
Author(s):  
Zheng Li ◽  
Anbo Ming ◽  
Wei Zhang ◽  
Tao Liu ◽  
Fulei Chu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Energy Operator ◽  
Segmentation Result ◽  
Rolling Element Bearings ◽  
Empirical Wavelet Transform ◽  
Rolling Element ◽  
Fault Feature Extraction

In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

scholarly journals Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map

2014 ◽  
Vol 8 (1) ◽  
pp. 101
Author(s):  
Ambarwati Ambarwati ◽  
Edi Winarko
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
System Application ◽  
Input Data ◽  
Information Resources ◽  
The Self ◽  
News Article ◽  
Self Organizing Map ◽  
Map Algorithm ◽  
Self Organizing

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM  Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM

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