scholarly journals Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Rong Jia ◽  
Yongtao Xie ◽  
Hua Wu ◽  
Jian Dang ◽  
Kaisong Dong
Keyword(s):  
Frequency Band ◽  
Power Transformer ◽  
Partial Discharge ◽  
Extraction Methods ◽  
Sample Entropy ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Hilbert Huang Transform ◽  
Frequency Band Energy

Effectively extracting power transformer partial discharge (PD) signals feature is of great significance for monitoring power transformer insulation condition. However, there has been lack of practical and effective extraction methods. For this reason, this paper suggests a novel method for the PD signal feature extraction based on multidimensional feature region. Firstly, in order to better describe differences in each frequency band of fault signals, empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) band-pass filter wave for raw signal is carried out. And the component of raw signals on each frequency band can be obtained. Secondly, the sample entropy value and the energy value of each frequency band component are calculated. Using the difference of each frequency band energy and complexity, signals feature region is established by the multidimensional energy parameters and the multidimensional sample entropy parameters to describe PD signals multidimensional feature information. Finally, partial discharge faults are classified by sphere-structured support vector machines algorithm. The result indicates that this method is able to identify and classify different partial discharge faults.

Envelope Analysis Based on the Combination of Morlet Wavelet and Kurtogram

2012 ◽  
Vol 490-495 ◽  
pp. 305-308
Author(s):  
Yu Liang ◽  
Yu Guo ◽  
Chuan Hui Wu ◽  
Yan Gao
Keyword(s):  
Frequency Band ◽  
Filter Banks ◽  
Morlet Wavelet ◽  
Center Frequency ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Filter Bandwidth ◽  
Envelope Analysis ◽  
Band Pass ◽  
Complex Morlet Wavelet

Envelope analysis based on the combination of complex Morlet wavelet and Kurtogram have advantages of automatic calculation of the center frequency and bandwidth of required band-pass filter. However, there are some drawbacks in the traditional algorithm, which include that the filter bandwidth is not -3dB bandwidth and the analysis frequency band covered by the filter-banks are inconsistent at different levels. A new algorithm is introduced in this paper. Through it, both optimal center frequency and bandwidth of band-pass filter in the envelop analysis can be obtained adaptively. Meanwhile, it ensures that the filters in the filter-banks are overlapped at the point of -3dB bandwidth and the consistency of frequency band that the filter-banks covered.

Hilbert-Huang Transform Based Modal Analysis of Structures

2008 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Hung-Jen Chen ◽  
Jeng-Yuan Lin
Keyword(s):  
Modal Analysis ◽  
Signal Reconstruction ◽  
Data Preprocessing ◽  
Structural Safety ◽  
Structural Parameter ◽  
Measured Signal ◽  
Reconstruction Technique ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Hilbert Huang Transform

This paper presents a Hilbert-Huang transform based signal reconstruction technique for the modal analysis of structural systems using vibration measurements. The original measured signal is initially undergone a well defined band-pass filter in order to solve the mode confounding problem. After the data preprocessing, each mode of the signal is reconstructed via the proper selection of intrinsic mode functions (IMFs) that are derived from the empirical mode decomposition of the signal’s mode. Through the signal reconstruction mode by mode, the structural parameter such as natural frequency is accurately evaluated, whose accuracy depends on the criterion for selecting the IMFs using the developed component sifting process. Reliable evaluation of systems’ characteristics leads to accurate prediction of systems’ behaviors for structural safety purpose. In this study, data preprocessing is operated to alleviate the problems of mode mixing and noise contaminated signal, as well as to compare with the previous work.

scholarly journals Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

2021 ◽  
Vol 11 (20) ◽  
pp. 9583
Author(s):  
Bongki Lee ◽  
Donghwan Kam ◽  
Yongjin Cho ◽  
Dae-Cheol Kim ◽  
Dong-Hoon Lee
Keyword(s):  
Success Rate ◽  
Sweet Pepper ◽  
Local Features ◽  
Bp Algorithm ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Plant Parts ◽  
Speeded Up Robust Features ◽  
Recognition Success

For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers.

Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy

2020 ◽  
pp. 107754632092566 ◽  
Author(s):  
HongChao Wang ◽  
WenLiao Du
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Sparse Decomposition ◽  
Vibration Data ◽  
Fault Features ◽  
Band Pass

As the key rotating parts in machinery, it is crucial to extract the latent fault features of rolling bearing in machinery condition monitoring to avoid the occurrence of sudden accidents. Unfortunately, the latent fault features are hard to extract by using the traditional signal processing method such as envelope demodulation because the effect of envelope demodulation is influenced strongly by the degree of background noise. Sparse decomposition, as a new promising method being able of capturing the latent fault feature components buried in the vibration signal, has attracted a lot of attentions, especially the predefined dictionary-based sparse decomposition methods. However, the feature extraction effect of the predefined dictionary-based sparse decomposition depends on whether the prior knowledge of the analyzed signal is sufficient or not. To overcome the above problems, a feature extraction method of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy is proposed in the article. First, a self-learned sparse atomics method is applied on the early weak vibration signal of rolling bearing and several self-learned atomics are obtained. Then, the self-learned atomics owing bigger kurtosis values are selected and used to reconstruct the vibration signal to remove the other interference signals. Subsequently, the frequency band entropy method is used to analyze the reconstructed vibration signal, and the optimal parameter of band-pass filter could be calculated. At last, the reconstructed vibration signal is filtered using the optimal band-pass filter, envelope demodulation on the filtered signal is applied, and better fault feature is extracted. The feasibility and effectiveness of the proposed method are verified through the vibration data of the accelerated fatigue life test of rolling bearing. Besides, the analysis results of the same vibration data using Autogram and spectral kurtosis methods are also presented to highlight the superiority of the proposed method.

scholarly journals Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions

2019 ◽  
Vol 9 (6) ◽  
pp. 1157 ◽  
Author(s):  
Yong Ren ◽  
Wei Li ◽  
Bo Zhang ◽  
Zhencai Zhu ◽  
Fang Jiang
Keyword(s):  
Frequency Band ◽  
Band Pass Filter ◽  
Angular Domain ◽  
Optimal Frequency ◽  
Rolling Bearings ◽  
Pass Filter ◽  
Bearing Fault ◽  
Order Spectrum ◽  
Resonance Band ◽  
Speed Fluctuation

Envelope analysis is a widely used method in fault diagnoses of rolling bearings. An optimal narrowband chosen for the envelope demodulation is critical to obtain high detection accuracy. To select the narrowband, the fast kurtogram (FK), which computes the kurtosis of a set of filtered signals, is introduced to detect cyclic transients in a signal, and the zone with the maximum kurtosis is the optimal frequency band. However, the kurtosis value is affected by rotating frequencies and is sensitive to large random impulses which normally occur in industrial applications. These factors weaken the performance of the FK for extracting weak fault features. To overcome these limitations, a novel feature named Order Spectrum Correlated Kurtosis (OSCK) is proposed, replacing the kurtosis index in the FK, to construct an improved kurtogram called Fast Order Spectrum Correlated Kurtogram (FOSCK). A band-pass filter is used to extract the optimal frequency band signal corresponding to the maximum OSCK. The envelope of the filtered signal is calculated using the Hilbert transform, and a low-pass filter is employed to eliminate the trend terms of the envelope. Then, the non-stationary filtered envelope is converted in the time domain into the stationary envelope in the angular domain via Computed Order Tracking (COT) to remove the effects of the speed fluctuation. The order structure of the angular domain envelope signal can then be used to determine the type of fault by identifying its characteristic order. This method offers several merits, such as fine order spectrum resolution and robustness to both random shock and heavy noise. Additionally, it can accurately locate the bearing fault resonance band within a relatively large speed fluctuation. The effectiveness of the proposed method is verified by a number of simulations and experimental bearing fault signals. The results are compared with several existing methods; the proposed method outperforms others in accurate bearing fault feature extraction under varying speed conditions.

Structural Modal Analysis Based on HHT and Butterworth Filter

2011 ◽  
Vol 255-260 ◽  
pp. 1836-1840
Author(s):  
Ding Yu Cui ◽  
Ke Gui Xin ◽  
Quan Quan Qi
Keyword(s):  
Damping Ratio ◽  
Mode Shapes ◽  
Local Characteristic ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Butterworth Filter ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Electronic Filter ◽  
Non Stationary Signal

Structural signal is taken self-adaptive decomposition by Hilbert-Huang Transform mainly composed of the EMD(empirical mode decomposition) and HT(Hilbert Transform), which is especially suitable for structural projects in the non-linear, non-stationary signal analysis through local characteristic. However, some weak signal may be ignored by EMD resulting in modal missing. Butterworth filter is an electronic filter designed to have flat frequency response. Considering the influence of noise , Hilbert-Huang Transform and Butterworth band pass filter are applied together to process the structural signal in order to obtain the frequency, damping ratio and mode shapes of the structure more precisely in this paper. Numerical Simulation results show that the proposed method in this paper could identify the modal parameters effectively and accurately.

scholarly journals Automatic Diagnosis of Epileptic Seizures in EEG Signals Using Fractal Dimension Features and Convolutional Autoencoder Method

2021 ◽  
Vol 5 (4) ◽  
pp. 78
Author(s):  
Anis Malekzadeh ◽  
Assef Zare ◽  
Mahdi Yaghoobi ◽  
Roohallah Alizadehsani
Keyword(s):  
Fractal Dimension ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Band Pass Filter ◽  
Classification Problems ◽  
Complex Wavelet Transform ◽  
Eeg Signals ◽  
Pass Filter ◽  
Box Counting ◽  
Convolutional Autoencoder

This paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.

scholarly journals Bandwidth Enhancement and Frequency Scanning Array Antenna Using Novel UWB Filter Integration Technique for OFDM UWB Radar Applications in Wireless Vital Signs Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3155 ◽  
Author(s):  
MuhibUr Rahman ◽  
Mahdi NaghshvarianJahromi ◽  
Seyed Mirjavadi ◽  
Abdel Hamouda
Keyword(s):  
Frequency Band ◽  
Vital Signs ◽  
Array Antenna ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Bandwidth Enhancement ◽  
Beam Array ◽  
Vital Signs Monitoring ◽  
Frequency Scanning ◽  
Band Pass

This paper presents the bandwidth enhancement and frequency scanning for fan beam array antenna utilizing novel technique of band-pass filter integration for wireless vital signs monitoring and vehicle navigation sensors. First, a fan beam array antenna comprising of a grounded coplanar waveguide (GCPW) radiating element, CPW fed line, and the grounded reflector is introduced which operate at a frequency band of 3.30 GHz and 3.50 GHz for WiMAX (World-wide Interoperability for Microwave Access) applications. An advantageous beam pattern is generated by the combination of a CPW feed network, non-parasitic grounded reflector, and non-planar GCPW array monopole antenna. Secondly, a miniaturized wide-band bandpass filter is developed using SCSRR (Semi-Complementary Split Ring Resonator) and DGS (Defective Ground Structures) operating at 3–8 GHz frequency band. Finally, the designed filter is integrated within the frequency scanning beam array antenna in a novel way to increase the impedance bandwidth as well as frequency scanning. The new frequency beam array antenna with integrated band-pass filter operate at 2.8 GHz to 6 GHz with a wide frequency scanning from the 50 to 125-degree range.

Sign in / Sign up

Export Citation Format

Share Document