scholarly journals Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 56
Author(s):  
Yongye Wu ◽  
Zhanlong Zhang ◽  
Rui Xiao ◽  
Peiyu Jiang ◽  
Zijian Dong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optimization Algorithm ◽  
Network Optimization ◽  
Extraction Method ◽  
Deep Belief Network ◽  
Vibration Signals ◽  
Belief Network ◽  
Feature Extraction Method ◽  
Vibration Detection ◽  
Detection Technology

The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state during its manufacture and processing to ensure the safety of its future connection to the grid. Numerous studies have shown that vibration signals in transformers can reflect their operating state. Therefore, in order to achieve an effective identification of the operation state of the converter transformer, this paper proposes a method for identifying the operation state of the converter transformer based on vibration detection technology and a deep belief network optimization algorithm. This paper firstly describes the background, principle and application of vibration detection technology, using vibration measurement systems with piezoelectric acceleration sensors, piezoelectric actuators and data acquisition instruments to collect vibration signals at different measurement points on the converter transformer in states of no-load and on-load. By analyzing the time-frequency characteristics of the vibration signals, fast Fourier transform (FFT), wavelet packet decomposition (WPD) and time domain indexes (TDI) are combined into a fused feature extraction method to extract the eigenvalues of the vibration signals, so that the fused eigenvectors of the signals can be constructed. Considering the excellent performance of deep learning in classification, the deep belief network is used to classify the signals’ eigenvectors. To effectively improve the network classification efficiency, the sparrow search algorithm was introduced to build a mathematical model based on the behavioral characteristics of sparrow populations and combine the model with a deep belief network, so as to achieve adaptive parameter optimization of the network and accurate classification of the signals’ eigenvectors. The proposed method is applied to a 500 kV converter transformer for experimental verification. The experimental results show that the fused feature extraction method was able to fully extract the features of the vibration signal, and the deep belief network optimization algorithm had higher classification accuracy and better operational efficiency, and was able to effectively achieve accurate identification of the operation state of the converter transformer. In addition, the method achieved a precision response to the detection results of the vibration sensors, contributing to future improvements in converter transformer manufacturing technology.

scholarly journals Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1949 ◽  
Author(s):  
Yang Yuan ◽  
Suliang Ma ◽  
Jianwen Wu ◽  
Bowen Jia ◽  
Weixin Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector ◽  
Mechanical State ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Detection Technology ◽  
Intelligent Detection ◽  
Diagnosis Method ◽  
Frequency Feature

The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

scholarly journals A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1319
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Yucai Li ◽  
Wei Lin ◽  
Jinjuan Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Vibration Signal ◽  
Practical Significance ◽  
Stable Operation ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

An optimization-based deep belief network for the detection of phishing e-mails

2020 ◽  
Vol 54 (4) ◽  
pp. 529-549
Author(s):  
Arshey M. ◽  
Angel Viji K. S.
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Information Needs ◽  
Selection Process ◽  
Deep Belief Network ◽  
Data Set ◽  
Content Type ◽  
Belief Network ◽  
E Mail

PurposePhishing is a serious cybersecurity problem, which is widely available through multimedia, such as e-mail and Short Messaging Service (SMS) to collect the personal information of the individual. However, the rapid growth of the unsolicited and unwanted information needs to be addressed, raising the necessity of the technology to develop any effective anti-phishing methods.Design/methodology/approachThe primary intention of this research is to design and develop an approach for preventing phishing by proposing an optimization algorithm. The proposed approach involves four steps, namely preprocessing, feature extraction, feature selection and classification, for dealing with phishing e-mails. Initially, the input data set is subjected to the preprocessing, which removes stop words and stemming in the data and the preprocessed output is given to the feature extraction process. By extracting keyword frequency from the preprocessed, the important words are selected as the features. Then, the feature selection process is carried out using the Bhattacharya distance such that only the significant features that can aid the classification are selected. Using the selected features, the classification is done using the deep belief network (DBN) that is trained using the proposed fractional-earthworm optimization algorithm (EWA). The proposed fractional-EWA is designed by the integration of EWA and fractional calculus to determine the weights in the DBN optimally.FindingsThe accuracy of the methods, naive Bayes (NB), DBN, neural network (NN), EWA-DBN and fractional EWA-DBN is 0.5333, 0.5455, 0.5556, 0.5714 and 0.8571, respectively. The sensitivity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.4558, 0.5631, 0.7035, 0.7045 and 0.8182, respectively. Likewise, the specificity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.5052, 0.5631, 0.7028, 0.7040 and 0.8800, respectively. It is clear from the comparative table that the proposed method acquired the maximal accuracy, sensitivity and specificity compared with the existing methods.Originality/valueThe e-mail phishing detection is performed in this paper using the optimization-based deep learning networks. The e-mails include a number of unwanted messages that are to be detected in order to avoid the storage issues. The importance of the method is that the inclusion of the historical data in the detection process enhances the accuracy of detection.

scholarly journals A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

2016 ◽  
Vol 16 (3) ◽  
pp. 149-159 ◽  
Author(s):  
Haifeng Huang ◽  
Huajiang Ouyang ◽  
Hongli Gao ◽  
Liang Guo ◽  
Dan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Signal To Noise Ratio ◽  
Experimental Studies ◽  
Vibration Signal ◽  
Noise Sources ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Spectral Kurtosis

Abstract Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

scholarly journals Time-Frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

2021 ◽  
Author(s):  
Nanyang Zhao ◽  
Jinjie Zhang ◽  
Zhiwei Mao ◽  
Zhinong Jiang ◽  
He Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Economic Losses ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Noise Interference ◽  
Time Frequency ◽  
Fault Recognition ◽  
Penalty Factor ◽  
Frequency Feature

Abstract Reciprocating machinery, e.g., diesel engines and reciprocating compressors, is the key power component in petroleum, petrochemical, nuclear power, and shipbuilding industries. Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery; therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. Moreover, failures occur frequently every year, causing serious economic losses. To accurately and efficiently extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals was conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number K and second penalty factor was proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) was established. A search strategy for wavelet local parameters of multi-impact signals was proposed using the harmony search (HS) method. A multi-source shock simulation signal was established and actual data of the valve fault were obtained through diesel engine fault experiments. The test results demonstrated that the new method achieved adaptive extraction of local shock features of non-stationary multi-source shock signals and was superior to the original method in terms of signal decomposition effect, sensitive feature extraction, fault recognition accuracy, and parameter search time. The fault recognition rate of the intake and exhaust valve clearance was above 90% and the extraction accuracy of the shock start position was improved by 10°.

A Novel Feature Extraction Method Using Ant Colony Clustering Analysis

2010 ◽  
Vol 37-38 ◽  
pp. 32-35
Author(s):  
De Bin Zhao ◽  
Ji Hong Yan
Keyword(s):  
Feature Extraction ◽  
Clustering Analysis ◽  
Extraction Method ◽  
Ant Colony Algorithm ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Ant Colony ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Ant Colony Clustering

A novel feature extraction method is presented by combining wavelet packet transform with ant colony clustering analysis in this paper. Vibration signals acquired from equipments are decomposed by wavelet packet transform, after which frequency bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed in frequency-domain for extracting intrinsic features reflecting operating condition of machinery. Furthermore, the robust ant colony clustering algorithm is proposed by adjusting comparing probability dynamically. Finally, effectiveness and feasibility of the proposed method are verified by vibration signals acquired from a rotor test bed.

scholarly journals KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiao Hu ◽  
Zhihuai Xiao ◽  
Dong Liu ◽  
Yongjun Tang ◽  
O. P. Malik ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Extraction Method ◽  
Principal Component ◽  
Rotating Machinery ◽  
Kernel Principal Component Analysis ◽  
Global Feature ◽  
Vibration Signals ◽  
Feature Extraction Method

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.

Optimized deep belief network and entropy-based hybrid bounding model for incremental text categorization

2020 ◽  
Vol 16 (3) ◽  
pp. 347-368
Author(s):  
V. Srilakshmi ◽  
K. Anuradha ◽  
C. Shoba Bindu
Keyword(s):  
Feature Extraction ◽  
Text Classification ◽  
Optimization Algorithm ◽  
Text Categorization ◽  
Deep Belief Network ◽  
Support Vector ◽  
Text Documents ◽  
Content Type ◽  
Belief Network ◽  
Bounding Model

Purpose This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier. Design/methodology/approach At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents. Findings The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively. Originality/value This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.

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