Signal Classification for Pipeline Security Threat Event Based on Optimized Support Vector Machine

2016 ◽  
Vol 693 ◽  
pp. 1428-1435
Author(s):  
Dong Jie Tan ◽  
Hong Zhang ◽  
Lu Liu
Keyword(s):  
Support Vector Machine ◽  
Fitness Function ◽  
Engineering Application ◽  
Warning System ◽  
Vibration Signal ◽  
Support Vector ◽  
Security Threats ◽  
Security Threat ◽  
Penalty Factor ◽  
Kernel Parameter

The way of efficiently classifying the manual digging, machine excavation, vehicle passing and other pipeline security threats, is an imperative problem for optical fiber pipeline security warning system. To solve this problem, a security threats classification method based on optimized support vector machine is proposed. In this method, after feature extraction based on wavelet to the original vibration signal, the artificial bee colony algorithm is introduced to optimize the penalty factor and kernel parameter of support vector machine under specified fitness function, and the optimized support vector machine is used to classify the pipeline security threats. To testify the performance of the proposed method, the experiment based on UCI feature datasets and actual vibration signal are made. Comparing with the support vector machine optimized by other algorithms, higher classification accuracy and less time consumption is achieved by the proposed method. Therefore, the effectiveness and the engineering application value of this proposed method is testified.

scholarly journals Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Dalian Yang ◽  
Jingjing Miao ◽  
Fanyu Zhang ◽  
Jie Tao ◽  
Guangbin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Bearing Faults ◽  
Ring Fault ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model

Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.

Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine

2016 ◽  
Author(s):  
Rui Yu ◽  
Xianling Li ◽  
Mo Tao ◽  
Zhiwu Ke
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Power Plants ◽  
Superior Performance ◽  
Support Vector ◽  
Feedwater Pump ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

The condition monitoring of the feedwater pump in secondary circuit is critical to the safe operation of the nuclear power plant. This article presents a fault diagnosis method of feedwater pump by using parameter-optimized support vector machine (SVM). While the fault features of feedwater pump are reflected from the power spectrum of the vibration signals, we trained and diagnosed the fault feature table with support vector machine. The optimal penalty factor C and kernel parameter γ of support vector machine are selected by grid search and k-fold cross validation. Then the faults are diagnosed by the SVM model under the optimal parameters. Diagnostic results show that the parameter-optimized SVM method achieves higher diagnostic accuracy than the PNN method, exhibiting superior performance to effectively diagnose the faults of feedwater pump.

scholarly journals Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm

2017 ◽  
Vol 36 (3) ◽  
pp. 227-242 ◽  
Author(s):  
E Jiaqiang ◽  
Cheng Qian ◽  
Hao Zhu ◽  
Qingguo Peng ◽  
Wei Zuo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Immune Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Preisach Model ◽  
Model Based ◽  
Weight Factors ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

In order to solve the hysteretic character of the piezoelectric material for application, the initial weight factors of the hysteretic units are calculated by the Preisach theory and the first-order reversal curves test data, a hysteretic Preisach model based on the improved fuzzy least square support vector machine (improved FLS-SVM) is established. In the established model, the fuzzy least square support vector machine is introduced to calculate more weight factors of the hysteretic units and the adaptive variable chaos immune algorithm is introduced to optimize the penalty factor and the kernel parameter of the FLS-SVM (the penalty factor c = 35 and the kernel parameter σ = 1.35 are obtained). Moreover, the quadratic polynomial interpolation method is used to eliminate the sawtooth phenomenon. The validity of established model reveals that fuzzy least square support vector machine method based on adaptive variable chaos immune algorithm (FLS-SVMAVCIA) is more accurate than FLS-SVM method according to application results of the real actuators (the absolute mean error of the FLS-SVMAVCIA model is less than 1 µm and its maximum error is less than 2 µm). As a result, the hysteretic phenomenon can be effectively eliminated by the hysteretic Preisach model based on the FLS-SVMAVCIA method.

Damage Identification for Simply-Supported Bridge Based on SVM Optimized by PSO (PSO-SVM)

2011 ◽  
Vol 80-81 ◽  
pp. 490-494 ◽  
Author(s):  
Han Bing Liu ◽  
Yu Bo Jiao ◽  
Ya Feng Gong ◽  
Hai Peng Bi ◽  
Yan Yi Sun
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Damage Identification ◽  
Fitness Function ◽  
Support Vector ◽  
Detection Accuracy ◽  
Damage Localization ◽  
Swarm Optimization ◽  
Simply Supported ◽  
Beam Bridge

A support vector machine (SVM) optimized by particle swarm optimization (PSO)-based damage identification method is proposed in this paper. The classification accuracy of the damage localization and the detection accuracy of severity are used as the fitness function, respectively. The best and can be obtained through velocity and position updating of PSO. A simply supported beam bridge with five girders is provided as numerical example, damage cases with single and multiple suspicious damage elements are established to verify the feasibility of the proposed method. Numerical results indicate that the SVM optimized by PSO method can effectively identify the damage locations and severity.

scholarly journals Analisis Strategi Forum Kewaspadaan Dini Masyarakat (FKDM) atas Cegah Dini dalam Penanganan Konflik Sosial di Provinsi Dki Jakarta Tahun 2019

2020 ◽  
Vol 3 (2) ◽  
pp. 205-209
Author(s):  
Dwi Agustina ◽  
Edy Mulyadi
Keyword(s):  
Government Regulation ◽  
Swot Analysis ◽  
Social Conflict ◽  
Warning System ◽  
Security Threats ◽  
Security Threat ◽  
National Unity ◽  
Conflict Handling ◽  
The Government

The community is responsible for the implementation of the community early awareness, meanwhile the government is obliged to facilitate it. A good role of the Community Early Awareness Forum or Forum Kewaspadaan Dini Masyarakat (FKDM) followed up by the government can save the community from security threat or disaster and minimize losses by anticipating the security threats and disaster. This research uses qualitative approach. Concept operationalization in this research refers to the used strategy, the SWOT analysis. The FKDM strategies in social conflict early prevention are: 1) inserting early warning system by increasing institutional capacities which include three elements; government, private sector, community through dialogue, 2) National Unity and Politics Agency or Badan Kesatuan Bangsa dan Politik (Kesbangpol) of DKI Jakarta actively making dialogue persuasively and finding solution, 3) budgeting of conflict handling according to the Government Regulation gives opportunity to strengthen community resilience to protect the community, encourage community participation, handle social conflict, and preserve local wisdom to maintain peace.

scholarly journals LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (M<sub>w</sub> = 7.5) OF 25 APRIL 2015

2016 ◽  
Vol III-8 ◽  
pp. 3-11
Author(s):  
M. Akhoondzadeh
Keyword(s):  
Support Vector Machine ◽  
Main Shock ◽  
Warning System ◽  
Least Square ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Strong Earthquakes ◽  
Nepal Earthquake ◽  
New Challenges

Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth&thinsp;=&thinsp;15.0 km) a powerful earthquake of M&lt;sub&gt;w&lt;/sub&gt;&thinsp;=&thinsp;7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.

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