scholarly journals An Early Warning Method of TCU Failure in Electromagnetic Environment Based on Pattern Matching and Support Vector Regression

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5537
Author(s):  
Xuan Li ◽  
Jianyong Zheng ◽  
Fei Mei ◽  
Haoyuan Sha ◽  
Danqi Li
Keyword(s):  
Support Vector Regression ◽  
Failure Mode ◽  
Pattern Matching ◽  
Early Warning ◽  
Failure Modes ◽  
Normal Operation ◽  
Support Vector ◽  
Stable Operation ◽  
Electromagnetic Disturbance ◽  
Electromagnetic Environment

With the continuous improvement of the voltage level of the power system, the electromagnetic interference problem of the converter station has become more and more serious. The thyristor control unit (TCU) is the core equipment of the converter valve, and its normal operation is related to the safe and stable operation of the entire converter valve. This paper starts with the actual electromagnetic environment in the converter valve hall, analyzes the failure principle of the TCU under electromagnetic disturbance, and observes the electromagnetic field distribution and sensitive components on the circuit board. Then, a TCU failure early warning method based on pattern matching and support vector regression (SVR) is proposed. The failure trend is deduced by constructing an abnormal information vector, and then the failure predictor is constructed using support vector regression optimized by grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO). Considering the failure type and warning time comprehensively, an early warning is issued when the failure mode probability increases to the threshold. When new failure modes appear, the failure mode library will continue to expand. The calculation example shows that this method can effectively warn the TCU failure in the electromagnetic environment, and its prediction accuracy can reach 89.2%, which is better than the traditional failure prediction method.

scholarly journals Water Network-Failure Data Assessment

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2990
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Barbara Tchórzewska-Cieślak ◽  
Mohamed Eid
Keyword(s):  
Water Supply ◽  
Failure Mode ◽  
Failure Modes ◽  
Task Force ◽  
Supply System ◽  
Water Supply System ◽  
Normal Operation ◽  
Water Leakage ◽  
Water Losses ◽  
Leakage Failure

The water-supply system is one of the basic and most important critical infrastructures. Water supply service disruption (water quality or quantity) may have serious consequences in modern societies. Water supply service is subject to various failure modes. Failure modes are specified by their degradation mechanisms, criticality, occurrence frequency and intensity. These failure modes have a random nature that impacts on the network disruption indicators, such as disruption frequency, network downtime, network repair time and network back-to-service time, i.e., the network resilience. This paper focuses on the water leakage failure mode. The water leakage failure mode assessment considers the unavoidable annual real water losses and the infrastructure leakage index recommended by the International Water Association’s Water Loss Task Force specialist group. Probabilistic statistical modelling was implemented to assess the seasonal index, the failure rates and the expectation value of the “mean time between failures.” The assessment is based on real operational data of the network. Specific attention is paid to the sensitivity of failures to seasonal variations. The presented methodology of the analysis of the water leakage failure mode is extendable to other failure modes and can help in developing new strategies in the management of the water-supply system in normal operation and crisis situations.

scholarly journals The Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Two Neural Network Models in the Context of COVID-19

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Feixiong-Ma ◽  
Yingying-Zhou ◽  
Xiaoyan-Mo ◽  
Yiwei-Xia
Keyword(s):  
Neural Network ◽  
Financial Crisis ◽  
Early Warning ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Listed Companies ◽  
Support Vector ◽  
Stable Operation ◽  
Social Significance ◽  
Neural Network Models

In the context of COVID-19, many companies have been affected by the financial crisis. In order to carry out a comparative study on the accuracy of the company’s financial crisis early warning method, this study used RPROP artificial neural network and support vector machine, with 162 listed companies’ two-year panel financial indicator data as a model sample, and the test sample established a financial crisis early warning model. The theory of comprehensive evaluation combining two kinds of neural network methods is put forward innovatively. The predicted results can strengthen the supervision of the listed companies with risks by themselves and others and have important economic and social significance to ensure the stable operation of the listed companies, the securities market, and the national economy.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

Combine Nanoprobing Technique with Difference Analysis to Identify Nonvisual Failures

2006 ◽  
Author(s):  
Cha-Ming Shen ◽  
Tsan-Cheng Chuang ◽  
Jie-Fei Chang ◽  
Jin-Hong Chou
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Electrical Characteristic ◽  
The Novel ◽  
Difference Analysis ◽  
Original Idea ◽  
Deductive Method ◽  
Complete Measurement

Abstract This paper presents a novel deductive methodology, which is accomplished by applying difference analysis to nano-probing technique. In order to prove the novel methodology, the specimens with 90nm process and soft failures were chosen for the experiment. The objective is to overcome the difficulty in detecting non-visual, erratic, and complex failure modes. And the original idea of this deductive method is based on the complete measurement of electrical characteristic by nano-probing and difference analysis. The capability to distinguish erratic and invisible defect was proven, even when the compound and complicated failure mode resulted in a puzzling characteristic.

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