A Novel Fault Type Identification and Fault Restoration Visualization for Substation

2020 ◽  
Vol 69 (10) ◽  
pp. 1432-1439
Author(s):  
Kyung-Min Lee ◽  
Jae-Young Hong ◽  
Tae-Won Kang ◽  
Chul-Won Park
Keyword(s):  
Fault Type ◽  
Fault Restoration

A New Technique for Scan Chain Failure Diagnosis

2002 ◽  
Author(s):  
Ruifeng Guo ◽  
Srikanth Venkataraman
Keyword(s):  
Hold Time ◽  
Failure Diagnosis ◽  
Second Step ◽  
Fault Type ◽  
Transition Faults ◽  
Input Side ◽  
Output Side ◽  
A New Technique ◽  
Scan Chain ◽  
Diagnostic Resolution

Abstract In this paper, we present a scan chain fault diagnosis procedure. The diagnosis for a single scan chain failure is performed in three steps. The first step uses special chain test patterns to determine both the faulty chain and the fault type in the faulty chain. The second step uses a novel procedure to generate special test patterns to identify the suspect scan cell within a range of scan cells. Unlike previously proposed methods that restrict the location of the faulty scan cell only from the scan chain output side, our method restricts the location of the faulty scan cell from both the scan chain output side and the scan chain input side. Hence the number of suspect scan cells is reduced significantly in this step. The final step further improves the diagnostic resolution by ranking the suspect scan cells inside this range. The proposed technique handles both stuck-at and timing failures (transition faults and hold time faults). The experimental results based on simulation and silicon units for several products show the effectiveness of the proposed method.

A Fault Diagnosis of Suck Rod Pumping System Based on SVM

2013 ◽  
Vol 307 ◽  
pp. 285-289 ◽  
Author(s):  
Wei Wu ◽  
Yu Zhou ◽  
Hang Xin Wei
Keyword(s):  
Fault Diagnosis ◽  
Support Vector ◽  
Oil Well ◽  
Pump System ◽  
Indicator Diagram ◽  
Invariant Moments ◽  
Pumping System ◽  
System A ◽  
Fault Type ◽  
Shape Characteristics

Aiming at the defects of fault diagnosis in the traditional method for sucker rod pump system, a new method based on support vector machine (SVM) pump fault diagnosis is proposed. Through studying the theory of invariant moment and the shape characteristics of pump indicator diagram, seven invariant moments is extracted from the indicator diagram as a pumping unit well condition of the characteristic parameters. Then these parameters are pretreatment, and it makes up seven eigenvector which are regarded as the input eigenvector of the SVM. The experiment indicates that the method can not only detect the fault of the pumping oil well but also can recognize the fault type of it, which is very effective for safety protection and fault diagnosis of the pumping oil.

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

scholarly journals Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1203 ◽  
Author(s):  
Chaichan Pothisarn ◽  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul ◽  
Chaiyan Jettanasen ◽  
Dimas Anton Asfani ◽  
...  
Keyword(s):  
Power System ◽  
Fault Location ◽  
Classification Algorithm ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
System Protection ◽  
Fault Type ◽  
Zero Sequence ◽  
Mother Wavelets

This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

scholarly journals Fault Diagnosis of Coal Mining Machinery Based on State Parameters

2020 ◽  
Author(s):  
Dong Song
Keyword(s):  
Fault Diagnosis ◽  
Coal Mining ◽  
Fault Type ◽  
Fault Parameters ◽  
Underground Coal Mines ◽  
Mining Machinery ◽  
The Status ◽  
New Perspective ◽  
Judgment Condition ◽  
Cutting Unit

The reliable operation of coal mining machinery acts as an important guarantee for safe productions in underground coal mines. The status monitoring and fault diagnosis of traditional coal mining machinery mainly rely on threshold judgments. However, a single judgment condition and a long fault propagation chain can be found in the method of threshold judgments, which make it difficult to accurately seek the fault type. By using the data analysis of state parameters for coal mining machinery, fault parameters and propagation paths can be analyzed effectively. This paper takes the cutting unit of a certain type of bolter miners as an example, a static and dynamic numerical analysis method of the cutting unit of bolter miners are established by virtue of FTA-Petri net models and BP-Firefly neural networks, which can provide a new perspective for fault diagnosis of coal mining machinery.

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