Fault Diagnosis of MOVA Based on Evidence Theory

2014 ◽  
Vol 556-562 ◽  
pp. 2685-2688
Author(s):  
Ai Hua Zhou ◽  
Hong Song ◽  
Yu Fang ◽  
Zheng Wei Chang
Keyword(s):  
Experimental Data ◽  
Fault Diagnosis ◽  
System Reliability ◽  
Infrared Imaging ◽  
Early Stage ◽  
Evidence Theory ◽  
Power System Reliability ◽  
Present Evidence ◽  
Insulation Coordination ◽  
Different Sources

The health of MOVA is very important for power system reliability and insulation coordination studies.MOVA is subjected to different kinds of stresses in services, which will cause degradation at early stage or in the long run.This paper present evidence theory for fault diagnosis of MOVA. Evidence theory can simultaneously analyze information from different sources,draw comprehensive conclusions,reduce single information misjudgment. In this paper, resistive leakage current and infrared imaging make up two types of evidence body for judge. Analysis of experimental data shows that this method can effectively detect early fault of the MOVA.

scholarly journals Research on Multi-element Fusion of Equipment Fault Monitoring Based on Evidence Theory

2020 ◽  
Vol 179 ◽  
pp. 02001
Author(s):  
Quan ZHOU ◽  
Lan LIU ◽  
Hao CHENG ◽  
Mian FU
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Health Monitoring ◽  
Evidence Theory ◽  
Reliable Data ◽  
Fusion Method ◽  
Relevant Evidence ◽  
Fault Monitoring ◽  
Different Sources

The multi-information fusion method of health monitoring based on evidence theory is used to study the problem of equipment fault diagnosis. The multi-information of fault monitoring is fused by the evidence theory and the reliability of the relevant evidence can be judged according to the ambiguity and uncertainty of the fault monitoring signal. Also it can determine the importance and reliability of the evidence from different sources. The data from multi-information fusion has higher reliability and accuracy which provides more reliable data for fault diagnosis.

Diagnostics of metal samples using the results of experimental and theoretical study of positively charged microparticles formed upon sample destruction

2018 ◽  
Vol 84 (10) ◽  
pp. 23-28
Author(s):  
D. A. Golentsov ◽  
A. G. Gulin ◽  
Vladimir A. Likhter ◽  
K. E. Ulybyshev
Keyword(s):  
Experimental Data ◽  
Early Stage ◽  
Experimental Studies ◽  
Material Model ◽  
Total Charge ◽  
Cross Sectional ◽  
Practical Applications ◽  
Mechanical And Electrical Properties ◽  
Order Of Magnitude ◽  
Using Data

Destruction of bodies is accompanied by formation of both large and microscopic fragments. Numerous experiments on the rupture of different samples show that those fragments carry a positive electric charge. his phenomenon is of interest from the viewpoint of its potential application to contactless diagnostics of the early stage of destruction of the elements in various technical devices. However, the lack of understanding the nature of this phenomenon restricts the possibility of its practical applications. Experimental studies were carried out using an apparatus that allowed direct measurements of the total charge of the microparticles formed upon sample rupture and determination of their size and quantity. The results of rupture tests of duralumin and electrical steel showed that the size of microparticles is several tens of microns, the particle charge per particle is on the order of 10–14 C, and their amount can be estimated as the ratio of the cross-sectional area of the sample at the point of discontinuity to the square of the microparticle size. A model of charge formation on the microparticles is developed proceeding from the experimental data and current concept of the electron gas in metals. The model makes it possible to determine the charge of the microparticle using data on the particle size and mechanical and electrical properties of the material. Model estimates of the total charge of particles show order-of-magnitude agreement with the experimental data.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

An Hybrid Expert Fault Diagnosis System for CNC System

2011 ◽  
Vol 189-193 ◽  
pp. 1562-1566
Author(s):  
You Dong Chen ◽  
Jin Jun Ye ◽  
Hua Song Min ◽  
Mei Hua Han
Keyword(s):  
Fault Diagnosis ◽  
System Reliability ◽  
Domain Knowledge ◽  
Fault System ◽  
Rule Base ◽  
Case Based Reasoning ◽  
Cnc System ◽  
System Diagnosis ◽  
Fault Diagnosis System ◽  
Sqlite Database

The CNC system is a complex mechatronics system, which make it difficult to diagnose fault. Expert system for fault diagnosis that utilizes domain knowledge and the profiles of experts to fix the problem of the complex system has become an important issue. A hybrid expert fault system combining the rule-base reasoning (RBR) with case-based reasoning (CBR) for CNC system is proposed. The combination can trouble-shoot rapidly, improve the CNC system reliability and maintainability. The hybrid system is implemented by using QT and SQLITE database. The experiment result of the system shows that the system diagnosis efficiently and accurately.

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