• Title/Summary/Keyword: fault prediction

Search Result 260, Processing Time 0.024 seconds

Prediction of Insulation Capability for Ground Fault to Consider Asymmetry in SF6 Circuit Breaker

  • Oh, Yeon-Ho;Song, Ki-Dong;Kim, Hong-Kyu;Lee, Hae June;Hahn, Sung-Chin
    • Journal of Electrical Engineering and Technology
    • /
    • v.10 no.5
    • /
    • pp.2046-2051
    • /
    • 2015
  • Currently, most high-voltage gas circuit breakers (CBs) include asymmetrical geometries in the shield, the tank, the hot-gas exhaust, and the connection parts for bushings. For this reason, a 3-dimensional (3-D) analysis of the insulation capability is necessary, rather than a 2-D analysis. However, a 3-D analysis has difficulties due to the computational time and complex modeling. This paper presents a 3-D analysis considering the asymmetry in high-voltage gas CBs and a technique to reduce the calculation time. In the proposed technique, the arc plasma requiring the most computational time is first calculated by a 2-D analysis. Then, the results such as pressure, temperature, and velocity are input as a source for the 3-D analysis. This technique is applied to a 145kV self-blast-type CB and the analysis result exhibits good agreement with the experimental result.

A Study on Sensor Module and Diagnosis of Automobile Wheel Bearing Failure Prediction (차량용 휠 베어링의 결함 예측을 위한 센서 모듈 및 진단 연구)

  • Hwang, Jae-Yong;Seol, Ye-In
    • Journal of the Korea Convergence Society
    • /
    • v.11 no.11
    • /
    • pp.47-53
    • /
    • 2020
  • There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

A Study on the High Temperature Deformation Behavior of a Solid Solution Aluminium Alloy (알루미늄 고용체 합금의 고온변형 거동에 관한 연구)

  • Kim, Ho-Gyeong
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.21 no.2
    • /
    • pp.346-351
    • /
    • 1997
  • The creep characteristics of an Al-5wt.% Ag alloy including the stress exponent, the activation energy for creep and the shape of the creep curve were investigated at a normalized shear stress extending from $ 10^{-5}{\;}to{\;}3{\times}10^{-4}$ and in the temperature range of 640-873 K, where silver is in solid solution. The experimental results shows that the stress exponent is 4.6, the activation energy is 141 kJ/mole, and the stacking fault energy is $180{\;}mJ/m^2$, suggesting that the creep behavior of Al-5 wt.% Ag is similiar to that reported for pure aluminum, and that under the current experimental conditions, the alloy behaves as a class II(metal class). The above creep characteristics obtained for Al-5 wt.% Ag are discussed in the light of prediction regarding deformation mechanisms in solid solution alloys.

Fault Diagnosis of Drone Using Machine Learning (머신러닝을 이용한 드론의 고장진단에 관한 연구)

  • Park, Soo-Hyun;Do, Jae-Seok;Choi, Seong-Dae;Hur, Jang-Wook
    • Journal of the Korean Society of Manufacturing Process Engineers
    • /
    • v.20 no.9
    • /
    • pp.28-34
    • /
    • 2021
  • The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

A Study on Irresistible Medical Accidents Victims Relief System in the Perspective of Public Law (불가항력적 의료사고에 대한 국가보상의 공법적 검토)

  • Lee, Ho-Yong
    • The Korean Society of Law and Medicine
    • /
    • v.11 no.1
    • /
    • pp.59-84
    • /
    • 2010
  • Medical practice is characterized by various physiological response and uncapacity of prediction, therefore when medical accident occur it's hard to prove medical professionals' mistake. Though medical accident by medical professionals' mistake will be compensated anyhow, about irresistible medical accidents, no one should be not bound to compensate, victims get into very difficult situation. So, the nation don't negligent irresistible medical accidents but compensate anyway. As in the past, to the legal principle's constitution of irresistible medical accidents, theory of liability without fault was adapted, and it was said this theory was illogical in theory of liability with fault. But the subject of compensation to irresistible medical accidents is nation, nation don't participate in medical treatment therefore there is no room to occur mistake. And it is not reasonable to regard medical agency as a truster of public service, to cast to it responsibility of medical accidents. The problem of compensation to irresistible medical accidents is understood under the theory of social compensation. Social compensation is consisted of compensation to sacrifice and contribution to nation and society and compensation to sacrifice revealed under danger, the compensation to irresistible medical accidents belongs to the latter. This is near to concept of relief, is applied to national compensation system supplementarily, and compensation have no option but to compensate minimum. And there are not relation between national compensation system of irresistible medical accidents and proof liability transposition and theory of liability with out fault, merely in side of sharing responsibility burden between medical treater and victim, it is reasonable to discuss transportation of proof liability and compulsive liability insurance together.

  • PDF

High-Reliable Classification of Multiple Induction Motor Faults using Robust Vibration Signatures in Noisy Environments based on a LPC Analysis and an EM Algorithm (LPC 분석 기법 및 EM 알고리즘 기반 잡음 환경에 강인한 진동 특징을 이용한 고 신뢰성 유도 전동기 다중 결함 분류)

  • Kang, Myeongsu;Jang, Won-Chul;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
    • /
    • v.19 no.2
    • /
    • pp.21-30
    • /
    • 2014
  • The use of induction motors has been recently increasing in a variety of industrial sites, and they play a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of induction motors in order to reduce economical damage caused by their faults. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results show that the proposed approach yields higher classification accuracies than the state-of-the-art conventional approach for both noiseless and noisy environments for identifying the induction motor faults.

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

  • Sun, Peng;Li, Jian;Wang, Caisheng;Yan, Yonglong
    • Journal of Electrical Engineering and Technology
    • /
    • v.12 no.2
    • /
    • pp.689-700
    • /
    • 2017
  • This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

The Abnormal Groundwater Changes as Potential Precursors of 2016 ML5.8 Gyeongju Earthquake in Korea (지하수위 이상 변동에 나타난 2016 ML5.8 경주 지진의 전조 가능성)

  • Lee, Hyun A;Hamm, Se-Yeong;Woo, Nam C.
    • Economic and Environmental Geology
    • /
    • v.51 no.4
    • /
    • pp.393-400
    • /
    • 2018
  • Despite some skeptical views on the possibility of earthquake prediction, observation and evaluation of precursory changes have been continued throughout the world. In Korea, the public concern on the earthquake prediction has been increased after 2016 $M_L5.8$ and 2017 $M_L5.4$ earthquakes occurred in Gyeongju and Pohang, the southeastern part in Korea, respectively. In this study, the abnormal increase of groundwater level was observed before the 2016 $M_L5.8$ Gyeongju earthquake in a borehole located in 52 km away from the epicenter. The well was installed in the Yangsan fault zone, and equipped for the earthquake surveillance. The abnormal change in the well would seem to be a precursor, considering the hydrogeological condition and the observations from previous studies. It is necessary to set up a specialized council to support and evaluate the earthquake prediction and related researches for the preparation of future earthquake hazards.

Mine water inrush characteristics based on RQD index of rock mass and multiple types of water channels

  • Jinhai Zhao;Weilong Zhu;Wenbin Sun;Changbao Jiang;Hailong Ma;Hui Yang
    • Geomechanics and Engineering
    • /
    • v.38 no.3
    • /
    • pp.215-229
    • /
    • 2024
  • Because of the various patterns of deep-water inrush and complicated mechanisms, accurately predicting mine water inflows is always a difficult problem for coal mine geologists. In study presented in this paper, the water inrush channels were divided into four basic water diversion structures: aquifer, rock fracture zone, fracture zone and goaf. The fluid flow characteristics in each water-conducting structure were investigated by laboratory tests, and multistructure and multisystem coupling flow analysis models of different water-conducting structures were established to describe the entire water inrush process. Based on the research of the water inrush flow paths, the analysis model of different water inrush space structures was established and applied to the prediction of mine water inrush inflow. The results prove that the conduction sequence of different water-conducting structures and the changing rule of permeability caused by stress changes before and after the peak have important influences on the characteristics of mine water-gushing. Influenced by the differences in geological structure and combined with rock mass RQD and fault conductivity characteristics and other mine exploration data, the prediction of mine water inflow can be realized accurately. Taking the water transmitting path in the multistructure as the research object of water inrush, breaking through the limitation of traditional stratigraphic structure division, the prediction of water inflow and the estimation of potentially flooded area was realized, and water bursting intensity was predicted. It is of great significance in making reasonable emergency plans.

Development of smart car intelligent wheel hub bearing embedded system using predictive diagnosis algorithm

  • Sam-Taek Kim
    • Journal of the Korea Society of Computer and Information
    • /
    • v.28 no.10
    • /
    • pp.1-8
    • /
    • 2023
  • If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.