• Tribology and Lubricants
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• 제14권3호
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Nuclear Engineering and Technology
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• 제53권2호
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• 한국추진공학회지
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• 제22권5호
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• pp.13-21
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• 2018

항공기용 엔진의 건전성 관리를 위한 탑재장비 및 지상 장비 소프트웨어의 발전 동향을 살펴보았다. 과거에는 지상 장비 중심의 결함 검출 및 식별기법에서 탑재 소프트웨어를 이용한 모델 기반의 건전성 식별 기법으로 변화해 왔고, 현재는 지상과 탑재장비 소프트웨어의 통합된 구조로 발전해 가고 있다. 이러한 진보된 기법이 선진국을 중심으로 기술발전을 이루어 가고 있음에 비해 국내의 연구는 초보적인 수준에 머물러 있다. 본 논문에서는 국내외 기술개발 현황을 고려하여 최적의 발전 방향을 제시하였다.

• 한국항공우주학회지
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• 제31권6호
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• pp.52-60
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• 2003

GPS(Global Positioning System)를 이용한 항공항법은 이용성과 무결성의 만족을 절대적으로 요구하고 있다. GPS의 무결성에 대한 연구로서 GPS수신기 내부 자체에서 무결성을 모니터링하는 다양한 RAIM(Receiver Autonomous Integrity Monitoring)기법이 연구되어 왔으며 이들 중에서 패러티 공간을 이용한 고장진단기법은 패러티 백터의 크기와 방향성을 이용할 수 있는 편리성을 갖고 있어 비교적 많은 연구가 진행되어 왔다. 한편, 지금까지의 RAIM 기법들은 대부분 단일고장을 가정하며, 실제 적용시 발생할 수 있는 다중고장의 경우 오차요인들의 상호간섭으로 정확한 식별이 어렵다는 단점을 갖고 있다. 본 논문에서는 확장된 패러티 공간에서 고장진단을 다룸으로써 2개의 고장식별에의 적용이 가능함을 보였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.839-845
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• 2003

This paper introduces multichannel blind source separation (BSS) and multichannel blind deconvolution (MBD) based on higher order statistics of signals from convolutive mixtures. In particular, we are concerned with the case that the number of inputs is the same as the number of outputs. Simulations for two input two output cases are carried out and their performances are assessed. One of the major applications of those sequential algorithms (BSS and MBD) is demonstrated through the fault signal detection from only a single measurement of rotating machine, which offers a certain degree of practicability in the engineering field such as machine health monitoring or condition monitoring.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.203-207
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• 1998

In this paper, it is shown how Fuzzy Pattern Matching can be applied to diagnosis of the most common faults of Rotating Machinery. The whole diagnosis process has been divided in three steps : Fault Detection, Fault Isolation and Fault Identification, whose possible results are described by linguistic patterns. Diagnosis will consist in obtaining a set of matching indexes that indexes that express the compatibility of the fuzzified features extracted from the measured vibration signals, with the knowledge contained in the corresponding patterns.

• 전기학회논문지P
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• 제56권1호
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• pp.28-36
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• 2007

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.710-713
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• 1995

Hopfield networks have been applied to the problem of linear system identification. In this paper, Hopfield network based parameter identification scheme of non-linear dynamic systems is proposed. Simulation results demonstrate that Hopfield network can be used effectively for the identification of non-linear systems assuming that the system states and their time derivatives are available. Therefore, the proposed scheme can be applied in fault detection and isolation(FDI) and adaptive control of non-linear systems where the Hopfield networks perform on-line identification of system parameters.

• Smart Structures and Systems
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• 제4권4호
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• pp.429-438
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• 2008

The aim of this paper is to define the required measurements and processing tools necessary for developing a maintenance approach applied to rotating machines in the presence of multiple faults. The system responses measured were accelerations and transmission errors. Acceleration measurements provide most of the information on bearing conditions, while transmission error measurements provide pertinent information on gear conditions. The measurements were carried out for several operating conditions (loads and speeds). System responses were processed in several analyzing domains (Time, Spectrum, and Cepstrum domains). The approach developed enables the detection and identification of combined faults and it can be applied to other types of rotating machines once the critical elements and their associated faults have been defined.

• 한국소음진동공학회논문집
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• 제25권11호
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• pp.764-770
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• 2015

A crack identification method using an equivalent bending stiffness and natural frequency for cracked beam is presented. Modal properties of cantilever beam with a tip mass is identified by applying the boundary conditions to a general solution. An equivalent bending stiffness for cracked beam based on an energy method is used to identify natural frequencies of cantilever thin-walled pipe with a tip mass, which has a through-the-thickness crack, subjected to bending. The identified natural frequencies of the cracked beam are used in constructing training patterns of neural networks. Then crack location and size are identified using a committee of the neural networks. Crack detection was carried out for an example beam using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.