• Advances in aircraft and spacecraft science
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• 제7권1호
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• pp.1-17
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• 2020

In this paper, a new model-based Fault Detection and Diagnosis (FDD) method for an agile supersonic flight vehicle is presented. A nonlinear model, controlled by a classical closed loop controller and proportional navigation guidance in interception scenario, describes the behavior of the vehicle. The proposed FDD method employs the Inertial Navigation System (INS) data and nonlinear dynamic model of the vehicle to inform fins damage to the controller before leading to an undesired performance or mission failure. Broken, burnt, unactuated or not opened control surfaces cause a drastic change in aerodynamic coefficients and consequently in the dynamic model. Therefore, in addition to the changes in the control forces and moments, system dynamics will change too, leading to the failure detection process being encountered with difficulty. To this purpose, an equivalent aerodynamic model is proposed to express the dynamics of the vehicle, and the health of each fin is monitored by the value of a parameter which is estimated using an adaptive robust filter. The proposed method detects and isolates fins damages in a few seconds with good accuracy.

• International Journal of Naval Architecture and Ocean Engineering
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• 제8권3호
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• 설비공학논문집
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• 제20권7호
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• pp.508-516
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• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. Failure modes in this study include refrigerant leakage, decrease in mass flow rate of the chilled water and cooling water, and sensor error of the cooling water inlet temperature. It is possible to detect and diagnose faults in this study by adopting FDD algorithm using only four parameters(compressor outlet temperature, chilled water inlet temperature, cooling water outlet temperature and compressor power consumption). Refrigerant leakage failure is detected at 20% of refrigerant leakage. When mass flow rate of the chilled and cooling water decrease more than 8% or 12%, FDD algorithm can detect the faults. The deviation of temperature sensor over $0.6^{\circ}C$ can be detected as fault.

• 한국기계가공학회지
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• 제20권9호
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• Nuclear Engineering and Technology
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• 제32권3호
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• pp.261-273
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• 2000

During an extensive review made as part of the Integrated Diagnosis System project of the Maiden Reactor Project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS is an integrated on-line diagnosis system that encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used at the Maiden Man-Machine Laboratory HAMMLAB of the OECD Maiden Reactor Project. The performance of MOAS, developed in G2 real-time expert system shell for the high-pressure preheaters of the NORS process in the HAMMLAB, was tested against a variety of transient scenarios, including failures of the control valves and sensors, and tube leakage of the preheaters. These tests showed that MOAS successfully carried out its intended functions, i.e., quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.96.1-96
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• 2002

$\textbullet$ Abstract $\textbullet$ Introduction $\textbullet$ Building a Model for Diagnosis $\textbullet$ Modular Approach to Diagnosis $\textbullet$ Extension to a General Case $\textbullet$ Conclusion $\textbullet$ References

• 한국컴퓨터정보학회논문지
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• 제16권2호
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• pp.141-151
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• 2011

본 논문에서는 미래농업의 핵심기술로 각광 받고 있는 식물공장의 안전하고 효과적인 운용을 위해, 식물공장 내부에 설치된 센서나 설비 장치가 정상적으로 작동하는지 실시간으로 진단하고, 내부 환경 및 설비의 제어상태를 모니터링 하는 식물공장 시설관리 시스템을 제안한다. 본 시스템은 데이터관리 모듈, 상황정보제공 모듈, 상황분석 모듈, 서비스제공 모듈, 정보저장소 모듈, 사용자 인터페이스 모듈로 구성된다. 이러한 각 모듈간의 상호작용을 통해 오작동 진단 서비스, 설비장치 제어 서비스, 고 신뢰성 모니터링 서비스를 제공한다. 오작동 진단 서비스는 식물공장 내부에 설치된 센서나 설비 장치의 오작동여부를 판단하고 관리자에게 통보하는 기능을 수행한다. 설비장치 제어 서비스는 설비의 오작동을 진단하는 과정에서 제어의 필요성이 판단 될 경우 해당 기기를 제어한다. 고 신뢰성 모니터링 서비스는 오작동 진단 서비스를 통해 검증된 데이터를 관리자에게 제공한다. 제안한 시스템의 시뮬레이션을 통하여 각 서비스가 정상적으로 동작함을 확인하였다.

• 한국태양에너지학회 논문집
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• 제38권3호
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• pp.21-28
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• 2018

The objects of PV (Photovoltaic) monitoring system is to reduce the loss of system and operation and maintenance costs. In case of PV plants with configured of centralized inverter type, only 1 PV module might be caused a large loss in the PV plant. For this reason, the monitoring technology of PV module-level that find out the location of the fault module and reduce the system losses is interested. In this paper, a fault diagnosis algorithm are proposed using thermal and electrical characteristics of PV modules under failure. In addition, the monitoring system applied with proposed algorithm was constructed. The wireless sensor using LoRa chip was designed to be able to connect with IoT device in the future. The characteristics of PV module by shading is not failure but it is treated as a temporary failure. In the monitoring system, it is possible to diagnose whether or not failure of bypass diode inside the junction box. The fault diagnosis algorithm are developed on considering a situation such as communication error of wireless sensor and empirical performance evaluation are currently conducting.

• 한국임상수의학회지
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• 제24권2호
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• pp.260-263
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• 2007

A 9-month-old, intact female mixed dog was referred to Veterinary Medical Teaching Hospital of Gyeongsang National University with symmetrically enlarged and protruded upper jaw. The patient was diagnosed as acute renal failure due to ethylene glycol poisoning and was treated for 1 month in a local animal hospital. In spite of treatment, the patient proceeded to chronic renal failure. Also, the patient's upper jaw begun to enlarge continuously. To evaluate this upper jaw, radiographic examination was performed. Skull radiographs revealed thickening of maxilla, decreased bone opacity, cortical thinning, loss of lamina dura and periodontal space in the maxilla. Diagnosis of rubber jaw syndrome is based on clinicial signs, radiographic findings and laboratory evidence of chronic renal failure due to ethylene glycol poisoning.