• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 하계학술대회 논문집 A
• /
• pp.355-358
• /
• 1998

Train control and monitoring system for the railway train requires a reliable real-time communication network. The system have various functions, diagnostics, passenger informations, and fault-tolerant controls. For this system, an international standard called TCN(Train Communication Network) is proposed by IEC and the train industries. The TCN is composed of two layers, wire train bus(WTB) and multifunction vehicle bus(MVB). This paper evaluates the performance of the proposed WTB and modified WTB. And computer simulations are performed. The evaluated results can be used for the fault tolerant network in the railway train system.

• International Journal of Reliability and Applications
• /
• 제2권1호
• /
• pp.57-71
• /
• 2001

Global competition to increase production output and to improve quality is spurring manufacturing companies to use condition monitoring and fault diagnostic systems for predictive maintenance. As monitoring, testing, and measuring techniques develop, predictive control of components and complete systems have become more practical and affordable. In this article, we will consider the computer based data acquisition system for condition monitoring and the condition parameter analysis techniques for fault detection and diagnostics in the machinery and briefly discuss reliability prediction and the limit value determination in condition monitoring.

• 드라이브 ㆍ 컨트롤
• /
• 제20권4호
• /
• pp.54-63
• /
• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• 한국조명전기설비학회:학술대회논문집
• /
• 한국조명전기설비학회 2004년도 학술대회 논문집
• /
• pp.371-374
• /
• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2008년 영문 학술대회
• /
• pp.187-194
• /
• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2007년도 제29회 추계학술대회논문집
• /
• pp.345-349
• /
• 2007

본 연구에서는 계측 데이터의 성능 추이를 분석하여 엔진의 기계적 결함 여부를 탐지하기 위한 퍼지 경향감시 방법을 제안하였다. 경향감시 방법은 연료유량, 배기가스 온도, 로터회전수, 진동수와 같은 중요 엔진 파라미터를 모니터링하여 시간에 따른 변화를 분석하여 엔진 상태를 진단하는 것이다. 선형회귀분석을 통해 엔진 상태 변화를 수식화하고 퍼지 로직을 통해 진단 결과를 분석하여 예측되는 손상 원인을 제시한다.

• 한국추진공학회지
• /
• 제15권2호
• /
• pp.1-7
• /
• 2011

다양한 비행환경에서 장시간 체공하며 운용되는 UAV에서 추진시스템을 신뢰성 있게 운용하는 것은 매우 중요하다. 이런 UAV에 사용되는 터보프롭 엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지 이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용하여 손상된 양을 정확히 진단할 수있는 방법을 제안하였다. 이렇게 제안된 진단 방법은 단일손상과 다중손상 모두 진단할 수 있다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.231-232
• /
• 2007

In this paper, a fault diagnosis and control integrated system (FDCIS) was developed to control the thermoelectric (TE) power in the SP-100 space reactor. The objectives of the proposed model predictive control were to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives were subject to maximum and minimum control drum angle and maximum drum angle variation speed. A genetic algorithm was used to optimize the model predictive controller. The model predictive controller was integrated with a fault detection and diagnostics algorithm so that the controller can work properly even under input and output measurement faults. With the presence of faults, the control law was reconfigured using online estimates of the measurements. Simulation results of the proposed controller showed that the TE generator power level controlled by the proposed controller could track the target power level effectively even under measurement faults, satisfying all control constraints.

• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2010년도 제35회 추계학술대회논문집
• /
• pp.499-505
• /
• 2010

다양한 비행환경에서 장시간 체공하며 운용되는 UAV에서 추진시스템을 신뢰성 있게 운용하는 것은 매우 중요하다. 이런 UAV에 사용되는 터보프롭 엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지 이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용하여 손상된 양을 정확히 진단할 수 있는 방법을 제안하였다. 이렇게 제안된 진단 방법은 단일손상과 다중손상 모두 진단할 수 있다.

• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2003년도 제21회 추계학술대회 논문집
• /
• pp.267-270
• /
• 2003

본 연구에서는 신경회로망을 이용한 가스터빈 엔진의 지능형 성능 진단 컴퓨터 프로그램을 개발하였다. 최근에는 엔진 손상을 분석하는데 있어서 주요 구성품의 가스 경로를 실시간 모니터링(monitoring)하는 가스경로해석(GPA, Gas Path Analysis)방법이 사용되고 있다. 그러나 엔진손상의 형태나 정도가 다양하고 복잡하기 때문에 가스경로해석 접근법만 가지고서는 엔진의 손상상태를 모두 모니터링하기란 쉽지 않다. 따라서 이 문제를 해결하기 위해 학습과 진단을 할 수 있는 신경회로망을 적용하였다. 본 연구에서는 PT6A-62 터보프롭 엔진의 진단에 1개의 은닉층을 갖는 역전파 신경회로망(BPN, Back Propagation Neural Network)이 제안되었다.