• 한국생산제조학회지
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• 제9권4호
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• pp.108-116
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• 2000

Undesired trouble such as chatter vibration and burning on the ground surface appears frequently in the cylindrical plunge grinding process. Establishment of a credible fault diagnostic system for the grinding process is the major purpose of this study. Power signals generated during the grinding operation were sampled and analyzed to determine the relationship between grinding troubles and behavior of signal changes. In addition, a neural network was optimized with a momentum coefficient a learning rate, and a structure of the hidden layer through the iterative learning process. Based on the established system, success rates of the trouble recognition were verified.

• 한국통신학회논문지
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• 제9권2호
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• pp.70-76
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• 1984

본 논문에서는 m개의 고장까지 극복할 수 있는 기능적 m-리던던트(Functional m-redundant)시스템을 그래프 이론에 바탕을 두고 정의하였다. 이 시스템은 리던던시를 효과적으로 이용하여 추가적인 테스트 기능없이 각 부시스템의 출력을 서로 비교함으로써 t(t$\geq$m)고장진단 가능하고 진단을 위한 시스템 정지가 필요없도록 설계되었다. 또한 이 시스템에 대한 진단 모델을 제시하였고 이 모델이 preparata의 진단 모델로 바뀌어질 수 있음을 보였으며 이를 이용하여 기능적 m-리던던트 시스템의 진단 특성을 Preparata에 의해 제시된 방법으로 해석하였다.

• 설비공학논문집
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• 제17권5호
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• pp.444-451
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• 2005

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of an air-conditioning system. In this study, partial faults for fans, coils, dampers, and sensors of a constant volume air handling unit were considered. A fuzzy algorithm was developed to detect and diagnose these faults. Diagnostic results by the fuzzy algorithm were compared with those by the model reference algorithm. The fuzzy algorithm showed better results in diagnostic accuracies.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 추계학술대회 논문집 전력기술부문
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• pp.374-376
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• 2001

In this paper, we developed the fault diagnostic expert system of silicone-immersed transformer using dissolved gas analysis. The knowledge base module consists of the knowledge using the rule: if Then . The inference engine uses the fuzzy rule for the management of uncertainty of the boundary and rule and derivate the Belief and Plausibility of the normality and fault using Dempster-Shafer theory. The expert system is connected to the database and it can manages the history of gas-data of the transformer.

• 한국군사과학기술학회지
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• 제3권1호
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• pp.138-145
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control method for the control systems in closed-loop affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the disturbance-decoupled state estimation using a 2-stage state observer for state, actuator bias and sensor bias. The estimated bias show the occurrence time, location and type of the faults directly. The estimated state is used for state feedback to achieve fault tolerant control against the faults. Simulation results show that the method has definite fault tolerant ability against actuator and sensor faults, moreover, the faults can be detected on-line, isolated and estimated simultaneously.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.665-667
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• 1999

An adaptive algorithm is presented for diagnosis of actuator faults. The concept of unknown input decoupling is combined with an adaptive observer, leading to an adaptive diagnostic observer, which has the robustness property in the presence of an unmeasurable term such as uncertainties. The observation error equation for the adaptive diagnostic observer does not depend on the effect of uncertainties and used to construct an adaptive diagnostic algorithm that provides the estimates of the gains of actuators, which can be obtained directly via the use of the augmented error technique. The simulation results indicate that the proposed algorithm is more realistic in the sense that better robustness properties can be assured without knowledge about uncertainties and is potentially useful in the development of a fault tolerant control system.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 춘계학술대회 논문집
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• pp.639-642
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• 2000

Chemical facility system have dangerous elements that can injure the human like an explosion and a fire, gas poisoning by a leakage of the harmful chemical material. In addition to a vibration of the machine occurs the leakage. Therefore, the chemical factory requires for periodic monitoring of the vibration. But, until now, the operator has executed a monitoring of the machine by the senses. So, the diagnostic expert system by which the operator can judge easily and expertly a condition of the machine is developed. This paper describes the structure of diagnostic system and the diagnostic algorithm using fuzzy inference

• 대한전자공학회논문지
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• 제27권9호
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• pp.1351-1363
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• 1990

In this paper, a reliable control system structured with dual CPU modules and dual I/O modules is implemented as a means of achieving a highly reliable fault tolerant control system. For this, faults in the system modules are first examined, and a fault detection technique consisting of self diagnostic, comparison process, and exception processing is applied. Self diagnostic is used to locate which components in the modules have been failed, while comparison process is to cmpare control outputs computed by both CPU modules and protect the plant from malfunction by blocking failed control outputsin advance. Finally exception processing is used to determine the faults that are not detected immediately by the self diagnostic and comparison process, e.g. bus error processing when acknowledge signal for data transfer is not activeted in the I/O modules. Also reliability analysis is conducted for the discrete time Markov model with dual structure. It is shown quantitatively that the reliability is improved in the control system with dual structure in comparison with a system with single module structure.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• 제28권7호
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• pp.1092-1100
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• 2004

This paper describes the application of fault tree technique to analyze of compressor failure. Fault tree analysis technique involves the decomposition of a system into the specific form of fault tree where certain basic events lead to a specified top event which signifies the total failure of the system. In this research. fault trees for failure analysis of screw type air compressor are made. This fault trees are used to obtain minimal cut sets from system failure and system failure rate for the top event occurrence can be calculated. It is Possible to estimate air compressor reliability by using constructed fault trees through compressor failure example. It is Proved that FTA is efficient to investigate the compressor failure modes and diagnose system.