• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• 한국항해항만학회지
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• 제46권5호
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• pp.400-408
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• 2022

Maritime transportation is one of the most complicated and hazardous business fileds. Maritime accidents still occur despite several precautions since maritime is exposed to natural factors more than any other industries. In this harsh environment as a part of their job, marine pilots often embark/disembark to/ from vessels and confront life-endangering personal accidents. In the maritime field, several risk assessments are applied. However, all of them could not evaluate occupational accident risk for maritime pilot specifically. This paper performs specific risk analysis using the bow-tie method based on past accident records. This paper aims to qualify root causes and quantify root causes by importance level according to occurrence probability. As a result of analysis, occupational accident occurrence probability is found to be 14%, indicating that accident occurrence rate is significantly high. Hence, the probability of root causes triggering accidents and accident occurrence probability can be ascertained so that preventive measurements can be implemented. Besides theoretical achievement, this paper provides safety awareness to marine pilots, Marine Pilot Organizations, and ship crew who play a key role during marine pilots' transfer.