• Title/Summary/Keyword: 진동맵

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Journal Bearing Design Retrofit for Process Large Motor-Generator - Part II : Rotordynamics Analysis (프로세스 대형 모터-발전기의 저어널 베어링 설계 개선 - Part II : 로터다이나믹스 해석)

  • Lee, An Sung
    • Tribology and Lubricants
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    • v.28 no.6
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    • pp.265-271
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    • 2012
  • In the preceding Part I study, for improving the unbalance response vibration of a large PRT motor-generator rotor fundamentally by design, a series of design analyses were carried out for bearing improvement by retrofitting from original plain partial journal bearings, applied for operation at a rated speed of 1,800 rpm, to final tilting pad journal bearings. To satisfy evenly key basic lubrication performances such as the minimum lift-off speed and maximum oil-film temperature, a design solution of 5-pad tilting pad journal bearings and maximizing the direct stiffness by about two times has been achieved. In this Part II study, a detailed rotordynamic analysis of the large PRT motor-generator rotor-bearing system will be performed, applying both the original plain partial journal bearings and the retrofitted tilting pad journal bearings, to confirm the effect of rotordynamic vibration improvement after retrofitting. The results show that the rotor unbalance response vibrations with the tilting pad journal bearings are greatly reduced by as much as about one ninth of those with the plain partial journal bearings. In addition, for the tilting pad journal bearings there exist no critical speed up to the rated speed and just one instance of a concerned critical speed around the rated speed, whereas for the plain partial journal bearings there exist one instance of a critical speed up to the rated speed and two instances of concerned critical speeds around the rated speed.

Expansion Joint Motion Analysis using Hall Effect Sensor and 9-Axis Sensor (Hall Effect Sensor와 9-Axis Sensor를 이용한 Expansion Joint 모션 분석)

  • Kwag, Tae-Hong;Kim, Sang-Hyun;Kim, Won-Jung
    • The Journal of the Korea institute of electronic communication sciences
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    • v.16 no.2
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    • pp.347-354
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    • 2021
  • In the equipment industry such as chemical plants, high temperature, high pressure, and toxic fluids move between various facilities through piping. The movement and damage of pipes due to changes in the surrounding environment such as temperature changes, vibrations, earthquakes, and ground subsidence often lead to major accidents involving personal injury. In order to prevent such an accident, various types of expansion joints are used to absorb and supplement various shocks applied to the pipe to prevent accidents in advance. Therefore, it is very important to measure the deformation of the used expansion joint and predict its lifespan to prevent a major accident. In this paper, the deformation of the expansion joint was understood as a kind of motion, and the change was measured using a Hall Effect Sensor and a 9-Axis Sensor. In addition, we studied a system that can predict the deformation of expansion joints by collecting and analyzing the measured data using a general-purpose microcomputer (Arduino Board) and C language.

Machine Classification in Ship Engine Rooms Using Transfer Learning (전이 학습을 이용한 선박 기관실 기기의 분류에 관한 연구)

  • Park, Kyung-Min
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.2
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    • pp.363-368
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    • 2021
  • Ship engine rooms have improved automation systems owing to the advancement of technology. However, there are many variables at sea, such as wind, waves, vibration, and equipment aging, which cause loosening, cutting, and leakage, which are not measured by automated systems. There are cases in which only one engineer is available for patrolling. This entails many risk factors in the engine room, where rotating equipment is operating at high temperature and high pressure. When the engineer patrols, he uses his five senses, with particular high dependence on vision. We hereby present a preliminary study to implement an engine-room patrol robot that detects and informs the machine room while a robot patrols the engine room. Images of ship engine-room equipment were classified using a convolutional neural network (CNN). After constructing the image dataset of the ship engine room, the network was trained with a pre-trained CNN model. Classification performance of the trained model showed high reproducibility. Images were visualized with a class activation map. Although it cannot be generalized because the amount of data was limited, it is thought that if the data of each ship were learned through transfer learning, a model suitable for the characteristics of each ship could be constructed with little time and cost expenditure.