18 research outputs found
์ฐ๋ฆฌ๋๋ผ ํตํ๊ฐํ์ ๋น๊ต ์ฐ๊ตฌ(A comparative study of currency reforms in Korea)
An approach for decomposing out-of-control monitoring statistics in multivariate clustering algorithm based control chart
Master์ํ ๋ชจ๋ํฐ๋ง์ ์ ์กฐ ๊ณต์ ์์ ํ์ฉ๋๋ ์ฅ๋น์ ์ํ๋ฅผ ํ์
ํ์ฌ, ์ด์์ ์ฐพ์๋ด๊ณ ์ด๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๊ณ ์ฅ์ ์๋ฐฉํ๊ณ ์์ฐํ์ ๋ถ๋์ ์ต์ํํ๋๋ฐ ํ์ฉ๋ ์ ์๋ค. ์ต๊ทผ ์ ๋ณดํต์ ๊ธฐ์ ์ ๋ฐ๋ฌ๋ก ์ฅ๋น์ ์ํ ๊ด๋ จ ๋ฐ์ดํฐ๊ฐ ์ง์์ ์ผ๋ก ์์ง ๊ฐ๋ฅํด์ง์ผ๋ก์จ, ํด๋น ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ ์ํ ๋ชจ๋ํฐ๋ง์ด ์ฃผ๋ชฉ๋ฐ๊ณ ์๋ค. ํนํ ์ฅ๋น ์ํ์ ์ดํ ์์ ์ ๋ํ๋ด๋ฉฐ ํน์ ์์ธ์ ์๋ณ์ ์ง์ํ๋ ๊ด๋ฆฌ๋๋ ์ํ ๋ชจ๋ํฐ๋ง์ ๋ง์ด ํ์ฉ๋๊ณ ์๋ค. ํ์ง๋ง, ๋ค์์ ๋ณ์๋ฅผ ํ๋ฒ์ ๊ด๋ฆฌํ๋ ๋ค๋ณ๋ ๊ด๋ฆฌ๋์ ๊ฒฝ์ฐ, ๊ด๋ฆฌ๋ ์์ฒด์ ๊ด๋ จํ ์ฐ๊ตฌ๋ ํ๋ฐํ ์งํ๋์์ง๋ง ๋ค๋ณ๋ ๊ด๋ฆฌ๋๋ฅผ ๋ถํดํ๋ ๋ฐฉ๋ฒ๊ณผ ๊ด๋ จํ ์ฐ๊ตฌ๋ ๋ฏธ๋นํ๋ค. ๊ธฐ์กด์ ์ฐ๊ตฌ๋ค์ Hotellingโs T2 ๊ด๋ฆฌ๋ ๊ตญํ๋์ด ๋ถํด ๋ฐฉ๋ฒ์ ์๊ฐํ์์ง๋ง, ์๋ก์ด ๊ฐ๋ฐ๋ ํด๋ฌ์คํฐ๋ง ๊ธฐ๋ฐ์ ๊ด๋ฆฌ๋์ ๊ณง๋ฐ๋ก ์ ์ฉํ๊ธฐ์ ์ด๋ ค์์ด ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ํ ๋ฐ์ดํฐ์ ํ์ฉ ๊ฐ๋ฅํ ํด๋ฌ์คํฐ๋ง ๊ธฐ๋ฐ์ ๊ด๋ฆฌ๋ ๋ด์์ ๋ฐ์ํ ์ด์์น๋ฅผ ๋ถํดํ๋ ๋ฐฉ๋ฒ๋ก ์ ๋ชฉํ๋ก ํ๋ค. ๋ถํด ๋ฐฉ๋ฒ๋ก ์ ๊ธฐ์กด์ ์กด์ฌํ๋ Rungetโs T2 ๋ถํด ๊ธฐ๋ฒ์ ๊ธฐ๋ฐ์ผ๋ก ๋ค์ํ ๋ถํฌ์ ํ์ฉ ๊ฐ๋ฅํ ํน์ ๋งํ ๋ผ๋
ธ๋น์ค ๊ฑฐ๋ฆฌ์ ํด๋ฌ์คํฐ๋ง ๊ธฐ๋ฐ์ ๊ด๋ฆฌ๋์ ํน์ง์ ์ถ๊ฐํ์ฌ ๊ตฌ์ฑ๋๋ค. ์ฒซ๋ฒ์งธ ํน์ง์ ๋ํ์ ๊ณ ์ ์ด๋ฉฐ, ๋๋ฒ์งธ ํน์ง์ ๋ฐ์ดํฐ ๋ถํฌ์ ์ด์์ฑ์ด๋ค. ์ด์ ๊ฐ์ ํน์ง์ ๋ฐ์ํ ๋ฐฉ๋ฒ๋ก ์ ์ ์ฐจ๋ ๋ฌธํ๋ฆฌ๋ทฐ์ ์ฌ๋ก ์ฐ๊ตฌ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ต๋ํ ์ง์์ ๊ธฐ๋ฐ์ผ๋ก ์ ์๋์๋ค. ์ ์๋ ์ ์ฐจ๋ ํด๋ฌ์คํฐ๋ง ๊ธฐ๋ฐ์ ๊ด๋ฆฌ๋์์ ๋์ถ๋ ์ด์์น๋ฅผ ๋ถ์ํ์ฌ ์ด์์น์ ๋ฐ์์ ํฐ ์ํฅ์ ๋ฏธ์น ์์ธ์ ๋ฐ๊ฒฌํ ์ ์๋๋ก ์ง์ํ๋ค.
์ ์๋ ์ ์ฐจ๋ ์คํ ์ฐ๊ตฌ ๋ฐ ์ ๋ฐ ๋ฉ์ธ ์์ง์ ์ํ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ํ
์คํธ๋์์ผ๋ฉฐ, ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๋ค์ํ ๋ถํฌ์ ํ์ฉ๊ฐ๋ฅํจ์ ํ์ธํ์๋ค. ๊ฐ๋ฐ๋ ๋ถํด ๊ธฐ๋ฒ๊ณผ ํด๋น ์ ์ฐจ๋ ์ค์ ์ฅ๋น์ ์ํ๋ฅผ ๋ชจ๋ํฐ๋งํ๊ณ ์ ์ง๋ณด์๋ฅผ ์ํํ๋ ์์ง๋์ด๋ค์ด ์ฅ๋น์ ์ํ๋ฅผ ์ดํดํ๊ณ ์ด์ ์์ธ์ ํ์
ํ๋๋ฐ ํจ๊ณผ์ ์ผ๋ก ํ์ฉ๋ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค. ๋ํ, ๋ณธ ์ฐ๊ตฌ๋ ํ์ ์ ๊ด์ ์์ ํด๋ฌ์คํฐ๋ง ๊ธฐ๋ฐ์ ๊ด๋ฆฌ๋์ ํน์ง์ ํ์
ํ๊ณ ์ด๋ฅผ ํ์ฉํจ์ผ๋ก์จ, ๊ธฐ์กด์ ์กด์ฌํ์๋ Rungerโs T2 ๋ถํด ๋ฐฉ๋ฒ์ ๋ฐฉ๋ฒ๋ก ์ ํ์ฅํ๋ค๋ ์์๊ฐ ์๋ค.The condition of machines must be monitored in terms of quality and productivity, and out-of-control observations should be detected before the machineโs condition non-normality leads to major damage such as breakdown or explosion. As the wide deployment of Information and Communication Technique and additional process monitoring systems with various sensors provide massive amounts of data online, such data proliferation provides opportunities for real-time continuous monitoring of a machine and identifying the major contributors. Despite the applicability of large data to detect out-of-control observation with control chart, few efforts have been made to establish a decomposition methodology based on such data. In particular, the unique nature of a clustering algorithm-based control chart is not reflected in the existing studies of decomposition because they do not focus on the characteristics of recent collected data.
To fill the research gap, this research aimed to develop a decomposition methodology for a clustering algorithm-based control chart. The methodology included a procedure for decomposing the out-of-control observations that were collected from the clustering algorithm-based control chart. The decomposition methodology was devised based on insights gained from literature related to control chart, decomposition, and distance measurement. In addition, two case studies on decomposing out-of-control observations by utilizing simulation and vesselโs main engine data provided insight to devise existing methodology. In this context, the procedure of decomposition consisted of three steps: 1) out-of-control observation collection, 2) decomposition out-of-control observation, and 3) definition of significant major contributors. In particular, 2.1) fixation of centroid and 2.2) degree of non-normality were introduced to construct the proposed methodology.
The proposed methodology presented a systematic design process for decomposing out-of-control observations collected from clustering algorithm-based control chart via the efficient analyses of condition data. In addition, it can create a synergistic effect if incorporated into brand new machine learning methodologies to conduct condition-based maintenance. From a theoretical perspective, this research shows the contribution to extending the research area of Rungerโs T2 decomposition by combining existing methodologies and characteristics of recent data. From a practical perspective, results of this study can support engineers in maintaining the condition of machines efficiently
๋ฐ๋์ฒด ๊ณต์ ์ผ๋ก์ ํ๋ก์ธ์ค ๋ง์ด๋ ์ ์ฉ: ํ ์ ์กฐ์ ๋ถ์ผ ์ฌ๋ก๋ก๋ถํฐ์ ํต์ฐฐ
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์ผ์ผ ๊ฑด๊ฐํ์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ์ ๋ํ์ ๊ฑด๊ฐ ์ํ์ต๊ด ์ ํ ํ์ ์ฌ๋ก์ฐ๊ตฌ
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Identifying New Service Opportunities for Driving Safety Enhancement based on Driving Behavior Analysis: Commercial Vehicle Case in Korea
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๋ฐ๋์ฒด ์ ์กฐ๊ณต์ ์์์ ํ์ง ํฅ์์ ์ํ โBoB ๊ณต์ ๊ฒฝ๋กโํ๋ณด๊ตฐ ๋์ถ ์ฐ๊ตฌ
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A Case Study on the Establishment of Upper Control Limit to Detect Vesselโs Main Engine Failures using Multivariate Control Chart
Main engine failures in ship operations can lead to a major damage in terms of the vessel itself and the financial cost. In this respect, monitoring of a vesselโs main engine condition is crucial in ensuring the vessel's performance and reducing the maintenance cost. The collection of a huge amount of vessel operational data in the maritime industry has never been easier with the advent of advanced data collection technologies. Real-time monitoring of the condition of a vesselโs main engine has a potential to create significant value in maritime industry. This study presents a case study on the establishment of upper control limit to detect vesselโs main engine failures using multivariate control chart. The case study uses sample data of an ocean-going vessel operated by a major marine services company in Korea, collected in the period of 2016.05-2016.07. This study first reviews various main engine-related variables that are considered to affect the condition of the main engine, and then attempts to detect abnormalities and their patterns via multivariate control charts. This study is expected to help to enhance the vesselโs availability and provide a basis for a condition-based maintenance that can support proactive management of vesselโs main engine in the future.22Nkc
Condition Monitoring of Vessel Main Engines Using Multivariate Control Chart: A Case Study
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