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ÇѱÛÁ¦¸ñ(Korean Title) |
¼³ºñ ¿À·ù À¯Çü ±¸Á¶È¸¦ À§ÇÑ Àΰø½Å°æ¸Á ±â¹Ý ±¸Àý ³×Æ®¿öÅ© ±¸Ãà ¹æ¹ý |
¿µ¹®Á¦¸ñ(English Title) |
An Artificial Neural Network Based Phrase Network Construction Method for Structuring Facility Error Types |
ÀúÀÚ(Author) |
³ë¿µÈÆ
ÃÖÀº¿µ
ÃÖ¿¹¸²
Younghoon Roh
Eunyoung Choi
Yerim Choi
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¿ø¹®¼ö·Ïó(Citation) |
VOL 19 NO. 06 PP. 0021 ~ 0029 (2018. 12) |
Çѱ۳»¿ë (Korean Abstract) |
4Â÷ »ê¾÷Çõ¸í ½Ã´ëÀÇ µµ·¡¿Í ÇÔ²² ½º¸¶Æ® ÆÑÅ丮ÀÇ °³³äÀÌ ´ëµÎµÇ¸é¼ ¼³ºñ°¡µ¿·ü°ú »ý»ê¼º¿¡ ¾Ç¿µÇâÀ» ¹ÌÄ¡´Â ¼³ºñ ¿À·ùÀÇ ¹ß»ýÀ» µ¥ÀÌÅÍ ºÐ¼® ±â¹ýÀ» ÅëÇØ ¿¹ÃøÇÏ°íÀÚ ÇÏ´Â ³ë·ÂÀÌ ÀÌ·ç¾îÁö°í ÀÖ´Ù. µ¥ÀÌÅÍ ºÐ¼® ±â¹ýÀ» È°¿ëÇÏ¿© ¼³ºñ ¿À·ù¸¦ ¿¹ÃøÇϱâ À§Çؼ´Â ¼³ºñ ¿À·ù°¡ ¹ß»ýÇÑ »óȲ°ú ¼³ºñ ¿À·ù À¯ÇüÀ» ¸í½ÃÇÑ µ¥ÀÌÅÍÀÎ ¼³ºñ ¿À·ù ÀÌ·ÂÀÌ ÇÊ¿äÇÏ´Ù. ÇÏÁö¸¸ ¸¹Àº Á¦Á¶ ÇöÀå¿¡¼´Â ¼³ºñ¿À·ù À¯ÇüÀÌ Á¤È®ÇÏ°Ô Á¤ÀÇ/ºÐ·ù°¡ µÇÁö ¾Ê¾Æ ¼³ºñ¸¦ ¿î¿µÇÏ´Â ÀÛ¾÷ÀÚ°¡ ÀÚ½ÅÀÇ °æÇèÀû ÆÇ´Ü¿¡ ÀÇ°ÅÇÏ¿© Á¤ÇüȵÇÁö ¾ÊÀº ÅؽºÆ®ÀÇ ÇüÅ·Π¼³ºñ ¿À·ù À¯ÇüÀ» ÀÛ¼ºÇÏ°í, ÀÌ¿¡ µû¶ó µ¥ÀÌÅÍ ºÐ¼® ±â¹ýÀÇ Àû¿ëÀÌ ¾î·Æ´Ù. µû¶ó¼ º» ³í¹®¿¡¼´Â ¼ö±â·Î ÀÛ¼ºµÈ ¼³ºñ ¿À·ùÀÌ·ÂÀ» È°¿ëÇÏ¿© ¼³ºñ ¿À·ù À¯ÇüÀ» ÆľÇÇÏ°í ±¸Á¶ÈÇϱâ À§ÇÑ ±¸Àý ³×Æ®¿öÅ© ±¸Ãà ¹æ¹ýÀ» Á¦¾ÈÇÏ°íÀÚ ÇÑ´Ù. ±¸Ã¼ÀûÀ¸·Î, ´Ü¾î¸¦ ¾²ÀÓ»õ¿¡ µû¶ó ºÐ·ùÇÑ ¿ëµµ µñ¼Å³Ê¸®¸¦ È°¿ëÇÏ¿© ºñÁ¤ÇüÀÇ ÅؽºÆ® µ¥ÀÌÅͷκÎÅÍ ¼³ºñ ¿À·ù À¯ÇüÀ» ÀǹÌÇÏ´Â ±¸ÀýÀ» ÃßÃâÇÏ°í, ÃßÃâµÈ ±¸Àý °£ÀÇ À¯»çµµ¸¦ °è»êÇÏ¿© ³×Æ®¿öÅ©¸¦ ±¸ÃàÇÑ´Ù. Á¦¾ÈÇÏ´Â ¹æ¹ýÀÇ ¼º´ÉÀ» ½ÇÁ¦ Á¦Á¶ ±â¾÷ÀÇ ¼³ºñ ¿À·ù ÀÌ·Â µ¥ÀÌÅ͸¦ È°¿ëÇÏ¿© °ËÁõÇÏ¿´À¸¸ç, º» ¿¬±¸ÀÇ °á°ú´Â ÅؽºÆ® µ¥ÀÌÅÍ¿¡ ±â¹ÝÇÑ ¼³ºñ ¿À·ù À¯Çü ±¸Á¶È¿Í ³ª¾Æ°¡¼´Â ¼³ºñ ¿À·ù ¹ß»ý ¿¹Ãø¿¡ ÀÌ¿ëÇÒ ¼ö ÀÖÀ» °ÍÀ» ±â´ëÇÑ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
In the era of the 4-th industrial revolution, the concept of smart factory is emerging. There are efforts to predict the occurrences of facility errors which have negative effects on the utilization and productivity by using data analysis. Data composed of the situation of a facility error and the type of the error, called the facility error log, is required for the prediction. However, in many manufacturing companies, the types of facility error are not precisely defined and categorized. The worker who operates the facilities writes the type of facility error in the form with unstructured text based on his or her empirical judgement. That makes it impossible to analyze data. Therefore, this paper proposes a framework for constructing a phrase network to support the identification and classification of facility error types by using facility error logs written by operators. Specifically, phrase indicating the types are extracted from text data by using dictionary which classifies terms by their usage. Then, a phrase network is constructed by calculating the similarity between the extracted phrase. The performance of the proposed method was evaluated by using real-world facility error logs. It is expected that the proposed method will contribute to the accurate identification of error types and to the prediction of facility errors.
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Å°¿öµå(Keyword) |
½º¸¶Æ® ÆÑÅ丮
¼³ºñ ¿À·ù
±¸Àý ³×Æ®¿öÅ©
ÅؽºÆ® ¸¶ÀÌ´×
Àΰø½Å°æ¸Á
word2vec
Smart factory
Facility error
Phrase network
Text mining
Artificial neural network
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