XAI 기반 발전설비 고장 기록 데이터 품질 향상 시스템 개발

• Published in: 品質經營學會誌 Vol. 52; no. 3; pp. 479 - 493
• Main Authors: 김유림, Kim Yu Rim, 박정인, Park Jeong In, 박동현, Park Dong Hyun, 강성우, Kang Sung Woo
• Format: Journal Article
• Language: Korean
• Published: 한국품질경영학회 30.09.2024
• Subjects: LIME; Natural Language Processing; Power Generation Facilities; Quality Improvement; XAI; 학제간연구; XAI; Quality Improvement; LIME; Power Generation Facilities; Natural Language Processing
• ISSN: 1229-1889; 2287-9005
• DOI: 10.7469/JKSQM.2024.52.3.479

Purpose: The deterioration in the quality of failure history data due to differences in interpretation of failures among workers at power plants and the lack of consistency in the way failures are recorded negatively impacts the efficient operation of power plants. The purpose of this study is to propose a system that classifies power generation facilities failures consistently based on the failure history text data created by the workers. Methods: This study utilizes data collected from three coal unloaders operated by Korea Midland Power Co., LTD, from 2012 to 2023. It classifies failures based on the results of Soft Voting, which incorporates the prediction probabilities derived from applying the predict_proba technique to four machine learning models: Random Forest, Logistic Regression, XGBoost, and SVM, along with scores obtained by constructing word dictionaries for each type of failure using LIME, one of the XAI (Explainable Artificial Intelligence) methods. Through this, failure classification system is proposed to improve the quality of power generation facilities failure history data. Results: The results of this study are as follows. When the power generation facilities failure classification system was applied to the failure history data of Continuous Ship Unloader, XGBoost showed the best performance with a Macro_F1 Score of 93%. When the system proposed in this study was applied, there was an increase of up to 0.17 in the Macro_F1 Score for Logistic Regression compared to when the model was applied alone. All four models used in this study, when the system was applied, showed equal or higher values in Accuracy and Macro_F1 Score than the single model alone. Conclusion: This study propose a failure classification system for power generation facilities to improve the quality of failure history data. This will contribute to cost reduction and stability of power generation facilities, as well as further improvement of power plant operation efficiency and stability.