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시스템 결함원인분석을 위한 데이터 로그 전처리 기법 연구
이양지(Yang Ji Lee),김덕영(Duck Young Kim),황민순(Min Soon Hwang),정영수(Young Soo Cheong) (사)한국CDE학회 2012 한국CDE학회 논문집 Vol.17 No.2
High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (ⅰ) the gathered data (event) logs are too large in general, and further (ⅱ) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.
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시스템 고장원인분석을 위한 데이터 로그 전처리 기법 연구
이양지(Lee Yang Ji),김덕영(Kim Duck Young),황민순(Hwang Min soon),정영수(Jung Young Soo) (사)한국CDE학회 2012 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2012 No.2
High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered data (event) logs are too large in general, and further (ii) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.
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이양지(Yang-Ji Lee),김덕영(Duck-Young Kim),황민순(Min-Soon Hwang),정영수(Young-Soo Cheong) 한국산업정보학회 2012 한국산업정보학회논문지 Vol.17 No.4
본 논문은 운항중인 선박에서 기록되어지는 운항정보 및 엔진 가동정보 등을 실시간으로 모니터링하고, 문제 발생 시에 그 근본원인을 찾아내어 민첩하게 대응할 수 있는 일련의 결함원인 분석 및 예방시스템 개발을 목적으로 한다. 결함분석을 위해서는 선박엔진의 주요기관에 부착된 센서들로부터 장기간 수집된 정보를 사용하게 되는데, 이 양이 매우 방대하며, 잡음 및 중복정보(Redundancy)가 너무 많이 포함되어, 수집된 센서 데이터를 바로 고장분석에 사용하기에는 어려움이 있다. 따라서 본 논문에서는 방대한 양의 데이터 중, 정보의 손실을 최소화하고 중요한 정보만을 추출하기 위해 "Equal-frequency binning"과 "Entropy" 기반의 데이터 필터링 방법에 관해 연구하였다. 실제로 시험운용 중인 선박엔진 데이터를 개발된 선박엔진 고장분석 소프트웨어를 이용하여 결함분석을 수행하여, 제안된 방법의 효용성을 검증한다. This paper aims to develop an event-driven failure analysis and prognosis system that is able to monitor ship status in real time, and efficiently react unforeseen system failures. In general, huge amount of recorded sensor data must be effectively interpreted for failure analysis, but unfortunately noise and redundant information in the gathered sensor data are obstacles to a successful analysis. This paper therefore applies "Equal-frequency binning" and "Entropy" techniques to extract only important information from the raw sensor data while minimizing information loss. The efficiency of the developed failure analysis system is demonstrated with the collected sensor data from a marine diesel engine.
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실시간 고장 예방을 위한 이벤트 기반 결함원인분석 시스템
이양지(Yang Ji Lee),김덕영(Duck Young Kim),황민순(Min Soon Hwang),정영수(Young Soo Cheong) (사)한국CDE학회 2013 한국CDE학회 논문집 Vol.18 No.4
This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.
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