• 품질경영학회지
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• 제43권4호
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• pp.573-588
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• 2015

Purpose: For improving result of estimated remaining useful life in Prognostics and Health Management (PHM), a system which is able to consider a lot of environment and load data is required. Method: A load profile monitoring system was presented based on cloud computing for gathering and processing raw data which is included environment and load data. Result: Users can access results of load profile information on the Internet. The developed system provides information which consists of distribution of load data, basic statistics, etc. Conclusion: We developed the load profile monitoring system for considering much environment and load data. This system has advantages such as improving accessibility through smart device, reducing cost, and covering various conditions.

• 품질경영학회지
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• 제51권2호
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• pp.223-245
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• 2023

Purpose: This study aim to identify the trends in AI-based PHM technology that can enhance reliability and minimize costs. Furthermore, this research provides valuable guidelines for future studies in various industries Methods: In this study, I collected and selected AI-based PHM studies, established classification criteria, and analyzed research trends based on classified fields and techniques. Results: Analysis of 125 domestic studies revealed a greater emphasis on machinery in both diagnosis and prognosis, with more papers dedicated to diagnosis. various algorithms were employed, including CNN for image diagnosis and frequency analysis for signal data. LSTM was commonly used in prognosis for predicting failures and remaining life. Different industries, data types, and objectives required diverse AI techniques, with GAN used for data augmentation and GA for feature extraction. Conclusion: As studies on AI-based PHM continue to grow, selecting appropriate algorithms for data types and analysis purposes is essential. Thus, analyzing research trends in AI-based PHM is crucial for its rapid development.

• 한국ITS학회 논문지
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• 제16권2호
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• pp.128-139
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• 2017

철도는 항공기, 선박 등과 더불어 대표적 대중교통 수단으로서 최근 고속 철도의 등장으로 인해 그 비중이 점점 더 높아지고 있으며, 아울러 대형사고의 위험 또한 증가하고 있다. 이중에서 철도 차량의 차축 베어링은 높은 안전성이 요구되는 부품으로서 최근 이의 고장예측을 위한 건전성 관리기술(Prognostics and Health Management, PHM)에 많은 연구가 집중되고 있다. PHM은 센서를 통해 얻은 데이터로부터 결함관련 특징신호를 추출하고 현재의 고장수준 진단과 미래의 고장싯점을 예측하는 기술로서, 이중에서 가장 중요한 부분은 올바른 특징신호를 추출하는 것이다. 그러나 지금까지의 특징신호들은 잡음으로 인한 심한 변동이나 비단조 경향으로 인해 고장예측에 이용하기에 부족한 점이 있었다. 본 연구에서는 이를 극복하기 위해 주파수 에너지 이동현상을 기반으로 정보 엔트로피를 특징신호로 사용하는 새로운 특징신호 추출법을 개발하고 IEEE 2012 PHM 경진대회에서 공개된 FEMTO 베어링 수명시험 데이터를 대상으로 기존의 특징신호들과 고장예측 성능비교를 함으로써 그 우수성을 검증하였다.

• 기계저널
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• 제57권3호
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• pp.38-41
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• 2017

이 글에서는 인공지능을 이용한 공학시스템 고장진단 및 예지기술(PHM: Prognostics and Health Management)의 개념을 소개하고, 실제 적용 사례를 제시한다.

• 제어로봇시스템학회논문지
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• 제17권11호
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• pp.1082-1089
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• 2011

Most engineered systems are designed with high levels of system redundancies to satisfy required reliability requirements under adverse events, resulting in high systems' LCCs (Life-Cycle Costs). Recent years have seen a surge of interest and tremendous advance in PHM (Prognostics and Health Management) methods that detect, diagnose, and predict the effects of adverse events. The PHM methods enable proactive maintenance decisions, giving rise to adaptive reliability. In this paper, we present a RAP (Resilience Allocation Problem) whose goal is to allocate reliability and PHM efficiency to components in an engineering context. The optimally allocated reliability and PHM efficiency levels serve as the design specifications for the system RBDO (Reliability-Based Design Optimization) and the system PHM design, which can be used to derive the detailed design of components and PHM units. The RAP is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimally allocated reliability, PHM efficiency and redundancy for the given parameter settings.

• 한국기계가공학회지
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• 제19권9호
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• pp.47-52
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• 2020

With the 4th industrial revolution, condition monitoring using machine learning techniques has become popular among researchers. An overload due to complex operations causes several irregularities in MOSFETs. This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failure mode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the current is beyond the threshold value. Several features were extracted from the collected voltage signals that indicate the health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller sample space by using a principal component analysis (PCA). A robust machine learning algorithm, the support vector machine (SVM), was used to classify different health states of an SMPS, and the classification results are presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong fault diagnosis framework for an SMPS.

• Structural Monitoring and Maintenance
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• 제6권3호
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• 정보보호학회논문지
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• 제29권6호
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• pp.1403-1412
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• 2019

터빈, 배관 및 저장탱크와 같은 물리장치들의 경우 노후화뿐만 아니라 제어장치에 대한 사이버공격으로 인해 PLC(Programmable Logic Controller)와 같은 제어시스템의 보호 및 상태감시기능이 동작하지 않는 경우, 피해파급력이 크고, 가동 중지 시 그 비용 손실 또한 매우 크다. 가동 중인 물리장치의 작동을 중지하지 않고 간접적으로 상태감시를 함으로써 가용성을 유지하기 위한 방안으로써 온도, 가속도, 전류 등을 간접적으로 감지하고, 데이터들을 Influx DB에 저장하여 실시간으로 감시하는 시스템을 설계 및 구현한다. 실제 구현된 시스템으로부터 데이터를 얻고 이를 이용하여 이상상태를 감지할 수 있음을 검증하였다. 간접적 실시간 감시시스템의 범용화를 통해 데이터를 축적해 활용하면, 추가비용 없이 가동을 중지하지 않고 사용할 수 있을 뿐만 아니라 미리 고장을 예측하고 필요한 경우에만 조치를 취하는 고장예지기술, 이상상태를 이중으로 감시하는 신뢰도 높은 건전성 관리 기술을 통해 유지보수비용과 위험도를 대폭적으로 감소시키고, 보안위협에 대한 대비가 가능하다.

• 한국수소및신에너지학회논문집
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• 제29권4호
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• pp.339-356
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• 2018

Although fuel cell systems have advantages in terms of electric efficiency and environmental impact compared with conventional power systems, fuel cell systems have not been deployed widely due to their low reliability and high price. In order to guarantee the lifetime of 10 years, which is the commercialization goal of Polymer electrolyte fuel cells (PEFCs), it is necessary to improve durability and reliability through optimized operation and maintenance technologies. Due to the complexity of components and their degradation phenomena, it's not easy to develop and apply the diagnose and prognostic methodologies for PEFCs. The purpose of the paper is to show the current state on PEFC prognostic technology for condition based maintenance. For the prognostic of PEFCs, the model driven method, the data-driven, and the hybrid method can be applied. The methods reviewed in this paper can contribute to the development of technologies to reduce the life cycle cost of fuel cells and increase the reliability through prognostics-based health management system.

• Smart Structures and Systems
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• 제22권2호
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.