• 스마트미디어저널
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• 제11권11호
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• Smart Structures and Systems
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• 제29권6호
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• pp.799-805
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• 2022

Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.

• 산업경영시스템학회지
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• 제45권3호
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• 풍력에너지저널
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• 제14권3호
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• pp.34-42
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• 2023

The reduction of operation and maintenance (O&M) costs is a critical factor in determining the competitiveness of wind energy. Predictive maintenance based on the estimation of remaining useful life (RUL) is a key technology to reduce logistic costs and increase the availability of wind turbines. Although a mechanical component usually has sudden changes during operation, most RUL estimation methods use the trend of a state index over the whole operation period. Therefore, overestimation of RUL causes confusion in O&M plans and reduces the effect of predictive maintenance. In this paper, two RUL estimation methods (load based and data driven) are proposed for the bearings of a wind turbine with the results of trend classification, which differentiates constant and increasing states of the state index. The proposed estimation method is applied to a bearing degradation test, which shows a conservative estimation of RUL.

• Nuclear Engineering and Technology
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• 제56권5호
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• pp.1863-1873
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• 2024

In the Korean domestic nuclear industry, to analyze the reliability of instrumentation and control (I&C) systems, the failure rates of the electronic components constituting the I&C systems are predicted based on the MIL-HDBK-217F standard titled 'Reliability Prediction of Electronic Equipment'. Based on these predicted failure rates, the mean time to failure of the I&C systems is calculated to determine the replacement period of the I&C systems. However, this conventional approach to the prediction of electronic component failure rates assumes that factors affecting the failure rates such as ambient temperature and operating voltage are static constants. In this regard, the objective of this study is to propose a prediction method for the remaining useful life (RUL) of electronic components considering mean time to failure calculations reflecting dynamic environments, such as changes in ambient temperature and operating voltage. Results of this study show that the RUL of electronic components can be estimated depending on time-varying temperature and electrical stress, implying that the RUL of electronic components can be predicted under dynamic stress conditions.

• Journal of Power Electronics
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• 제17권5호
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• 한국산학기술학회논문지
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• 제22권4호
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• pp.611-620
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• 2021

정비 산업은 사후정비, 예방정비를 거쳐, 상태기반 정비를 중심으로 진행되고 있다. 상태기반 정비는 장비의 상태를 파악하여, 최적 시점에서의 정비를 수행한다. 최적의 정비 시점을 찾기 위해서는 장비의 상태, 즉 잔여 유효 수명을 정확하게 파악하는 것이 중요하다. 이에, 본 논문은 시뮬레이션 데이터(C-MAPSS)를 사용한 터보팬 엔진의 잔여 유효수명(RUL, Remaining Useful Life) 예측 모델을 제시한다. 모델링을 위해 C-MAPSS(Commercial Modular Aero-Propulsion System Simulation) 데이터를 전처리, 변환, 예측하는 과정을 거쳤다. RUL 임계값 설정, 이동평균필터 및 표준화를 통해 데이터 전처리를 수행하였고, 주성분 분석(Principal Component Analysis)과 k-NN(k-Nearest Neighbor)을 활용하여 잔여 유효 수명을 예측하였다. 최적의 성능을 도출하기 위해, 5겹 교차검증기법을 통해 최적의 주성분 개수 및 k-NN의 근접 데이터 개수를 결정하였다. 또한, 사전 예측의 유용성, 사후 예측의 부적합성을 고려한 스코어링 함수(Scoring Function)를 통해 예측 결과를 분석하였다. 마지막으로, 현재까지 제시되어온 뉴럴 네트워크 기반의 알고리즘과 예측 성능 비교 및 분석을 통해 k-NN 활용 모델의 유용성을 검증하였다.

• 한국통신학회논문지
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• 제42권4호
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• pp.939-949
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• 2017

고장 예지 및 건전성 관리 기술(Prognostics and Health Management; PHM)은 시스템의 현재 상태를 진단하고 향후 발생 가능한 고장 시점을 신뢰성 있게 예지하는 기술로써 유지 보수 비용의 절감 및 시스템의 안정성 향상을 꾀하고자 하는 다양한 산업분야에서 활발하게 이용되고 있다. 스마트 그리드의 에너지 저장장치, 전기차, 스마트폰, 항공우주산업 등 광범위한 사용처에서 중요한 에너지원으로 사용되고 있는 배터리 또한 성능 저하 및 폭발의 위험성으로부터 자유로울 수 없기 때문에 이러한 고장 예지 및 건전성 관리 기술이 반드시 적용되어야 할 어플리케이션이다. 본 논문에서는 PHM의 기본적인 개념을 소개함과 동시에 배터리의 잔존 유효 수명(Remaining Useful Life; RUL)을 예측하는 각종 알고리즘 및 성능 평가 지표 서술에 초점을 맞추도록 한다. 더불어 배터리의 기능적 동작 원리 및 전기화학 기반의 모델링에 대한 설명을 통해 향후 잠재적인 가능성을 지닌 배터리의 전반적인 특성에 대한 깊은 이해 및 응용 기술에 대한 통찰력을 제시하고자 한다.

• 전력전자학회논문지
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• 제27권1호
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.