• Smart Structures and Systems
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• v.29 no.6
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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.

• Journal of Power Electronics
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• v.17 no.5
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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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.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.311-312
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• 2016

본 논문에서는 사이즈가 다른 고출력 원통형 리튬이온 배터리의 Remaining Useful Life(RUL)을 방전용량 기반으로 전기적 특성분석을 실시하였다. 우선, 배터리의 충/방전이 계속될 시 용량이 어떻게 변화하는지 실험해보았으며, 만충 전압(Fully Charged)에서 만방 전압(Fully Discharged) 까지의 각각의 State-Of-Charge(SOC)에서 Hybrid Pulse Power Characterization (HPPC) Test를 이용해 충전 저항과 방전 저항을 구하여, 용량과 저항의 관계를 파악하였으며, 배터리 RUL을 알기 위한 기본 정보를 확보했다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.436-442
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• 2020

ESS(energy storage system) has recently become an important power source in various areas due to increased renewable energy resources. The more ESS is used, the less the effective capacity of the ESS. Therefore, it is important to manage the remaining useful lifetime(RUL). RUL can be checked regularly by inspectors, but it is common to be monitored and estimated by an automated monitoring system. The accurate state estimation is important to ESS operator for economical and efficient operation. RUL estimation model usually requires complex mathematical calculations consisting of cycle aging and calendar aging that are caused by the operation frequency and over time, respectively. A lightweight RUL estimation model is required to be embedded in low-performance processors that are installed on ESS. In this paper, a lightweight ESS RUL estimation model is proposed to operate on low-performance micro-processors. The simulation results show less than 1% errors compared to the original RUL model case. In addition, a performance analysis is conducted based on ATmega 328. The results show 76.8 to 78.3 % of computational time reduction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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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.

• Smart Media Journal
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• v.11 no.11
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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.

• Journal of Chest Surgery
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• v.33 no.4
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• pp.324-328
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• 2000

Multiple primary lung cancer is not common and classified as a synchronous primary lung cancer and a metachronous primary lung cancer. We experienced one case of the triple synchronous primary lung cancer of different cell types. We conducted right pneumonectomy for preoperative diagnosed neuronendocrine tumor of the RUL and adenocarcinoma of the RLL. Pathologic examination revealed the carcinoid tumor of RUL bronchus, the squamous carcinoma of the RML and the adenocarcinoma of the RLL.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.1-11
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• 2021

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

• The Journal of Korean Medicine
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• v.24 no.4
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• pp.87-91
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• 2003

Objectives: To examine the relaxational response to the water extract of Rheum palmatum L. and Rheum undulatum L. on rat thoracic aorta and abdominal aorta. Methods: Segments of thoracic aorta and abdominal aorta obtained from rats immediately after delivery were mounted in organ baths superfused on a polygraph. Results : We found that the thoracic aorta segments responded to the water extract of genus Rheum with a dose-dependent vasorelaxation. At $10^{-4}$ M 5-HT, the maximal contraction force was 93.5% of the maximum KCI-response. The 5-HT induced contractions at $10^{-4}$ M were inhibited by 86.4% and 62.1 % after addition of the high concentrations of R. palmatum root (RPR) and leaf (RPL) and R. undulatum root (RUR) and leaf (RUL). At 10 mg/ml RPR and RUR, the relaxational response at thoracic aorta and abdominal aorta with and without endothelium were 86.4%, 83.2%, 85.8%, and 62.1% of the maximum 5-HT induced contraction. Conclusion: Our result showed that RPL and RUL induced dose-dependent vasorelaxation on rat thoracic aorta and abdominal aorta, and that RPL and RUL roots have more potent effects than the leaves.