• Title/Summary/Keyword: Remaining Useful Life Prediction

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Prognostic Technique for Pump Cavitation Erosion (펌프 캐비테이션 침식 예측진단)

  • Lee, Do Hwan;Kang, Shin Chul
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.37 no.8
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    • pp.1021-1027
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    • 2013
  • In this study, a prognostic technique for cavitation erosion that is applicable to centrifugal pumps is devised. To estimate the erosion states of pumps, damage rates are calculated based on cavitation noise measurements. The accumulated damage is predicted by using Miner's rule and the estimated damage undergone when coping with particular operating conditions. The remaining useful life (RUL) of the pump impellers is estimated according to the accumulated damage prediction and based on the assumption of future operating conditions. A Monte Carlo simulation is applied to obtain a prognostic uncertainty. The comparison of the prediction and the test results demonstrates that the developed method can be applied to predict cavitation erosion states and RUL estimates.

Durability Prediction for Concrete Structures Exposed to Carbonation Using a Bayesian Approach (베이지안 기법을 이용한 중성화에 노출된 콘크리트 구조물의 내구성 예측)

  • Jung, Hyun-Jun;Kim, Gyu-Seon;Ju, Min-Kwan;Lee, Sang-Cheol
    • Proceedings of the Korea Concrete Institute Conference
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    • 2009.05a
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    • pp.275-276
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    • 2009
  • This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

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Machine Learning Based State of Health Prediction Algorithm for Batteries Using Entropy Index (엔트로피 지수를 이용한 기계학습 기반의 배터리의 건강 상태 예측 알고리즘)

  • Sangjin, Kim;Hyun-Keun, Lim;Byunghoon, Chang;Sung-Min, Woo
    • Journal of IKEEE
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    • v.26 no.4
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    • pp.531-536
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    • 2022
  • In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).

A Study on the Health Index Based on Degradation Patterns in Time Series Data Using ProphetNet Model (ProphetNet 모델을 활용한 시계열 데이터의 열화 패턴 기반 Health Index 연구)

  • Sun-Ju Won;Yong Soo Kim
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.46 no.3
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    • pp.123-138
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    • 2023
  • The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

Health prognostics of stator Windings in Water-Cooled Generator using Fick's second law (Fick's second law 를 이용한 수냉식 발전기 고정자 권선의 건전성 예지)

  • Youn, Byeng D.;Jang, Beom-Chan;Kim, Hee-Soo;Bae, Yong-Chae
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2014.10a
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    • pp.533-538
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    • 2014
  • Power generator is one of the most important component of electricity generation system to convert mechanical energy to electrical energy. I t designed robustly to maintain high system reliability during operation time. But unexpected failure of the power generator could happen and it cause huge amount of economic and social loss. To keep it from unexpected failure, health prognostics should be carried out In this research, We developed a health prognostic method of stator windings in power generator with statistical data analysis and degradation modeling against water absorption. We divided whole 42 windings into two groups, absorption suspected group and normal group. We built a degradation model of absorption suspected winding using Fick's second law to predict upcoming absorption data. Through the analysis of data of normal group, we could figure out the distribution of data of normal windings. After that, we can properly predict absorption data of normal windings. With data prediction of two groups, we derived upcoming Directional Mahalanobis Distance (DMD) of absorption suspected winding and time vs DMD curve. Finally we drew the probability distribution of Remaining Useful Life of absorption suspected windings.

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An advanced technique to predict time-dependent corrosion damage of onshore, offshore, nearshore and ship structures: Part I = generalisation

  • Kim, Do Kyun;Wong, Eileen Wee Chin;Cho, Nak-Kyun
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.12 no.1
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    • pp.657-666
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    • 2020
  • A reliable and cost-effective technique for the development of corrosion damage model is introduced to predict nonlinear time-dependent corrosion wastage of steel structures. A detailed explanation on how to propose a generalised mathematical formulation of the corrosion model is investigated in this paper (Part I), and verification and application of the developed method are covered in the following paper (Part II) by adopting corrosion data of a ship's ballast tank structure. In this study, probabilistic approaches including statistical analysis were applied to select the best fit probability density function (PDF) for the measured corrosion data. The sub-parameters of selected PDF, e.g., the largest extreme value distribution consisting of scale, and shape parameters, can be formulated as a function of time using curve fitting method. The proposed technique to formulate the refined time-dependent corrosion wastage model (TDCWM) will be useful for engineers as it provides an easy and accurate prediction of the 1) starting time of corrosion, 2) remaining life of the structure, and 3) nonlinear corrosion damage amount over time. In addition, the obtained outcome can be utilised for the development of simplified engineering software shown in Appendix B.

A Comparison Study of Model Parameter Estimation Methods for Prognostics (건전성 예측을 위한 모델변수 추정방법의 비교)

  • An, Dawn;Kim, Nam Ho;Choi, Joo Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.25 no.4
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    • pp.355-362
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    • 2012
  • Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

A Study on the Metadata Schema for the Collection of Sensor Data in Weapon Systems (무기체계 CBM+ 적용 및 확대를 위한 무기체계 센서데이터 수집용 메타데이터 스키마 연구)

  • Jinyoung Kim;Hyoung-seop Shim;Jiseong Son;Yun-Young Hwang
    • Journal of Internet Computing and Services
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    • v.24 no.6
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    • pp.161-169
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    • 2023
  • Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.

Development of Borough Road Pavement Condition Evaluation Criteria and Prediction Index (자치구 포장상태평가등급 기준 개선 및 포장상태 예측지수 개발)

  • Lee, Sang Yum;Jeon, Jin Ho
    • International Journal of Highway Engineering
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    • v.18 no.6
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    • pp.115-122
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    • 2016
  • OBJECTIVES : This study develops an evaluation method, which is useful to inspect pavement condition of specific boroughs. This is because pavement condition is broadly divided into five grades via visual inspection, which does not consider the types of deteriorations, and is decided by an investigator having a subjective viewpoint. This visual inspection method is not a satisfactory method for accurate maintenance when various deteriorations occur. METHODS : The performance model considers several factors such as crack, rutting, and IRI. This method is also modified from borough SPI based on SPI (Seoul Pavement Index). Considering limited budget of borough, PI (prediction index) is suggested, which is related to the grade of pavement condition evaluation and type of materials. Practical correlation review is also conducted with statistical verification by using the Monte Carlo simulation. RESULTS : The results of the study show that modified criteria are reasonable. First, the comparison between the visual inspection result and the SPI result indicates that the R-square value is sufficiently high. Second, through the common section, each evaluation method could be compared, and the result shows considerable similarity, which increases when the range is modified. Finally, PI for predicting remaining life and the random number SPI have common parts, which means that each indicator would be adequate to be used as an evaluation method. CONCLUSIONS : Comparison and analysis results show that the developed evaluation method is reasonable for specific boroughs where financial support is inadequate for the evaluation process by using the newer equipment. Moreover, for more accurate evaluation method, previous visual inspection data should be utilized, and the database of inspection equipment have to be collected.

Prediction Model for Toothache Occurrence in College Students by using Oral Hygiene Habits and the CART Model (대학생의 구강건강관리실태와 CART모델을 이용한 치통발생예측)

  • Kim, Nam-Song;Lim, Kun-Ok
    • Journal of dental hygiene science
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    • v.9 no.4
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    • pp.419-426
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    • 2009
  • The occurrence of toothache signals the malfunction in oral health, which allows the detection of any abnormal condition in the oral cavity at an early stage to prevent the condition from worsening, and thus can act as a preventive measure. This study has looked into the status of oral health management in relation to toothache through the structured survey administered to 235 college students. Based on the survey results, this study aimed at comparing the toothache occurrence prediction between regression analysis and CART model in order to clarify the relationship between the factors of oral health management habits that contribute to toothache occurrence. According to the result, there was a difference between the present health status and the health status of the past year depending on the presence or non-presence of toothache occurrence (p<0.05). There was a difference in the regularity of meal time depending on the presence non-presence of toothache occurrence from the dietary habits of the research subjects (p<0.05). As for the presence or non-presence of toothache occurrence from the oral hygiene habits of the research subject, there was a difference between the occurrence and nonoccurrence of bleeding during brushing or flossing (p<0.05). According to the results of regression analysis, no factors were signifiant in the relationship with the presence or non-presence of toothache occurrence from the status of life habits and oral hygiene habits. 70% of the researched group was randomly selected as the sample for generating an analytical model and the remaining 30% was used as the sample for generating an evaluation model. According to the results of CART model, the occurrence of toothache was higher in the case of irregular meal time and poor current health condition than the case of average or satisfactory health condition. The above results imply that CART model is very useful technique in predicting toothache occurrence compared to regression analysis, and suggests that CART model could be very useful in predicting other oral diseases including toothache.

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