• 제목/요약/키워드: Remaining Useful Life Prediction

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EMD-CNN-LSTM을 이용한 하이브리드 방식의 리튬 이온 배터리 잔여 수명 예측 (Remaining Useful Life Prediction for Litium-Ion Batteries Using EMD-CNN-LSTM Hybrid Method)

  • 임제영;김동환;노태원;이병국
    • 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.

Data-Driven Approach for Lithium-Ion Battery Remaining Useful Life Prediction: A Literature Review

  • Luon Tran Van;Lam Tran Ha;Deokjai Choi
    • 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.

k-NN을 활용한 터보팬 엔진의 잔여 유효 수명 예측 모델 제안 (A Proposal of Remaining Useful Life Prediction Model for Turbofan Engine based on k-Nearest Neighbor)

  • 김정태;서양우;이승상;김소정;김용근
    • 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.

잔여 유효 수명 예측 모형과 최소 수리 블록 교체 모형에 기반한 비용 최적 예방 정비 방법 (Cost-optimal Preventive Maintenance based on Remaining Useful Life Prediction and Minimum-repair Block Replacement Models)

  • 주영석;신승준
    • 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.

Prediction of Remaining Useful Life (RUL) of Electronic Components in the POSAFE-Q PLC Platform under NPP Dynamic Stress Conditions

  • Inseok Jang;Chang Hwoi Kim
    • 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.

Remaining useful life prediction for PMSM under radial load using particle filter

  • Lee, Younghun;Kim, Inhwan;Choi, Sikgyoung;Oh, Jaewook;Kim, Namsu
    • 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.

Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

  • Hung-Cuong Trinh;Van-Huy Pham;Anh H. Vo
    • 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.

Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks

  • Utah, M.N.;Jung, J.C.
    • Nuclear Engineering and Technology
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    • 제52권9호
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    • pp.1998-2008
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    • 2020
  • Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

An improved regularized particle filter for remaining useful life prediction in nuclear plant electric gate valves

  • Xu, Ren-yi;Wang, Hang;Peng, Min-jun;Liu, Yong-kuo
    • Nuclear Engineering and Technology
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    • 제54권6호
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    • pp.2107-2119
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    • 2022
  • Accurate remaining useful life (RUL) prediction for critical components of nuclear power equipment is an important way to realize aging management of nuclear power equipment. The electric gate valve is one of the most safety-critical and widely distributed mechanical equipment in nuclear power installations. However, the electric gate valve's extended service in nuclear installations causes aging and degradation induced by crack propagation and leakages. Hence, it is necessary to develop a robust RUL prediction method to evaluate its operating state. Although the particle filter(PF) algorithm and its variants can deal with this nonlinear problem effectively, they suffer from severe particle degeneracy and depletion, which leads to its sub-optimal performance. In this study, we combined the whale algorithm with regularized particle filtering(RPF) to rationalize the particle distribution before resampling, so as to solve the problem of particle degradation, and for valve RUL prediction. The valve's crack propagation is studied using the RPF approach, which takes the Paris Law as a condition function. The crack growth is observed and updated using the root-mean-square (RMS) signal collected from the acoustic emission sensor. At the same time, the proposed method is compared with other optimization algorithms, such as particle swarm optimization algorithm, and verified by the realistic valve aging experimental data. The conclusion shows that the proposed method can effectively predict and analyze the typical valve degradation patterns.

거대언어모델 기반 SHAP 분석을 이용한 리튬 이온 배터리 잔존 수명 예측 기법 해석 (Large Language Model-based SHAP Analysis for Interpretation of Remaining Useful Life Prediction of Lithium-ion Battery)

  • 이재승;유제혁
    • 한국산업정보학회논문지
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    • 제29권5호
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    • pp.51-68
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    • 2024
  • 이동성을 갖춘 전자 장비에 에너지원을 공급하는 리튬 이온 배터리를 안전하게 운영하기 위해서는 배터리의 잔존 수명을 정확히 예측하는 것이 중요하다. 최근, 기계학습 기술의 발달로, 인공지능 기반의 배터리 잔존 수명 예측 모델이 활발히 연구되고 있다. 하지만, 기존 모델들은 모델 내부에서 일어나는 추론 과정을 알 수 없어 기계학습을 통해 예측된 값을 완전히 신뢰하고 사용하는 데 제약이 있었다. 이를 해결하기 위해 여러 설명가능한 인공지능 기법이 제안되었지만, 이러한 기법들은 단순히 결과를 그래프 형태로 시각화하였기에 사용자가 직접 그래프를 분석해야 했다. 이에 본 논문에서는 거대언어모델에 기반한 SHAP 분석을 이용하여 예측 모델의 추론 과정을 텍스트 형태로 해석하는 설명가능한 리튬 이온 배터리 잔존 수명 예측 기법을 제안한다. 공개 리튬 이온 배터리 데이터셋을 이용한 실험 결과, 거대언어모델 기반 SHAP 분석을 통해 모델의 예측 근거를 텍스트 형태로 구체화하여 이해할 수 있었다.