Journal articles: 'Useful lifetime prediction'

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Woo, Chang Su, and Hyun Sung Park. "Useful lifetime prediction of rubber component." Engineering Failure Analysis 18, no. 7 (2011): 1645–51. http://dx.doi.org/10.1016/j.engfailanal.2011.01.003.

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Kıyak, İsmail, Gökhan Gökmen, and Gökhan Koçyiğit. "Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)." Journal of Nanomaterials 2021 (March 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/6681335.

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Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

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da Costa, Paulo Roberto de Oliveira, Alp Akçay, Yingqian Zhang, and Uzay Kaymak. "Remaining useful lifetime prediction via deep domain adaptation." Reliability Engineering & System Safety 195 (March 2020): 106682. http://dx.doi.org/10.1016/j.ress.2019.106682.

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Woo, Chang Su, Sung Seen Choi, Seong Beom Lee, and Hyun Sub Kim. "Useful Lifetime Prediction of Rubber Components Using Accelerated Testing." IEEE Transactions on Reliability 59, no. 1 (2010): 11–17. http://dx.doi.org/10.1109/tr.2010.2042103.

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Bender, Amelie, and Walter Sextro. "Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties." PHM Society European Conference 6, no. 1 (2021): 11. http://dx.doi.org/10.36001/phme.2021.v6i1.2843.

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Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach.  In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties.  Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results.

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Sun, Fuqiang, Xiaoyang Li, Haitao Liao, and Xiankun Zhang. "A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component." Advances in Mechanical Engineering 9, no. 1 (2017): 168781401668596. http://dx.doi.org/10.1177/1687814016685963.

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Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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Won, Dong-Yeon, Hyun Su Sim, and Yong Soo Kim. "Prediction of Remaining Useful Lifetime of Membrane Using Machine Learning." Science of Advanced Materials 12, no. 10 (2020): 1485–91. http://dx.doi.org/10.1166/sam.2020.3788.

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We present a novel analytical procedure estimating the remaining useful life (RUL) of complex systems or facilities based on degradation data obtained over time; we consider the maintenance characteristics of units that are incompletely repaired. We develop an extended prognostic model that accurately predicts the RUL; we use machine-learning featuring smoothing, logging, variable transformation and clustering to this end. The performance of a general model was more predictable than that of an extended model. A linear regression (LR) method was superior in terms of root mean square error prediction and an artificial neural network (ANN) was superior in terms of prognostics and health management (PHM) scoring. The procedure is both practical and efficient, and can be deployed in various industries, yielding low-cost prognostics even in low-expertise domains. The procedure can be applied to high-risk industries, aiding management decision-making in terms of the establishment of optimal, preventative maintenance policies.

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Wu, Wenbo, Tianji Zou, Lu Zhang, Ke Wang, and Xuzhi Li. "Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty." Sensors 23, no. 23 (2023): 9535. http://dx.doi.org/10.3390/s23239535.

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Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.

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Srinivasan, R., and T. Paul Robert. "Remaining Useful Life Prediction on Wind Turbine Gearbox." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 57–65. http://dx.doi.org/10.35940/ijrte.e5145.019521.

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This research proposes a methodology to estimate the reliability of gearbox using life data analysis and predict the Lifetime Use Estimation (LUE). Life data analysis involves collection of historical field replacements of gearbox and perform statistical analysis such as Weibull analysis to estimate the reliability. Remaining useful life is estimated by using Cumulative damage model and data-driven methods. The first approach is based on the physics of failure models of degradation and the second approach is based on the operational, environmental & loads data provided by the design team which is translated into a mathematical model that represent the behavior of the degradation. Data-driven method is used in this research, where the different performance data from components are exploited to model the degradation's behavior. LUE is used to make key business decisions such as planning of spares, service cost and increase availability of wind turbine. Gearbox is the heart of the wind turbine and it is made up of several stages of helical/planetary gears. Performance data is acquired separately for each of these stages and LUE is calculated individually. The individual LUE is then rolled up to estimate the overall Lifetime Use Estimation of gearbox. This will identify the weak link which is going to fail first and the failure mode which is driving the primary failure can be identified. Finally, corrective measures can be planned accordingly. The cumulated damage and LUE are estimated by using Inverse power law damage model along with Miner’s rule.

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R, Srinivasan, and Paul Robert T. "Remaining Useful Life Prediction on Wind Turbine Gearbox." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 5 (2021): 57–65. https://doi.org/10.35940/ijrte.E5145.019521.

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<strong>Abstract</strong>—This research proposes a methodology to estimate the reliability of gearbox using life data analysis and predict the Lifetime Use Estimation (LUE). Life data analysis involves collection of historical field replacements of gearbox and perform statistical analysis such as Weibull analysis to estimate the reliability. Remaining useful life is estimated by using Cumulative damage model and data-driven methods. The first approach is based on the physics of failure models of degradation and the second approach is based on the operational, environmental & loads data provided by the design team which is translated into a mathematical model that represent the behavior of the degradation. Data-driven method is used in this research, where the different performance data from components are exploited to model the degradation's behavior. LUE is used to make key business decisions such as planning of spares, service cost and increase availability of wind turbine. Gearbox is the heart of the wind turbine and it is made up of several stages of helical/planetary gears. Performance data is acquired separately for each of these stages and LUE is calculated individually. The individual LUE is then rolled up to estimate the overall Lifetime Use Estimation of gearbox. This will identify the weak link which is going to fail first and the failure mode which is driving the primary failure can be identified. Finally, corrective measures can be planned accordingly. The cumulated damage and LUE are estimated by using Inverse power law damage model along with Miner’s rule.

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Tang, Hesheng, Xueyuan Guo, and Songtao Xue. "Uncertainty Quantification in Small-Timescale Model-Based Fatigue Crack Growth Analysis Using a Stochastic Collocation Method." Metals 10, no. 5 (2020): 646. http://dx.doi.org/10.3390/met10050646.

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Due to the uncertainties originating from the underlying physical model, material properties and the measurement data in fatigue crack growth (FCG) processing, the prediction of fatigue crack growth lifetime is still challenging. The objective of this paper was to investigate a methodology for uncertainty quantification in FCG analysis and probabilistic remaining useful life prediction. A small-timescale growth model for the fracture mechanics-based analysis and predicting crack-growth lifetime is studied. A stochastic collocation method is used to alleviate the computational difficulties in the uncertainty quantification in the small-timescale model-based FCG analysis, which is derived from tensor products based on the solution of deterministic FCG problems on sparse grids of collocation point sets in random space. The proposed method is applied to the prediction of fatigue crack growth lifetime of Al7075-T6 alloy plates and verified by fatigue crack-growth experiments. The results show that the proposed method has the advantage of computational efficiency in uncertainty quantification of remaining life prediction of FCG.

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Das, Kaushik, and Roushan Kumar. "Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review." Clean Energy 7, no. 6 (2023): 1268–81. http://dx.doi.org/10.1093/ce/zkad054.

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Abstract Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility. However, predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions. Additionally, state-of-health (SOH) and remaining-useful-life (RUL) predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance. Due to the non-linear behaviour of the health prediction of electric vehicle batteries, the assessment of SOH and RUL has therefore become a core research challenge for both business and academics. This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management, emphasizing state prediction and ageing prognostics. The objective is to provide comprehensive information about the evaluation, categorization and multiple machine-learning algorithms for predicting the SOH and RUL. Additionally, lithium-ion battery behaviour, the SOH estimation approach, key findings, advantages, challenges and potential of the battery management system for different state estimations are discussed. The study identifies the common challenges encountered in traditional battery management and provides a summary of how machine learning can be employed to address these challenges.

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Schuh, Peter, Hendrik Stern, and Kirsten Tracht. "Integration of Expert Judgment into Remaining Useful Lifetime Prediction of Components." Procedia CIRP 22 (2014): 109–14. http://dx.doi.org/10.1016/j.procir.2014.07.014.

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Magadán, L., J. C. Granda, and F. J. Suárez. "Robust prediction of remaining useful lifetime of bearings using deep learning." Engineering Applications of Artificial Intelligence 130 (April 2024): 107690. http://dx.doi.org/10.1016/j.engappai.2023.107690.

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Jin, Siyu, Xin Sui, Xinrong Huang, Shunli Wang, Remus Teodorescu, and Daniel-Ioan Stroe. "Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction." Electronics 10, no. 24 (2021): 3126. http://dx.doi.org/10.3390/electronics10243126.

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Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.

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Shi, Yun. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Transfer Learning and DAE-LSTM." Academic Journal of Science and Technology 9, no. 3 (2024): 181–88. http://dx.doi.org/10.54097/pdvk6n65.

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Predicting the Remaining Useful Life (RUL) of lithium-ion batteries is one of the core tasks in battery health management. This study aims to enhance the accuracy of RUL prediction by proposing a battery RUL prediction method that integrates source domain battery iteration transfer with Long Short-Term Memory (LSTM) neural networks. Firstly, multiple batteries with known degradation trends are utilized as source domain batteries, and combined with full-lifetime capacity degradation data to construct the Source Domain Battery Iterations Module (SIM) to obtain the optimal LSTM pre-training model. Secondly, the pre-trained model is transferred to the target domain and fine-tuned using the target domain training set. Finally, the fine-tuned LSTM pre-training model is applied to the task of predicting the capacity of target domain batteries, thereby completing the RUL prediction. The effectiveness of the algorithm is validated on three open-source datasets. Experimental results demonstrate that in scenarios where the source domain and target domain battery types are the same, the absolute error of RUL prediction is less than 2 cycles. Moreover, in cases where the battery types differ, except for the 20% prediction starting point, the RUL prediction error is less than 10 cycles, indicating a high level of prediction accuracy.

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Zraibi, Brahim, Mohamed Mansouri, and Chafik Okar. "Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries." E3S Web of Conferences 297 (2021): 01043. http://dx.doi.org/10.1051/e3sconf/202129701043.

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The prediction lifetime of a Lithium-ion battery is able to be utilized as an early warning system to prevent the battery’s failure that makes it very significant for assuring safety and reliability. This paper represents a benchmark study that compares its RUL prediction results of single and hybrid methods with similar articles. We suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), for predicting and improving the accuracy of the remaining useful life (RUL) of Lithium-ion battery. We selected three statistical indicators (MAE, R², and RMSE) to assess the results of performance prediction. Experimental validation is performed using the lithium-ion battery dataset from the NASA and results reveal that the effectiveness of the suggested hybrid method in reducing the prediction error and in achieving better RUL prediction performance compared to the other algorithms.

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Youness, Genane, and Adam Aalah. "An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction." Aerospace 10, no. 5 (2023): 474. http://dx.doi.org/10.3390/aerospace10050474.

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Prognosis and health management depend on sufficient prior knowledge of the degradation process of critical components to predict the remaining useful life. This task is composed of two phases: learning and prediction. The first phase uses the available information to learn the system’s behavior. The second phase predicts future behavior based on the available information of the system and estimates its remaining lifetime. Deep learning approaches achieve good prognostic performance but usually suffer from a high computational load and a lack of interpretability. Complex feature extraction models do not solve this problem, as they lose information in the learning phase and thus have a poor prognosis for the remaining lifetime. A new prepossessing approach is used with feature clustering to address this issue. It allows for restructuring the data into homogeneous groups strongly related to each other using a simple architecture of the LSTM model. It is advantageous in terms of learning time and the possibility of using limited computational capabilities. Then, we focus on the interpretability of deep learning prognosis using Explainable AI to achieve interpretable RUL prediction. The proposed approach offers model improvement and enhanced interpretability, enabling a better understanding of feature contributions. Experimental results on the available NASA C-MAPSS dataset show the performance of the proposed model compared to other common methods.

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Badger, Merete, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager. "Lifetime prediction of turbine blades using global precipitation products from satellites." Wind Energy Science 7, no. 6 (2022): 2497–512. http://dx.doi.org/10.5194/wes-7-2497-2022.

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Abstract. The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds, and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cost, it is relevant to estimate the blade lifetime for a given wind farm site prior to wind farm construction. Modeling tools for blade lifetime prediction require input time series of rainfall intensities and wind speeds in addition to a turbine-specific tip speed curve. In this paper, we investigate the suitability of satellite-based precipitation data from the Global Precipitation Measurement (GPM) mission in the context of blade lifetime prediction. We first evaluate satellite-based rainfall intensities from the Integrated Multi-Satellite Retrievals for GPM (IMERG) final product against in situ observations at 18 weather stations located in Germany, Denmark, and Portugal. We then use the satellite and in situ rainfall intensities as input to a model for blade lifetime prediction, together with the wind speeds measured at the stations. We find that blade lifetimes estimated with rainfall intensities from satellites and in situ observations are in good agreement despite the very different nature of the observation methods and the fact that IMERG products have a 30 min temporal resolution, whereas in situ stations deliver 10 min accumulated rainfall intensities. Our results indicate that the wind speed has a large impact on the estimated blade lifetimes. Inland stations show significantly longer blade lifetimes than coastal stations, which are more exposed to high mean wind speeds. One station located in mountainous terrain shows large differences between rainfall intensities and blade lifetimes based on satellite and in situ observations. IMERG rainfall products are known to have a limited accuracy in mountainous terrain. Our analyses also confirm that IMERG overestimates light rainfall and underestimates heavy rainfall. Given that networks of in situ stations have large gaps over the oceans, there is a potential for utilizing rainfall products from satellites to estimate and map blade lifetimes. This is useful as more wind power is installed offshore including floating installations very far from the coast.

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Moon, Seokho, Hansam Cho, Eunji Koh, et al. "Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction." Sustainability 14, no. 19 (2022): 12357. http://dx.doi.org/10.3390/su141912357.

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Remanufacturing has emerged as a way to solve production problems, as raw material costs increase and environmental pollution caused by discarded equipment occurs. The process can extend product lifetime and prevent waste of resources. In particular, it has economical efficiency for large equipment such as GIS (Gas Insulated Switchgear). The crucial points in remanufacturing are determining replaceable parts and economic valuation. To address these issues, we propose a framework for remanufacturing GIS with remaining lifetime prediction. We construct a regression model for remaining useful life (RUL) in the proposed framework using GIS sensor data. The cost of the replacement parts is estimated with the selected sensors. To validate the effectiveness of the proposed framework, we conducted accelerated life testing on a GIS for data acquisition and applied our framework. The experimental results demonstrate that the tree-based RUL regression model outperforms the others in prediction accuracy. In the simulation of part replacement, the important sensor-based decision-making improves RUL significantly.

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Zhang, Ming, Amirpiran Amiri, Yuchun Xu, Lucy Bastin, and Tony Clark. "Self-adaptive digital twin of fuel cell for remaining useful lifetime prediction." International Journal of Hydrogen Energy 89 (November 2024): 634–47. http://dx.doi.org/10.1016/j.ijhydene.2024.09.266.

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Chang, Yeong-Hwa, Yu-Chen Hsieh, Yu-Hsiang Chai, and Hung-Wei Lin. "Remaining-Useful-Life Prediction for Li-Ion Batteries." Energies 16, no. 7 (2023): 3096. http://dx.doi.org/10.3390/en16073096.

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This paper aims to establish a predictive model for battery lifetime using data analysis. The procedure of model establishment is illustrated in detail, including the data pre-processing, modeling, and prediction. The characteristics of lithium-ion batteries are introduced. In this study, data analysis is performed with MATLAB, and the open-source battery data are provided by NASA. The addressed models include the decision tree, nonlinear autoregression, recurrent neural network, and long short-term memory network. In the part of model training, the root-mean-square error, integral of the squared error, and integral of the absolute error are considered for the cost functions. Based on the defined health indicator, the remaining useful life of lithium-ion batteries can be predicted. The confidence interval can be used to describe the level of confidence for each prediction. According to the test results, the long short-term memory network provides the best performance among all addressed models.

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Du, Qing, Zhigang Zhan, Xiaofei Wen, et al. "A Hybrid Model to Assess the Remaining Useful Life of Proton Exchange Membrane Fuel Cells." Processes 11, no. 5 (2023): 1583. http://dx.doi.org/10.3390/pr11051583.

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Durability and remaining useful life (RUL) prediction techniques are ones of the key issues for proton exchange membrane fuel cell (FC) commercialization. Herein, the performance degradation of an FC is analyzed based on the whole lifetime experimental data (up to 6500 h). The voltage model with different patterns is developed based on the voltage data, which can be easily measured. The mechanism model is developed based on the evolution of degradation indices reflecting the degradation state. However, the former is sensitive to the local and periodic changes in the voltage curve, leading to a large prediction error, and the latter requires aging data from complex and high-cost characterization, limiting the practical applications. Therefore, a hybrid prediction model combining the voltage and mechanism model is proposed where the respective weight of each model is dynamically determined based on their local prediction errors. The results reveal that the maximum errors in RUL prediction are 9.72%, 3.90% and 2.01% for the voltage, mechanism and hybrid model, respectively, and the RUL prediction results of the hybrid model are close to actual RUL when those of the voltage model are far from the accuracy zone, indicating that the hybrid model provides credible RUL predictions with the highest accuracy.

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Liu, Xiyang, Guo Chen, Zhenjie Cheng, Xunkai Wei, and Hao Wang. "Convolution neural network based particle filtering for remaining useful life prediction of rolling bearing." Advances in Mechanical Engineering 14, no. 6 (2022): 168781322211006. http://dx.doi.org/10.1177/16878132221100631.

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Aiming at the problem of remaining useful life prediction of rolling bearing in aero engine, a data-driven prediction method based on deep learning and particle filter is proposed. Initially, only the vibration data of rolling bearing in normal stage are trained by the deep convolution neural network. According to the feature distance between normal and degraded samples, the evolution features during the whole lifetime are extracted adaptively, and the health index of rolling bearing is constructed. Then, the alarm and failure threshold are determined by unsupervised clustering algorithm. Combined with the extracted feature, remaining useful life of rolling bearing is tracked and predicted by particle filter algorithm based on four parameter exponential model. Finally, the effectiveness of the proposed method is verified by three groups of whole lifetime test data of rolling bearings. Results show that the degradation feature extracted by deep learning method has higher prediction accuracy of 2.19%, 0.93%, and 1.43% respectively than RMS values, and has more stable performance and less influenced by the number of particles or resampling methods, which can better reflect the evolution trend of rolling bearing than the traditional feature.

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Wu, Wei, Yongqian Gu, Mingkang Yu, Chongbing Gao, and Yong Chen. "Remaining Useful Lifetime Prediction Based on Extended Kalman Particle Filter for Power SiC MOSFETs." Micromachines 14, no. 4 (2023): 836. http://dx.doi.org/10.3390/mi14040836.

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Nowadays, the performance of silicon-based devices is almost approaching the physical limit of their materials, which have difficulty meeting the needs of modern high-power applications. The SiC MOSFET, as one of the important third-generation wide bandgap power semiconductor devices, has received extensive attention. However, numerous specific reliability issues exist for SiC MOSFETs, such as bias temperature instability, threshold voltage drift, and reduced short-circuit robustness. The remaining useful life (RUL) prediction of SiC MOSFETs has become the focus of device reliability research. In this paper, a RUL estimation method using the Extended Kalman Particle Filter (EPF) based on an on-state voltage degradation model for SiC MOSFETs is proposed. A new power cycling test platform is designed to monitor the on-state voltage of SiC MOSFETs used as the failure precursor. The experimental results show that the RUL prediction error decreases from 20.5% of the traditional Particle Filter algorithm (PF) algorithm to 11.5% of EPF with 40% data input. The life prediction accuracy is therefore improved by about 10%.

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Skriver, Mette Vinther, Michael Væth, and Henrik Støvring. "Loss of life expectancy derived from a standardized mortality ratio in Denmark, Finland, Norway and Sweden." Scandinavian Journal of Public Health 46, no. 7 (2018): 767–73. http://dx.doi.org/10.1177/1403494817749050.

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Aims: The standardized mortality ratio (SMR) is a widely used measure. A recent methodological study provided an accurate approximate relationship between an SMR and difference in lifetime expectancies. This study examines the usefulness of the theoretical relationship, when comparing historic mortality data in four Scandinavian populations. Methods: For Denmark, Finland, Norway and Sweden, data on mortality every fifth year in the period 1950 to 2010 were obtained. Using 1980 as the reference year, SMRs and difference in life expectancy were calculated. The assumptions behind the theoretical relationship were examined graphically. The theoretical relationship predicts a linear association with a slope, [Formula: see text], between log(SMR) and difference in life expectancies, and the theoretical prediction and calculated differences in lifetime expectancies were compared. We examined the linear association both for life expectancy at birth and at age 30. All analyses were done for females, males and the total population. Results: The approximate relationship provided accurate predictions of actual differences in lifetime expectancies. The accuracy of the predictions was better when age was restricted to above 30, and improved if the changes in mortality rate were close to a proportional change. Slopes of the linear relationship were generally around 9 for females and 10 for males. Conclusions: The theoretically derived relationship between SMR and difference in life expectancies provides an accurate prediction for comparing populations with approximately proportional differences in mortality, and was relatively robust. The relationship may provide a useful prediction of differences in lifetime expectancies, which can be more readily communicated and understood.

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Asmai, Siti Azirah, Burairah Hussin, Mokhtar Mohd Yusof, and Abdul Samad Shibghatullah. "Time Series Prediction Techniques for Estimating Remaining Useful Lifetime of Cutting Tool Failure." International Review on Computers and Software (IRECOS) 9, no. 10 (2014): 1783. http://dx.doi.org/10.15866/irecos.v9i10.3004.

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Xue, Dingyuan, Zezhou Wang, and Yunxiang Chen. "Joint Maintenance Decision Based on Remaining Useful Lifetime Prediction Using Accelerated Degradation Data." IEEE Access 10 (2022): 38650–63. http://dx.doi.org/10.1109/access.2022.3165050.

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Cheng, Danpeng, Wuxin Sha, Linna Wang, et al. "Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning." Applied Sciences 11, no. 10 (2021): 4671. http://dx.doi.org/10.3390/app11104671.

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Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

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Hell, Sebastian Matthias, and Chong Dae Kim. "Development of a Data-Driven Method for Online Battery Remaining-Useful-Life Prediction." Batteries 8, no. 10 (2022): 192. http://dx.doi.org/10.3390/batteries8100192.

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Remaining-useful-life (RUL) prediction of Li-ion batteries is used to provide an early indication of the expected lifetime of the battery, thereby reducing the risk of failure and increasing safety. In this paper, a detailed method is presented to make long-term predictions for the RUL based on a combination of gated recurrent unit neural network (GRU NN) and soft-sensing method. Firstly, an indirect health indicator (HI) was extracted from the charging processes using a soft-sensing method that can accurately describe power degradation instead of capacity. Then, a GRU NN with a sliding window was applied to learn the long-term performance development. The method also uses a dropout and early stopping method to prevent overfitting. To build the models and validate the effectiveness of the proposed method, a real-world NASA battery data set with various battery measurements was used. The results show that the method can produce a long-term and accurate RUL prediction at each position of the degradation progression based on several historical battery data sets.

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Dangar, Nikhil S., and Pravin H. Vataliya. "Prediction of Lifetime Milk Yield using Principal Component Analysis in Gir Cattle." Indian Journal of Veterinary Sciences & Biotechnology 18, no. 4 (2022): 92–96. http://dx.doi.org/10.48165/ijvsbt.18.4.19.

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The objective of the research was to investigate the relationship among production traits i.e., lactation milk yield, lactation length and lactation peak milk yield of the first three lactations using principal component analysis and formulation of prediction equation to predict lifetime milk production in Gir cattle. Data were from multiparous dairy cows of the University farm. Principal component analysis with correlation matrix was used to find the relationship among lactation milk yield, lactation length and lactation peak milk yield of first three lactation and other fixed effects, including the year of calving, season and parity with random effect of sire. The principal components were fitted to identify the best-fitted model for predicting lifetime milk yield using all principal components as a predictor in different combinations. The first six principal components (first lactation milk yield, lactation length and peak milk yield, second lactation milk yield, lactation length and peak milk yield), explained 98% variation in the estimated values with adjusted R2= 59.85% variation in the estimated values. The curve estimation analysis revealed that the first six principal components as the predictor was the most fitting model for predicting lifetime milk yield. The prediction equation found most fitted will be useful for the selection of Gir cattle at an early stage of lactation.

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Gao, Zhan, Qi-guo Hu, and Xiang-yang Xu. "Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment." Complexity 2020 (November 17, 2020): 1–10. http://dx.doi.org/10.1155/2020/8847703.

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Residual useful lifetime (RUL) prediction plays a key role of failure prediction and health management (PHM) in equipment. Aiming at the problems of residual life prediction without comprehensively considering multistage and individual differences in equipment performance degradation at present, we explore a prediction model that can fit the multistage random performance degradation. Degradation modeling is based on the random Wiener process. Moreover, according to the degradation monitoring data of the same batch of equipment, we apply the expectation maximization (EM) algorithm to estimate the prior distribution of the model. The real-time remaining life distribution of the equipment is acquired by merging prior information of real-time degradation data and historical degradation monitoring data. The accuracy of the proposed model is demonstrated by analyzing a practical case of metalized film capacitors, and the result shows that a better RUL estimation accuracy can be provided by our model compared with existing models.

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Kučera, Marián, Zdeněk Aleš, Jan Mareček, and Pavel Máchal. "Effect of Contamination on the Lifetime of Hydraulic Oils and Systems." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 65, no. 4 (2017): 1205–12. http://dx.doi.org/10.11118/actaun201765041205.

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The extensions of service‑lives regarding hydraulic fluids is gaining prominence due to several considerations including environmental pollution, conservation of natural resources and the economic benefits associated with extended service‑life. The presented methods for testing the durability and oxidation stabilities of hydraulic fluids can be simultaneously used in two ways. Firstly for comparing different hydraulic biooils and for selecting more adequate oils with higher oxidation stabilities and longer service lifetimes and secondly for the development of a prognostic model for an accurate prediction of an oil’s condition and its remaining useful lifetime, which could help to extend the service life of the oil without concerns about damaging the equipment.

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Weng, Hongkai. "Lifetime Prediction of Semiconductor Devices Based on Deep Learning." Applied and Computational Engineering 147, no. 1 (2025): 113–20. https://doi.org/10.54254/2755-2721/2025.22570.

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The reliability of power semiconductor devices, such as insulated gate bipolar transistors (IGBTs), is crucial for aerospace and industrial applications. Traditional prediction methods face challenges in multimodal data, integration physical constraints, and feature extraction accuracy. This study proposes a physics-informed deep learning framework for remaining useful life(RUL) prediction, by fusing high-frequency transient waveforms, steady-state thermal measurements, and electrical characterization data from source measurement units(SMUs). A hybrid architecture combines dilated convolutional neural networks (Dilated CNNs), to capture multi-scale transient features, long short-term memory (LSTM) networks with attention mechanisms for thermal sequence modeling and physics-guided loss functions incorporating the Coffin-Manson fatigue model. Experimental validation on NASAs accelerated aging dataset devices 2-5 demonstrated rapid convergence, with validation loss decreasing from 8362.9460 to 0.0224, and training loss from 0.4585 to 0.1121 over 100 epochs. The model achieved an RMSE of 0.0536 and an MAE of 0.0523, significantly outperforming non-dilated CNN baselines in convergence speed and stability.

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Saadat, Boulanouar, Ahmed Hafaifa, Ali Bennani, Nadji Hadroug, Abdellah Kouzou, and Mohamed Haddar. "Remaining useful lifetime prediction of gas turbine bearings based on experiment vibration signals data." Journal of Vibration Testing and System Dynamics 2, no. 2 (2018): 173–85. http://dx.doi.org/10.5890/jvtsd.2018.06.006.

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Park, Hye-Jin, and Sang-Shin Kwak. "Study of Remaining Useful Lifetime Prediction for Electrolytic Capacitors based on Accelerated Aging Tests." Transactions of The Korean Institute of Electrical Engineers 71, no. 11 (2022): 1614–23. http://dx.doi.org/10.5370/kiee.2022.71.11.1614.

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Wang, Lu, Li Zhang, and Xue-zhi Wang. "Reliability estimation and remaining useful lifetime prediction for bearing based on proportional hazard model." Journal of Central South University 22, no. 12 (2015): 4625–33. http://dx.doi.org/10.1007/s11771-015-3013-9.

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Du, Nguyen Huu, Nguyen Hoang Long, Kieu Ngan Ha, Nguyen Viet Hoang, Truong Thu Huong, and Kim Phuc Tran. "Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction." Computers in Industry 148 (June 2023): 103888. http://dx.doi.org/10.1016/j.compind.2023.103888.

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Amaitik, Nasser, Ming Zhang, Yuchun Xu, Tony Clark, and Lucy Bastin. "Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles." MATEC Web of Conferences 401 (2024): 10010. http://dx.doi.org/10.1051/matecconf/202440110010.

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This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.

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Dimitriadou, Krystallia, Charlotte Bay Hasager, Elena Cantero Nouqueret, and Ásta Hannesdóttir. "Quality assessment of the GPM IMERG product for lifetime prediction of turbine blades in complex terrain." Journal of Physics: Conference Series 2767, no. 4 (2024): 042010. http://dx.doi.org/10.1088/1742-6596/2767/4/042010.

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Abstract Wind turbine blades may suffer leading edge erosion when rain hits the blades extremely fast, resulting in blade damage that will negatively impact power production. Since wind turbines are growing in size, this translates into higher tip speeds when the blades rotate and, therefore, are more prone to erosion. Wind turbines in mountainous terrain may also suffer erosion due to the high winds and precipitation rates. Therefore, it becomes important to estimate blade lifetimes in wind farm sites with terrain complexity. Blade lifetime prediction models utilize a time series of rainfall intensity, wind speeds, and a turbine-specific tip speed curve. In our study, we assess the quality of the Integrated Multi-satellitE Retrievals for GPM (IMERG) final product in a blade lifetime prediction model for a mountainous area during the period 2015-2020. We first compare the IMERG rainfall intensities against in situ observations at 28 stations in Navarra in Northern Spain. We find that the two datasets are closer to agreement when the rainfall intensities are aggregated in monthly rather than 30-minute temporal scales with correlation coefficients between 0.74 - 0.93. We calculate the average annual rainfall in the period, and we find that IMERG over(under)estimates precipitation in 15 (8) stations, in line with previous studies that have pinpointed the limitations of IMERG in complex terrain. We then input the 30-minute IMERG, in situ rainfall intensities, and the 30-minute New European Wind Atlas (NEWA) wind speeds, extracted at each station location and interpolated at 119 m height, into a blade lifetime model. Our results indicate blade lifetimes of 6-17 years in 13 stations, with the in situ data to provide, on average, longer estimates than the IMERG product. Despite the limitations, we conclude that the satellite-based precipitation from IMERG may become a useful dataset for the lifetime estimation of wind turbine blades in complex terrain, with calibration and adjustments of the IMERG data.

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Lyu, Yi, Yijie Jiang, Qichen Zhang, and Ci Chen. "Remaining useful life prediction with insufficient degradation data based on deep learning approach." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 4 (2021): 745–56. http://dx.doi.org/10.17531/ein.2021.4.17.

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Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

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Ibrahim, Mesfin Seid, Waseem Abbas, Muhammad Waseem, et al. "Long-Term Lifetime Prediction of Power MOSFET Devices Based on LSTM and GRU Algorithms." Mathematics 11, no. 15 (2023): 3283. http://dx.doi.org/10.3390/math11153283.

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Predicting the long-term lifetime of power MOSFET devices plays a central role in the prevention of unprecedented failures for power MOSFETs used in safety-critical applications. The various traditional model-based approaches and statistical and filtering algorithms for prognostics have limitations in terms of handling the dynamic nature of failure precursor degradation data for these devices. In this paper, a prognostic model based on LSTM and GRU is developed that aims at estimating the long-term lifetime of discrete power MOSFETs using dominant failure precursor degradation data. An accelerated power cycling test has been designed and executed to collect failure precursor data. For this purpose, commercially available power MOSFETs passed through power cycling tests at different temperature swing conditions and potential failure precursor data were collected using an automated curve tracer after certain intervals. The on-state resistance degradation data identified as one of the dominant failure precursors and potential aging precursors has been analyzed using RNN, LSTM, and GRU-based algorithms. The LSTM and GRU models have been found to be superior compared to RNN, with MAPE of 0.9%, 0.78%, and 1.72% for MOSFET 1; 0.90%, 0.66%, and 0.6% for MOSFET 5; and 1.05%, 0.9%, and 0.78%, for MOSFET 9, respectively, predicted at 40,000 cycles. In addition, the robustness of these methods is examined using training data at 24,000 and 54,000 cycles of starting points and is able to predict the long-term lifetime accurately, as evaluated by MAPE, MSE, and RMSE metrics. In general, the prediction results showed that the prognostics algorithms developed were trained to provide effective, accurate, and useful lifetime predictions and were found to address the reliability concerns of power MOSFET devices for practical applications.

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Moghadam, Farid K., Geraldo F. de S. Rebouças, and Amir R. Nejad. "Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains." Forschung im Ingenieurwesen 85, no. 2 (2021): 273–86. http://dx.doi.org/10.1007/s10010-021-00468-9.

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AbstractThis paper presents a multi-degree of freedom torsional model of drivetrain system as the digital twin model for monitoring the remaining useful lifetime of the drivetrain components. An algorithm is proposed for the model identification, which receives the torsional response and estimated values of rotor and generator torques, and calculates the drivetrain dynamic properties, e.g. eigenvalues, and torsional model parameters. The applications of this model in prediction of gearbox remaining useful lifetime is discussed. The proposed method is computationally fast, and can be implemented by integrating with the current turbine control and monitoring system without a need for a new system and sensors installation. A test case, using 5 MW reference drivetrain, has been demonstrated.

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Dong, Qing, Hong Pei, Changhua Hu, Jianfei Zheng, and Dangbo Du. "Remaining Useful Life Prediction Method for Stochastic Degrading Devices Considering Predictive Maintenance." Sensors 25, no. 4 (2025): 1218. https://doi.org/10.3390/s25041218.

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Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data.

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Azevedo, Daniel, Alberto Cardoso, and Bernardete Ribeiro. "Estimation of Health Indicators using Advanced Analytics for Prediction of Aircraft Systems Remaining Useful Lifetime." PHM Society European Conference 5, no. 1 (2020): 10. http://dx.doi.org/10.36001/phme.2020.v5i1.1226.

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A valuable asset for the improvement of aviation maintenance is the correct assessment of the aircraft systems health condition, for a more accurate planning and execution of maintenance routines. As such, the creation of a Prognostic and Health Management (PHM) system, supported by Condition Based Maintenance (CBM) can bring important benefits to the aeronautical field. The ultimate goal of a PHM system is the correct prediction of the Remaining Useful Lifetime (RUL) of a certain aircraft system, using the sensors data extracted during flights. Nevertheless, a relevant stage in the PHM pipeline concerns the estimation of the system condition, expressed by the Health Indicator (HI). The HI value reflects the health condition of a specific aircraft system, which can possibly be affected by degradation, failures or external conditions occurred during flight time. Henceforth, due to the relevancy of the HI assessment for the accuracy of the PHM model, this paper aims to propose a new formulation for the HI computation, derived from raw anonymized data retrieved from different sensors within the aircraft system. The proposed formulation combines information from the different variables (like sensors) that have an impact on the overall system condition, by assigning a positive or negative weight to each variable depending on the influence on the system behaviour. The weights are determined based on the typical and standard data patterns. Thus, the estimated HI aims to reflect the number of hours of flight expected to be flown, based only on raw data extracted from the system. Furthermore, considering that the available sensors data is anonymized, a study of the relevancy of the different sensors features for the degradation assessment is performed, using specific metrics. Considering a scenario where the HI ground truth is unknown, the failure data of each aircraft system is used to evaluate and discuss the formulation suitability. The HI formulation is applied in real datasets, on the environmental systems of two wide body aircraft types.

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Duc, Van Nguyen, Limmer Steffen, Yang Kaifeng, Olhofer Markus, and Bäck Thomas. "Modeling and Prediction of Remaining Useful Lifetime for Maintenance Scheduling Optimization of a Car Fleet." International Journal of Performability Engineering 15, no. 9 (2019): 2318. http://dx.doi.org/10.23940/ijpe.19.09.p4.23182328.

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Woo, Chang Su, and Hyun Sung Park. "Development of Useful Lifetime Prediction Model of Rubber Spring for Primary Suspension of Railway Vehicles." Journal of The Korean Society For Urban Railway 7, no. 1 (2019): 55–61. http://dx.doi.org/10.24284/jkosur.2019.3.7.1.055.

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Zezhou, WANG, Hou Jian, Zhu Jiantai, Wang Liyuan, and Cai Zhongyi. "Stochastic degradation modeling and remaining useful lifetime prediction based on long short-term memory network." Measurement 234 (July 2024): 114803. http://dx.doi.org/10.1016/j.measurement.2024.114803.

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Guo, Jianchao, Yongbo Zhang, and Junling Wang. "Real-Time Prediction of Remaining Useful Life for Composite Laminates with Unknown Inputs and Varying Threshold." Machines 10, no. 12 (2022): 1185. http://dx.doi.org/10.3390/machines10121185.

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Prognostics and health management (PHM) has emerged as an essential approach for improving the safety, reliability, and maintainability of composite structures. However, an obstacle remains in its damage state estimation and lifetime prediction due to unknown inputs. Thus, a self-calibration Kalman-filter-based framework for residual life prediction is proposed, which involves unknown input items in the fatigue damage evolution model and employs health-monitoring data to estimate and compensate for them. Combined with the time-varying structural failure threshold, the remaining useful life (RUL) of composite laminates subjected to fatigue loading is predicted, providing a novel solution to the problem of unknown inputs in PHM. The simulation results demonstrate that the developed method can estimate the performance degradation state well, and its RUL prediction accuracy is within 5% with existing unknown inputs such as foreign impact damage.

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Magadán, Luis, Francisco J. Suárez, Juan C. Granda, Francisco J. delaCalle, and Daniel F. García. "A Robust Health Prognostics Technique for Failure Diagnosis and the Remaining Useful Lifetime Predictions of Bearings in Electric Motors." Applied Sciences 13, no. 4 (2023): 2220. http://dx.doi.org/10.3390/app13042220.

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Remaining useful lifetime (RUL) predictions of electric motors are of vital importance in the maintenance and reduction of repair costs. Thanks to technological advances associated with Industry 4.0, physical models used for prediction and prognostics have been replaced by data-driven models that do not require specialized staff for feature selection, as the model itself learns what features are important. However, these models are usually trained and tested with the same datasets. That makes it difficult to reuse models with different datasets, so they should be retrained with data from the specific motor being analyzed. This paper presents a novel and robust health prognostics technique that predicts the remaining useful lifetime of the bearings of electric motors under different motor conditions (shaft frequency, load, type of bearing) without retraining or fine-tuning the model used. The model integrates the frequency-domain signal analysis and a stacked autoencoder (SAE) with a bidirectional long short-term memory (BiLSTM) neural network. The proposed model is trained with the IMS-bearing dataset and is then tested with IMS, FEMTO, and XJTU-SY datasets without retraining it, providing accurate results in all of them, and proving its robustness with different electric motors and work conditions.

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Journal articles on the topic 'Useful lifetime prediction'

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