Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo...Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.展开更多
Extensive research has been conducted on remaining oil in the Daqing Oilfield during high water cuts’late stage,but few studies have offered multi-level analyses from both macro and micro perspectives for remaining o...Extensive research has been conducted on remaining oil in the Daqing Oilfield during high water cuts’late stage,but few studies have offered multi-level analyses from both macro and micro perspectives for remaining oil under varying formation conditions and displacement methods.This article focuses on the remaining oil in the S,P,and G reservoirs of Daqing Oilfield by employing the frozen section analysis method on the cores from the S,P,and G oil layers.The research identifies patterns among them,revealing that the Micro Remaining Oil types in these cores primarily include pore surface thin film,corner,throat,cluster,intergranular adsorption,and particle adsorption.Among these,intergranular adsorption contains the highest amount of remaining oil(the highest proportion reaches 60%)and serves as the main target for development potential.The overall distribution pattern of the Micro Remaining Oil in the S,P,and G oil layers shows that as flooding intensity increases,the amount of free-state remaining oil gradually decreases,while bound-state remaining oil gradually increases.The study also examines eight typical coring wells for macroscopic remaining oil,finding four main types in the reservoir:interlayer difference,interlayer loss,interlayer interference,and injection-production imperfect types.Among these,the injection-production imperfect type has the highest remaining oil content and is the primary target for development potential.Analyzing the reservoir utilization status and oil flooding efficiency reveals that as water flooding intensifies,the oil displacement efficiency of the oil layer gradually decreases,while the efficiency of oil layer displacement improves.Strongly flooded cores exhibit less free-state remaining oil than weakly flooded cores,making displacement more challenging.This study aims to provide a foundation and support for the development of remaining oil in the S,P,and G oil layers.展开更多
1 Antoni Gaudíwas sickly as a boy in Reus,Spain,often riding a donkey due to his weak legs.He loved art and nature and was full of ideas.As he grew older and stronger,Gaudíexplored the remains of many old bu...1 Antoni Gaudíwas sickly as a boy in Reus,Spain,often riding a donkey due to his weak legs.He loved art and nature and was full of ideas.As he grew older and stronger,Gaudíexplored the remains of many old buildings near his city,which made him realize what he wanted to do for the rest of his life.展开更多
This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in ...This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.展开更多
Interlayer is an important factor affecting the distribution of remaining oil.Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development.However,the trad...Interlayer is an important factor affecting the distribution of remaining oil.Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development.However,the traditional method of identifying interlayers has some limitations:(1)Due to the existence of overlaps in the cross plot for different categories of interlayers,it is difficult to establish a determined model to classify the type of interlayer;(2)Traditional identification methods only use two or three logging curves to identify the types of interlayers,making it difficult to fully utilize the information of the logging curves,the recognition accuracy will be greatly reduced;(3)For a large number of complex logging data,interlayer identification is time-consuming and laborintensive.Based on the existing well area data such as logging data and core data,this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CIII sandstone group in the M oilfield.Through the comparison of various classifiers,it is found that the decision tree method has the best applicability and the highest accuracy in the study area.Based on single well identification of interlayers,the continuity of well interval interlayers in the study area is analyzed according to the horizontal well.Finally,the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield.展开更多
Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-spe...Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.展开更多
Hepatocellular carcinoma(HCC)remains one of the most challenging malignancies worldwide,with surgical resection being the cornerstone of curative treatment for early-stage disease[1,2].Despite significant advancements...Hepatocellular carcinoma(HCC)remains one of the most challenging malignancies worldwide,with surgical resection being the cornerstone of curative treatment for early-stage disease[1,2].Despite significant advancements in surgical techniques and perioperative management,the high incidence of postoperative recurrence following hepatectomy(exceeding 50%within 5 years)continues to be a major obstacle to long-term survival[3,4].The study by Yao et al.published in this issue of Hepatobiliary&Pancreatic Diseases International provides compelling evidence on a critical yet often overlooked aspect of HCC management—the impact of compliance to postoperative regular follow-up on long-term outcomes after curative resection[5].展开更多
Gastric cancer(GC)remains a major global health challenge,because of its poor prognosis and limited treatment options in advanced stages1,2.Recent advancements in immunotherapy,highlighted by the findings of the CHECK...Gastric cancer(GC)remains a major global health challenge,because of its poor prognosis and limited treatment options in advanced stages1,2.Recent advancements in immunotherapy,highlighted by the findings of the CHECKMATE-649,ORIENT-16,and KEYNOTE-859 trials,have markedly transformed the treatment paradigm for advanced gastric cancer(AGC)3-5.展开更多
1. Introduction Prognostics, known as ‘Remaining Useful Life(RUL) prediction', plays a crucial role in health management of critical systems, which is vital for maintaining the operating safety and reliability, a...1. Introduction Prognostics, known as ‘Remaining Useful Life(RUL) prediction', plays a crucial role in health management of critical systems, which is vital for maintaining the operating safety and reliability, and reducing the management costs.1Here, the RUL is usually defined as the length from the current time to the end of the useful life.展开更多
BACKGROUND Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease(CAD)remains incomplete.The aim of this study was to investigate the relationships bet...BACKGROUND Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease(CAD)remains incomplete.The aim of this study was to investigate the relationships between postdischarge cancers and all-cause mortality and cardiovascular mortality in CAD patients.METHODS In this retrospective cohort study,25%of CAD patients without prior cancer history who underwent coronary artery angiography between January 1,2011 and December 31,2015,were randomly enrolled using SPSS 26.0.Patients were monitored for the incidence of postdischarge cancer,which was defined as cancer diagnosed after the index hospitalization,survival status and cause of death.Cox regression analysis was used to explore the association between postdischarge cancer and all-cause mortality and cardiovascular mortality in CAD patients.RESULTS A total of 4085 patients were included in the final analysis.During a median follow-up period of 8 years,174 patients(4.3%)developed postdischarge cancer,and 343 patients(8.4%)died.A total of 173 patients died from cardiovascular diseases.Postdischarge cancer was associated with increased all-cause mortality risk(HR=2.653,95%CI:1.727–4.076,P<0.001)and cardiovascular mortality risk(HR=2.756,95%CI:1.470–5.167,P=0.002).Postdischarge lung cancer(HR=5.497,95%CI:2.922–10.343,P<0.001)and gastrointestinal cancer(HR=1.984,95%CI:1.049–3.750,P=0.035)were associated with all-cause mortality in CAD patients.Postdischarge lung cancer was significantly associated with cardiovascular death in CAD patients(HR=4.979,95%CI:2.114–11.728,P<0.001),and cardiovascular death was not significantly correlated with gastrointestinal cancer or other types of cancer.CONCLUSIONS Postdischarge cancer was associated with all-cause mortality and cardiovascular mortality in CAD patients.Compared with other cancers,postdischarge lung cancer had a more significant effect on all-cause mortality and cardiovascular mortality in CAD patients.展开更多
Gastric cancer(GC)remains one of the most common cancers and leading causes of cancer deaths globally1with 60.0%of cases and 56.6%of deaths occurring in East Asia.South Korea and Japan have conducted nationwide GC scr...Gastric cancer(GC)remains one of the most common cancers and leading causes of cancer deaths globally1with 60.0%of cases and 56.6%of deaths occurring in East Asia.South Korea and Japan have conducted nationwide GC screening programs for decades but with essential differences in strategies,organization,and coverage2.展开更多
Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering systems.However, research on PHM framework development for rad...Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering systems.However, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to implement.This paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also investigated.Several experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DLbased prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.展开更多
Colorectal cancer(CRC)is the third most common cancer worldwide and the second leading cause of cancer-related mortality.While early-stage CRC patients generally exhibit favorable overall survival(OS)rates,the prognos...Colorectal cancer(CRC)is the third most common cancer worldwide and the second leading cause of cancer-related mortality.While early-stage CRC patients generally exhibit favorable overall survival(OS)rates,the prognosis for metastatic CRC(mCRC)remains poor,with a survival rate<15%.Targeted combination therapy remains the main treatment strategy for mCRC,with a median OS(mOS)of only 25-30 months.展开更多
The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through acceler...The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.展开更多
Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degra...Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.展开更多
The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle co...The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.展开更多
NUMBERS OF THE WEEK"1】5"Total yields from insurance premium investment in 2007 reached 279.17 billion yuan($38.77 billion),exceeding the aggregate amount of the previous five years,according to Wu Dingfu, C...NUMBERS OF THE WEEK"1】5"Total yields from insurance premium investment in 2007 reached 279.17 billion yuan($38.77 billion),exceeding the aggregate amount of the previous five years,according to Wu Dingfu, Chairman of China Insurance Regulatory Commission.Wu said it was the best achievement ever for the insurance industry,and the 2007 premium totaled 703.58 billion yuan($97.72 billion), increasing 25 percent year on year. Beijing’s Vice Mayor Chen Gang said(?) total cost of Beijing Olympic venues would amount to 13 billion yuan($1.81(?)展开更多
We study age-structured branching models with reproduction law depending on the remaining lifetime of the parent. The lifespan of an individual is determined at its birth and its remaining lifetime decreases at the un...We study age-structured branching models with reproduction law depending on the remaining lifetime of the parent. The lifespan of an individual is determined at its birth and its remaining lifetime decreases at the unit speed. The models, without or with immigration, are constructed as measure-valued processes by pathwise unique solutions of stochastic equations driven by time-space Poisson random measures. In the subcritical branching case, we give a sufficient condition for the ergodicity of the process with immigration. Two large number laws and a central limit theorem of the occupation times are proved.展开更多
Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews ar...Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.展开更多
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by the National Natural Science Foundation of China(No.52207229)the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003)+1 种基金the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254)the financial support from the China Scholarship Council(No.202207550010).
文摘Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
文摘Extensive research has been conducted on remaining oil in the Daqing Oilfield during high water cuts’late stage,but few studies have offered multi-level analyses from both macro and micro perspectives for remaining oil under varying formation conditions and displacement methods.This article focuses on the remaining oil in the S,P,and G reservoirs of Daqing Oilfield by employing the frozen section analysis method on the cores from the S,P,and G oil layers.The research identifies patterns among them,revealing that the Micro Remaining Oil types in these cores primarily include pore surface thin film,corner,throat,cluster,intergranular adsorption,and particle adsorption.Among these,intergranular adsorption contains the highest amount of remaining oil(the highest proportion reaches 60%)and serves as the main target for development potential.The overall distribution pattern of the Micro Remaining Oil in the S,P,and G oil layers shows that as flooding intensity increases,the amount of free-state remaining oil gradually decreases,while bound-state remaining oil gradually increases.The study also examines eight typical coring wells for macroscopic remaining oil,finding four main types in the reservoir:interlayer difference,interlayer loss,interlayer interference,and injection-production imperfect types.Among these,the injection-production imperfect type has the highest remaining oil content and is the primary target for development potential.Analyzing the reservoir utilization status and oil flooding efficiency reveals that as water flooding intensifies,the oil displacement efficiency of the oil layer gradually decreases,while the efficiency of oil layer displacement improves.Strongly flooded cores exhibit less free-state remaining oil than weakly flooded cores,making displacement more challenging.This study aims to provide a foundation and support for the development of remaining oil in the S,P,and G oil layers.
文摘1 Antoni Gaudíwas sickly as a boy in Reus,Spain,often riding a donkey due to his weak legs.He loved art and nature and was full of ideas.As he grew older and stronger,Gaudíexplored the remains of many old buildings near his city,which made him realize what he wanted to do for the rest of his life.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.
基金supported by the Natural Science Basic Research Program of Shaanxi(2024JC-YBMS-202).
文摘Interlayer is an important factor affecting the distribution of remaining oil.Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development.However,the traditional method of identifying interlayers has some limitations:(1)Due to the existence of overlaps in the cross plot for different categories of interlayers,it is difficult to establish a determined model to classify the type of interlayer;(2)Traditional identification methods only use two or three logging curves to identify the types of interlayers,making it difficult to fully utilize the information of the logging curves,the recognition accuracy will be greatly reduced;(3)For a large number of complex logging data,interlayer identification is time-consuming and laborintensive.Based on the existing well area data such as logging data and core data,this paper uses machine learning method to quantitatively identify the interlayers in the single well layer of CIII sandstone group in the M oilfield.Through the comparison of various classifiers,it is found that the decision tree method has the best applicability and the highest accuracy in the study area.Based on single well identification of interlayers,the continuity of well interval interlayers in the study area is analyzed according to the horizontal well.Finally,the influence of the continuity of interlayers on the distribution of remaining oil is verified by the spatial distribution characteristics of interlayers combined with the production situation of the M oilfield.
基金Funded by the Spanish Government and FEDER funds(AEI/FEDER,UE)under grant PID2021-124502OB-C42(PRESECREL)the predoctoral program“Concepción Arenal del Programa de Personal Investigador en formación Predoctoral”funded by Universidad de Cantabria and Cantabria’s Government(BOC 18-10-2021).
文摘Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.
文摘Hepatocellular carcinoma(HCC)remains one of the most challenging malignancies worldwide,with surgical resection being the cornerstone of curative treatment for early-stage disease[1,2].Despite significant advancements in surgical techniques and perioperative management,the high incidence of postoperative recurrence following hepatectomy(exceeding 50%within 5 years)continues to be a major obstacle to long-term survival[3,4].The study by Yao et al.published in this issue of Hepatobiliary&Pancreatic Diseases International provides compelling evidence on a critical yet often overlooked aspect of HCC management—the impact of compliance to postoperative regular follow-up on long-term outcomes after curative resection[5].
基金supported by The National Key Research and Development Program of China(Grant no.2021YFA0910100)Healthy Zhejiang One Million People Cohort(Grant no.K-20230085)+5 种基金Post-doctoral Innovative Talent Support Program(Grant no.BX2023375)Lingyan Project of Zhejiang Provincial Department of Science and Technology(Grant no.2025C02059)the National Natural Science Foundation of China(Grant nos.82304946,82473489,and 82403546)Natural Science Foundation of Zhejiang Province(Grant nos.LR21H280001,LGF22H160056,ZCLQN25H1602,and LMS25H160006)Medicine and Health Science Fund of Zhejiang Province Health Commission(Grant nos.2025KY047 and 2022KY658)Traditional Chinese Medicine Science and Technology Project of Zhejiang Provincial Health Commission(Grant no.2022ZA023).
文摘Gastric cancer(GC)remains a major global health challenge,because of its poor prognosis and limited treatment options in advanced stages1,2.Recent advancements in immunotherapy,highlighted by the findings of the CHECKMATE-649,ORIENT-16,and KEYNOTE-859 trials,have markedly transformed the treatment paradigm for advanced gastric cancer(AGC)3-5.
基金supported by the National Natural Science Foundation of China (Nos. 62450056 and 62233017).
文摘1. Introduction Prognostics, known as ‘Remaining Useful Life(RUL) prediction', plays a crucial role in health management of critical systems, which is vital for maintaining the operating safety and reliability, and reducing the management costs.1Here, the RUL is usually defined as the length from the current time to the end of the useful life.
基金supported by the National Natural Science Foundation of China(No.82173450&No.81770237).
文摘BACKGROUND Our understanding of the correlation between postdischarge cancer and mortality in patients with coronary artery disease(CAD)remains incomplete.The aim of this study was to investigate the relationships between postdischarge cancers and all-cause mortality and cardiovascular mortality in CAD patients.METHODS In this retrospective cohort study,25%of CAD patients without prior cancer history who underwent coronary artery angiography between January 1,2011 and December 31,2015,were randomly enrolled using SPSS 26.0.Patients were monitored for the incidence of postdischarge cancer,which was defined as cancer diagnosed after the index hospitalization,survival status and cause of death.Cox regression analysis was used to explore the association between postdischarge cancer and all-cause mortality and cardiovascular mortality in CAD patients.RESULTS A total of 4085 patients were included in the final analysis.During a median follow-up period of 8 years,174 patients(4.3%)developed postdischarge cancer,and 343 patients(8.4%)died.A total of 173 patients died from cardiovascular diseases.Postdischarge cancer was associated with increased all-cause mortality risk(HR=2.653,95%CI:1.727–4.076,P<0.001)and cardiovascular mortality risk(HR=2.756,95%CI:1.470–5.167,P=0.002).Postdischarge lung cancer(HR=5.497,95%CI:2.922–10.343,P<0.001)and gastrointestinal cancer(HR=1.984,95%CI:1.049–3.750,P=0.035)were associated with all-cause mortality in CAD patients.Postdischarge lung cancer was significantly associated with cardiovascular death in CAD patients(HR=4.979,95%CI:2.114–11.728,P<0.001),and cardiovascular death was not significantly correlated with gastrointestinal cancer or other types of cancer.CONCLUSIONS Postdischarge cancer was associated with all-cause mortality and cardiovascular mortality in CAD patients.Compared with other cancers,postdischarge lung cancer had a more significant effect on all-cause mortality and cardiovascular mortality in CAD patients.
基金supported by grants from the Shanghai Key Disciplines of Public Health(2023-2025)for New Threeyear Action Plan(Grant Nos.GWVI-11.1-22 and GWVI-11.1-23)the Fudan School of Public Health-Jiading CDC key disciplines for the high-quality development of public health(Grant No.GWGZLXK-2023-02)the Fudan Undergraduate Research Opportunities Program(Grant No.FDUROP-24647)。
文摘Gastric cancer(GC)remains one of the most common cancers and leading causes of cancer deaths globally1with 60.0%of cases and 56.6%of deaths occurring in East Asia.South Korea and Japan have conducted nationwide GC screening programs for decades but with essential differences in strategies,organization,and coverage2.
基金National Natural Science Foundation of China (42027805)。
文摘Implementing an efficient real-time prognostics and health management (PHM) framework improves safety and reduces maintenance costs in complex engineering systems.However, research on PHM framework development for radar systems is limited. Furthermore, typical PHM approaches are centralized, do not scale well, and are challenging to implement.This paper proposes an integrated PHM framework for radar systems based on system structural decomposition to enhance reliability and support maintenance actions. The complexity challenge associated with implementing PHM at the system level is addressed by dividing the radar system into subsystems. Subsequently, optimal measurement point selection and sensor placement algorithms are formulated for effective data acquisition. Local modules are developed for each subsystem health assessment, fault diagnosis, and fault prediction without a centralized controller. Maintenance decisions are based on each local module’s fault diagnosis and prediction results. To further improve the effectiveness of the prognostics stage, the feasibility of integrating deep learning (DL) models is also investigated.Several experiments with different degradation patterns are performed to evaluate the effectiveness of the framework’s DLbased prognostics model. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar systems.
基金supported by grants from the National Natural Science Foundation of China(Nos.U22A20330 and 82373372)the Key Project of Research and Development Plan in Heilongjiang Province(Nos.2022ZX06C01 and JD2023SJ40)+1 种基金the Natural Science Funding of Heilongjiang(No.YQ2022H017)the Haiyan Foundation of Harbin Medical University Cancer Hospital(No.JJJQ 2024-02).
文摘Colorectal cancer(CRC)is the third most common cancer worldwide and the second leading cause of cancer-related mortality.While early-stage CRC patients generally exhibit favorable overall survival(OS)rates,the prognosis for metastatic CRC(mCRC)remains poor,with a survival rate<15%.Targeted combination therapy remains the main treatment strategy for mCRC,with a median OS(mOS)of only 25-30 months.
基金supported by the National Key Research and Development Project(Grant Number 2023YFB3709601)the National Natural Science Foundation of China(Grant Numbers 62373215,62373219,62073193)+2 种基金the Key Research and Development Plan of Shandong Province(Grant Numbers 2021CXGC010204,2022CXGC020902)the Fundamental Research Funds of Shandong University(Grant Number 2021JCG008)the Natural Science Foundation of Shandong Province(Grant Number ZR2023MF100).
文摘The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.
基金supported in part by the National Natural Science Foundation of China(U2034209)the Postdoctoral Science Foundation of Chongqing(cstc2021jcyj-bsh X0047)+1 种基金the Fundamental Research Funds for the Central Universities(2022CDJJMRH-008)the National Natural Science Foundation of China(62203075)
文摘Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.
基金co-supported by the National Natural Science Foundation of China(Nos.52272403,52402506)Natural Science Basic Research Program of Shaanxi,China(Nos.2022JC-27,2023-JC-QN-0599)。
文摘The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.
文摘NUMBERS OF THE WEEK"1】5"Total yields from insurance premium investment in 2007 reached 279.17 billion yuan($38.77 billion),exceeding the aggregate amount of the previous five years,according to Wu Dingfu, Chairman of China Insurance Regulatory Commission.Wu said it was the best achievement ever for the insurance industry,and the 2007 premium totaled 703.58 billion yuan($97.72 billion), increasing 25 percent year on year. Beijing’s Vice Mayor Chen Gang said(?) total cost of Beijing Olympic venues would amount to 13 billion yuan($1.81(?)
基金supported by the National Key R&D Program of China(2020YFA0712901).
文摘We study age-structured branching models with reproduction law depending on the remaining lifetime of the parent. The lifespan of an individual is determined at its birth and its remaining lifetime decreases at the unit speed. The models, without or with immigration, are constructed as measure-valued processes by pathwise unique solutions of stochastic equations driven by time-space Poisson random measures. In the subcritical branching case, we give a sufficient condition for the ergodicity of the process with immigration. Two large number laws and a central limit theorem of the occupation times are proved.
基金Supported by Tianjin Municipal Education Commission of China (Grant No. 2023KJ303)National Natural Science Foundation of China (Grant Nos. 12121002, 51975355)
文摘Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.
