The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology.The refrigeration system within the wind tunnel is...The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology.The refrigeration system within the wind tunnel is an important subsystem.However,the design of the wind tunnel refrigeration system management program presents significant scientific challenges and limitations.Traditional management approaches in wind tunnel refrigeration systems suffer from prolonged decision-making times and reliance on experiential knowledge,necessitating the need for intelligent transformation.This paper aims to address these issues by exploring existing intelligent management methodologies and defining the concept of a wind tunnel intelligent laboratory along with its primary modules.Furthermore,we propose a water cooler failure prediction model based on the existing equipment model of the wind tunnel's refrigeration system.This model effectively predicts the Remaining Useful Life(RUL) of the water cooler in the case of fouling failure,contributing to enhanced efficiency,cost reduction,and safety improvements in laboratories.展开更多
Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of elec...Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.展开更多
In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictiv...In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.展开更多
Background In patients with coronary artery disease,age is of known significance in predicting outcomes.Data on clinical outcomes in patients≥85 years undergoing percutaneous coronary intervention(PCI)remain scarce.T...Background In patients with coronary artery disease,age is of known significance in predicting outcomes.Data on clinical outcomes in patients≥85 years undergoing percutaneous coronary intervention(PCI)remain scarce.The study aim was to determine clinical characteristics,risk of adverse cardiovascular events,and mortality in patients aged≥85 years compared to those aged<85 undergoing PCI.Methods In this retrospective study,data were obtained from the nationwide Netherlands Heart Registration on patients undergoing PCI between January 1st,2017 and January 1st,2021.The primary endpoint was all-cause mortality at long-term followup.Results A total of 155,683 patients underwent PCI,of which 100,209(64.4%)acute coronary syndrome cases.Compared to patients aged<85 years,patients aged≥85 were more often female and showed a higher number of cardiovascular comorbidities,including impaired left ventricle ejection fraction and reduced kidney function.Mortality at short-term and long-term follow-up were significantly higher in those aged≥85(P<0.001).Patients aged≥85 were more likely to have a myocardial infarction within 30 days following the index intervention(0.9%vs.0.7%;P=0.024),though they less often underwent revascularization at longterm follow-up compared to patients aged<85(P<0.001).Conclusions The elderly(≥85 years)patient requiring PCI carries an extensive cardiovascular risk profile,translating in significant risk of recurrent cardiovascular events and increased mortality rate.Clinicians should carefully weigh perceived risks and potential benefits in the individual patient,considering the patients’age,cardiovascular risk profile,and associated risk of morbidity and mortality.展开更多
Alzheimer’s disease(AD)remains an incurable neurodegenerative disorder with devastating societal and personal impacts.Despite decades of intensive research,therapeutic efforts targeting the clinical stages of AD have...Alzheimer’s disease(AD)remains an incurable neurodegenerative disorder with devastating societal and personal impacts.Despite decades of intensive research,therapeutic efforts targeting the clinical stages of AD have largely failed to halt or reverse disease progression.This has prompted a critical shift in focus toward the earlier,preclinical stages of AD,where interventions may hold greater promise for altering the disease trajectory.展开更多
Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction pla...Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.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
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
Archaeologists(考古学家)have unearthed the remains of a Mayan city nearly 3,000 years old in northern Guatemala,with pyramids(金字塔)and monuments that showcase its significance as an important ceremonial site.The May...Archaeologists(考古学家)have unearthed the remains of a Mayan city nearly 3,000 years old in northern Guatemala,with pyramids(金字塔)and monuments that showcase its significance as an important ceremonial site.The Mayan civilization arose around 2000 BC,reaching its height between 400 AD and 900 AD in what is present-day southern Mexico and Guatemala,as well as parts of Belize,El Salvador and Honduras.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
Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or acc...Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.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
Transformers have achieved promising results on aeroengine remaining useful life(RUL)prediction,but they still have several limitations:1)Aeroengine domain knowledge,which contains rich information that can reflect th...Transformers have achieved promising results on aeroengine remaining useful life(RUL)prediction,but they still have several limitations:1)Aeroengine domain knowledge,which contains rich information that can reflect the aeroengine’s health statue,is largely ignored in modeling process;2)Traditional transformer ignores the valuable degradation information from other time scales.To address these issues,a novel domain knowledge-augmented multiscale transformer(DKAMFormer)is developed by integrating domain knowledge and multiscale learning to improve the prognostic performance and reliability.First,to obtain rich and professional aeroengine domain knowledge,multiple detail and complete knowledge graphs(KGs)are established based on the working principle of aeroengine,including aeroengine structure,components working characteristics and sensor parameters.Second,the domain knowledge contained in KGs is convert to embedded vector by KG representative learning,which are then utilized to strengthen and enrich the original multidimensional time-series(MTS)monitoring data,aiming to intergrade domain knowledge and monitoring data to train DKAMFormer.Third,to learn rich and complementary degradation features,a novel multiscale time scale-guided self-attention(MTSGSA)mechanism is designed,which maps original MTS into different time-scale feature spaces,and then employs multiple independent self-attention head to extract the degradation features from different time-scale spaces.Finally,through a series of comparative experiments on the public CMAPSS and NCMAPSS datasets and compared with 17 SOTA methods,the developed DKAMFormer significantly improves the RUL prediction performance under multiple operation conditions and degradation modes.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
文摘The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology.The refrigeration system within the wind tunnel is an important subsystem.However,the design of the wind tunnel refrigeration system management program presents significant scientific challenges and limitations.Traditional management approaches in wind tunnel refrigeration systems suffer from prolonged decision-making times and reliance on experiential knowledge,necessitating the need for intelligent transformation.This paper aims to address these issues by exploring existing intelligent management methodologies and defining the concept of a wind tunnel intelligent laboratory along with its primary modules.Furthermore,we propose a water cooler failure prediction model based on the existing equipment model of the wind tunnel's refrigeration system.This model effectively predicts the Remaining Useful Life(RUL) of the water cooler in the case of fouling failure,contributing to enhanced efficiency,cost reduction,and safety improvements in laboratories.
基金supported by the National Natural Science Foundation of China(No.U23A20651)the Central Government Guides Local Science and Technology Development Foundation(No.2023ZYDF022)+1 种基金the Sichuan Science and Technology Program(2024ZDZX0031)the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines(No.SKLMRDPC23KF19).
文摘Under complex working conditions,accurate prediction of the remaining useful life(RUL)of lithium-ion batteries is of great significance to ensure the stable operation of energy storage systems,the safe driving of electric vehicles,and the continuous power supply of electronic devices.This paper systematically describes the RUL prediction methods of lithium-ion batteries and comprehensively summarizes the development status and future trends in this field.First,the battery degradation mechanisms and lightweight data acquisition are analyzed.Secondly,a systematic classification model is constructed for the more widely used lithium battery RUL prediction methods,and the application characteristics and implementation limitations of different methods are analyzed in detail.An innovative classification framework for hybrid methods is proposed based on the depth of physical-data interaction.Then,collaborative modelling of calendar ageing and cyclic ageing is discussed,revealing their coupled effects and corresponding RUL prediction methods.Finally,the technical bottlenecks faced by the current RUL prediction of lithium batteries are identified,potential solutions are proposed,and the future development trends are outlined.
基金supported by the National Natural Science Foundation of China(No.51767017)the Key Research and Development Program of Gansu Province(No.25YFGA032)the Industry Support and Guidance Project for Higher Education Institutions of Gansu Province(No.2022CYZC-22).
文摘In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.
文摘Background In patients with coronary artery disease,age is of known significance in predicting outcomes.Data on clinical outcomes in patients≥85 years undergoing percutaneous coronary intervention(PCI)remain scarce.The study aim was to determine clinical characteristics,risk of adverse cardiovascular events,and mortality in patients aged≥85 years compared to those aged<85 undergoing PCI.Methods In this retrospective study,data were obtained from the nationwide Netherlands Heart Registration on patients undergoing PCI between January 1st,2017 and January 1st,2021.The primary endpoint was all-cause mortality at long-term followup.Results A total of 155,683 patients underwent PCI,of which 100,209(64.4%)acute coronary syndrome cases.Compared to patients aged<85 years,patients aged≥85 were more often female and showed a higher number of cardiovascular comorbidities,including impaired left ventricle ejection fraction and reduced kidney function.Mortality at short-term and long-term follow-up were significantly higher in those aged≥85(P<0.001).Patients aged≥85 were more likely to have a myocardial infarction within 30 days following the index intervention(0.9%vs.0.7%;P=0.024),though they less often underwent revascularization at longterm follow-up compared to patients aged<85(P<0.001).Conclusions The elderly(≥85 years)patient requiring PCI carries an extensive cardiovascular risk profile,translating in significant risk of recurrent cardiovascular events and increased mortality rate.Clinicians should carefully weigh perceived risks and potential benefits in the individual patient,considering the patients’age,cardiovascular risk profile,and associated risk of morbidity and mortality.
基金supported by the Canadian Institutes of Health Research Project grant (PJT-169197) to QYsupported by a CGS-M fellowship from the Canadian Institutes of Health Research
文摘Alzheimer’s disease(AD)remains an incurable neurodegenerative disorder with devastating societal and personal impacts.Despite decades of intensive research,therapeutic efforts targeting the clinical stages of AD have largely failed to halt or reverse disease progression.This has prompted a critical shift in focus toward the earlier,preclinical stages of AD,where interventions may hold greater promise for altering the disease trajectory.
基金funded by scientific research projects under Grant JY2024B011.
文摘Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
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Archaeologists(考古学家)have unearthed the remains of a Mayan city nearly 3,000 years old in northern Guatemala,with pyramids(金字塔)and monuments that showcase its significance as an important ceremonial site.The Mayan civilization arose around 2000 BC,reaching its height between 400 AD and 900 AD in what is present-day southern Mexico and Guatemala,as well as parts of Belize,El Salvador and Honduras.
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 National Natural Science Foundation of China(Grant No.62227814,52205040,22279070,and U21A20170)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0140)+3 种基金the Young Talent Fund of Xi’an Association for Science and Technology(Grant No.959202313096)the Key Projects of the Shaanxi Province Natural Science Foundation(Grant No.2025JC-QYXQ-038)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(Grant No.GZKF-202430)the National Key Research and Development Program of China(Grant No.2024YFB3311204)。
文摘Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.
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 in part by the National Natural Science Foundation of China(52305570)the National Natural Science Foundation of China Key Support Project(U2133202)+2 种基金China Postdoctoral Science Foundation(2022M720955)Postdoctoral Science Foundation of Heilongjiang Province(LBH-Z22187)Outstanding Doctoral Dissertation Funding Project of Heilongjiang Province(LJYXL2022-011).
文摘Transformers have achieved promising results on aeroengine remaining useful life(RUL)prediction,but they still have several limitations:1)Aeroengine domain knowledge,which contains rich information that can reflect the aeroengine’s health statue,is largely ignored in modeling process;2)Traditional transformer ignores the valuable degradation information from other time scales.To address these issues,a novel domain knowledge-augmented multiscale transformer(DKAMFormer)is developed by integrating domain knowledge and multiscale learning to improve the prognostic performance and reliability.First,to obtain rich and professional aeroengine domain knowledge,multiple detail and complete knowledge graphs(KGs)are established based on the working principle of aeroengine,including aeroengine structure,components working characteristics and sensor parameters.Second,the domain knowledge contained in KGs is convert to embedded vector by KG representative learning,which are then utilized to strengthen and enrich the original multidimensional time-series(MTS)monitoring data,aiming to intergrade domain knowledge and monitoring data to train DKAMFormer.Third,to learn rich and complementary degradation features,a novel multiscale time scale-guided self-attention(MTSGSA)mechanism is designed,which maps original MTS into different time-scale feature spaces,and then employs multiple independent self-attention head to extract the degradation features from different time-scale spaces.Finally,through a series of comparative experiments on the public CMAPSS and NCMAPSS datasets and compared with 17 SOTA methods,the developed DKAMFormer significantly improves the RUL prediction performance under multiple operation conditions and degradation modes.
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.
