In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However...In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.展开更多
Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomne...Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.展开更多
Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understand...Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.展开更多
Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a n...Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.展开更多
Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productiv...Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productivity of the hot rolling process.In addition,the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance.Therefore,a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper.Firstly,a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator(HI)is developed,where the HI is able to indicate the health state of the roller.Following that,a state-space model is constructed to describe the HI,and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold.Finally,application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site,and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.展开更多
Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipmen...Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipment.The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function.This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model.Based on the historical measured data of similar equipment,the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient.Using the on-site measured data of the target equipment,the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm.The analytical form of the RUL distribution function is derived based on the first hitting time distribution.Combined with the two case studies,the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.展开更多
Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of over...Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.展开更多
In this paper,a novel remaining useful life prediction approach considering fault effects is proposed.The Wiener process is used to construct the degradation process of single performance characteristic with the fault...In this paper,a novel remaining useful life prediction approach considering fault effects is proposed.The Wiener process is used to construct the degradation process of single performance characteristic with the fault effects.The first passage time based remaining useful life distribution is calculated by assuming fault occurrence moment is a random variable and follows a certain distribution.Expectation maximization algorithm is employed to estimate model parameters,where the fault occurrence moment is considered as a missing data.Finally,a Copula function is used to describe the dependence between the multiple performance characteristics and derive joint remaining useful life(RUL)distribution of product with the fault effects.The effectiveness of the proposed approach is verified by the experiments of turbofan engines.展开更多
The remaining useful life(RUL)prediction is a crucial indicator for the lithium-ion battery health prognostic.The particle filter(PF),used together with an empirical model,has become one of the most well-accepted tech...The remaining useful life(RUL)prediction is a crucial indicator for the lithium-ion battery health prognostic.The particle filter(PF),used together with an empirical model,has become one of the most well-accepted techniques for RUL prediction.In this work,a novel filtering algorithm,named the Gaussian mixture model(GMM)-ensemble Kalman filter(EnKF)is proposed.It embeds the Gaussian mixture model in the EnKF framework to cope with the non-Gaussian feature of the system state space,and meanwhile address some of the major shortcomings of the PF.The GMM-EnKF and the PF are both applied on public data sets for RUL prediction and the simulation results show superiority of our proposed approach to the PF.展开更多
As the lithium-ion battery is widely applied,the reliability of the battery has become a high-profile content in recent years.Accurate estimation and prediction of state of health(SOH)and remaining useful life(RUL)pre...As the lithium-ion battery is widely applied,the reliability of the battery has become a high-profile content in recent years.Accurate estimation and prediction of state of health(SOH)and remaining useful life(RUL)prediction are crucial for battery management systems.In this paper,the core contribution is the construction of a datadriven model with the long short-term memory(LSTM)network applicable to the time-series regression prediction problem with the integration of two methods,data-driven methods and feature signal analysis.The input features of model are extracted from differential thermal voltammetry(DTV)curves,which could characterize the battery degradation characteristics,so that the accurate prediction of battery capacity fade could be accomplished.Firstly,the DTV curve is smoothed by the Savitzky-Golay filter,and six alternate features are selected based on the connection between DTV curves and battery degradation characteristics.Then,a correlation analysis method is used to further filter the input features and three features that are highly associated with capacity fade are selected as input into the data driven model.The LSTM neural network is trained by using the root mean square propagation(RMSprop)technique and the dropout technique.Finally,the data of four batteries with different health levels are deployed for model construction,verification and comparison.The results show that the proposed method has high accuracy in SOH and RUL prediction and the capacity rebound phenomenon can be accurately estimated.This method can greatly reduce the cost and complexity,and increase the practicability,which provides the basis and guidance for battery data collection and the application of cloud technology and digital twin.展开更多
Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accur...Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.展开更多
Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cel...Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cell stacks.A short lifetime is one of the main challenges for the fuel cell before largescale commercial applications,and prognostic is an important method to improve the reliability of fuel cells.Different from the traditional prognostic approaches applied to single-stack fuel cell systems,the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation,which can directly affect the accuracy of prediction.In response to this difficulty,a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack,and then the probability density function of the remaining useful life(RUL)of each stack is calculated.Furthermore,a Copula function is adopted to reflect the dependence between life distributions,so as to obtain the remaining useful life for the whole multi-stack system.1 The simulation results show that compared with the traditional prediction model,the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.展开更多
Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system ...Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.展开更多
In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when opera...In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.展开更多
Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines.Currently in the wind power industry,maintenance is mainly performed on regular schedules or when signifi...Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines.Currently in the wind power industry,maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable,instead of being determined by the actual condition of the wind turbine.Among the total maintenance costs,approximately 25%~35%is related to regularly scheduled preventive maintenance and 65%~75%to unscheduled corrective maintenance.To reduce the failure rate and level and maintenance costs and improve the availability,reliability,safety,and lifespans of wind turbines,it is desirable to perform condition-based predictive maintenance for wind turbines,which will require a high-fidelity online prognostic condition monitoring system(CMS)for fault diagnosis and prognosis and remaining useful life(RUL)prediction of wind turbines.Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction.Compared to vibration monitoring,the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost,hardware complexity,implementation,and reliability.This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.展开更多
基金supported by the National Science Fund for Distinguished Young Scholars of China(52025056)Fundamental Research Funds for the Central Universities(xzy012022062)。
文摘In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.
基金supported by the National Natural Science Foundation of China(61703410,61873175,62073336,61873273,61773386,61922-089)the Basic Research Plan of Shaanxi Natural Science Foundation of China(2022JM-376).
文摘Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.
基金supported by Agency for Science,Technology and Research(A*STAR)under the Career Development Fund(C210112037)。
文摘Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.
基金supported by National Natural Science Foundation of China (61703410,61873175,62073336,61873273,61773386,61922089)。
文摘Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.
基金the Natural Science Foundation of China(NSFC)(61873024,61773053)the China Central Universities of USTB(FRF-TP-19-049A1Z)the National Key RD Program of China(2017YFB0306403)。
文摘Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productivity of the hot rolling process.In addition,the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance.Therefore,a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper.Firstly,a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator(HI)is developed,where the HI is able to indicate the health state of the roller.Following that,a state-space model is constructed to describe the HI,and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold.Finally,application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site,and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.
基金supported by the National Defense Foundation of China(7160118371901216)the China Postdoctoral Science Foundation(2017M623415)
文摘Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipment.The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function.This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model.Based on the historical measured data of similar equipment,the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient.Using the on-site measured data of the target equipment,the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm.The analytical form of the RUL distribution function is derived based on the first hitting time distribution.Combined with the two case studies,the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.
基金support by Natural Science Foundation of China(61873122)。
文摘Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.
基金supported by General Program of National Natural Science Foundation of China(61773080)China Central Universities Foundation(2019CDYGZD001)+1 种基金Scientific Reserve Talent Programs of Chongqing University(cqu2018CDHB1B04)Graduate Research and Innovation Foundation of Chongqing(CYB20065)。
文摘In this paper,a novel remaining useful life prediction approach considering fault effects is proposed.The Wiener process is used to construct the degradation process of single performance characteristic with the fault effects.The first passage time based remaining useful life distribution is calculated by assuming fault occurrence moment is a random variable and follows a certain distribution.Expectation maximization algorithm is employed to estimate model parameters,where the fault occurrence moment is considered as a missing data.Finally,a Copula function is used to describe the dependence between the multiple performance characteristics and derive joint remaining useful life(RUL)distribution of product with the fault effects.The effectiveness of the proposed approach is verified by the experiments of turbofan engines.
基金supported by Natural Science Basic Research Program of Shaanxi Province of China(No.2020JQ-683)Key Research&Design Project of Shaanxi Province(No.2019TSLGY04-06)。
文摘The remaining useful life(RUL)prediction is a crucial indicator for the lithium-ion battery health prognostic.The particle filter(PF),used together with an empirical model,has become one of the most well-accepted techniques for RUL prediction.In this work,a novel filtering algorithm,named the Gaussian mixture model(GMM)-ensemble Kalman filter(EnKF)is proposed.It embeds the Gaussian mixture model in the EnKF framework to cope with the non-Gaussian feature of the system state space,and meanwhile address some of the major shortcomings of the PF.The GMM-EnKF and the PF are both applied on public data sets for RUL prediction and the simulation results show superiority of our proposed approach to the PF.
基金financially supported by the National Natural Science Foundation of China(No.52102470)the Science and Technology Development Project of Jilin province(No.20200501012GX)。
文摘As the lithium-ion battery is widely applied,the reliability of the battery has become a high-profile content in recent years.Accurate estimation and prediction of state of health(SOH)and remaining useful life(RUL)prediction are crucial for battery management systems.In this paper,the core contribution is the construction of a datadriven model with the long short-term memory(LSTM)network applicable to the time-series regression prediction problem with the integration of two methods,data-driven methods and feature signal analysis.The input features of model are extracted from differential thermal voltammetry(DTV)curves,which could characterize the battery degradation characteristics,so that the accurate prediction of battery capacity fade could be accomplished.Firstly,the DTV curve is smoothed by the Savitzky-Golay filter,and six alternate features are selected based on the connection between DTV curves and battery degradation characteristics.Then,a correlation analysis method is used to further filter the input features and three features that are highly associated with capacity fade are selected as input into the data driven model.The LSTM neural network is trained by using the root mean square propagation(RMSprop)technique and the dropout technique.Finally,the data of four batteries with different health levels are deployed for model construction,verification and comparison.The results show that the proposed method has high accuracy in SOH and RUL prediction and the capacity rebound phenomenon can be accurately estimated.This method can greatly reduce the cost and complexity,and increase the practicability,which provides the basis and guidance for battery data collection and the application of cloud technology and digital twin.
基金by National Natural Science Foundation of China(No.61972443)National Key Research and Development Plan Program of China(No.2019YFE0105300)+1 种基金Hunan Provincial Hu-Xiang Young Talents Project of China(No.2018RS3095)Hunan Provincial Natural Science Foundation of China(No.2020JJ5199).
文摘Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.
基金supported by the State key R&D sub project(Grant No.2020YFB 1506002-03)the Department of Science and Technology of Sichuan Province(Grant No.2020YJ0109)+2 种基金the Fundamental Research Funds for the Central Universities(Grant No.ZYGX2019J060)the State Key Laboratory of Automotive Safety and Energy(Grant No.KF2022)the National Natural Science Foundation of China(Grant No.U2066202).
文摘Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cell stacks.A short lifetime is one of the main challenges for the fuel cell before largescale commercial applications,and prognostic is an important method to improve the reliability of fuel cells.Different from the traditional prognostic approaches applied to single-stack fuel cell systems,the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation,which can directly affect the accuracy of prediction.In response to this difficulty,a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack,and then the probability density function of the remaining useful life(RUL)of each stack is calculated.Furthermore,a Copula function is adopted to reflect the dependence between life distributions,so as to obtain the remaining useful life for the whole multi-stack system.1 The simulation results show that compared with the traditional prediction model,the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.
基金supported by the National Key R&D Program of China(2018YFB1306100)the National Natural Science Foundation of China(61922089,61833016,62073336,61903376,61773386)the National Science Foundation of Shannxi Province(2020JQ-489,2020JM-360).
文摘Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.
文摘In the rolling stock sector, the ability to protect passengers, freight and services relies on heavy inborn maintenance. Initiating an accurate model suitable to foresee the change of attitude on components when operating rolling stock systems will assist in reducing lock down and favors heavy productivity. In that light, this paper showcases a suitable methodology to track degradation of components through the blinding of physic laws and artificial intelligent techniques. This model used to foresee failure deterioration rate and remaining useful life (RUL) speculation is case study to showcase its quality and perfection, within which behavioral data are obtained through simulated models initiated in Mathlab. For feature extraction and forecasting issues, different neuro-fuzzy inference systems are designed, learnt and authenticated with powerful outputs gained during this process.
基金This work was supported in part by the Office of Energy Efficiency and Renewable Energy(EERE),U.S.Department of Energy under Awards Number DE-EE0006802 and DE-EE0001366in part by the U.S.National Science Foundation under Grant ECCS-1308045.
文摘Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines.Currently in the wind power industry,maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable,instead of being determined by the actual condition of the wind turbine.Among the total maintenance costs,approximately 25%~35%is related to regularly scheduled preventive maintenance and 65%~75%to unscheduled corrective maintenance.To reduce the failure rate and level and maintenance costs and improve the availability,reliability,safety,and lifespans of wind turbines,it is desirable to perform condition-based predictive maintenance for wind turbines,which will require a high-fidelity online prognostic condition monitoring system(CMS)for fault diagnosis and prognosis and remaining useful life(RUL)prediction of wind turbines.Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction.Compared to vibration monitoring,the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost,hardware complexity,implementation,and reliability.This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.
