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
The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle co...The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.展开更多
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
Turbofan engine is a critical aircraft component with complex structure and high-reliability requirements. Effectively predicting the remaining useful life(RUL) of turbofan engines has essential significance for devel...Turbofan engine is a critical aircraft component with complex structure and high-reliability requirements. Effectively predicting the remaining useful life(RUL) of turbofan engines has essential significance for developing maintenance strategies and reducing maintenance costs. Considering the characteristics of large sample size and high dimension of monitoring data, a hybrid health condition prediction model integrating the advantages of autoencoder and bidirectional long short-term memory(BLSTM) is proposed to improve the prediction accuracy of RUL. Autoencoder is used as a feature extractor to compress condition monitoring data. BLSTM is designed to capture the bidirectional long-range dependencies of features. A hybrid deep learning prediction model of RUL is constructed. This model has been tested on a benchmark dataset. The results demonstrate that this autoencoder-BLSTM hybrid model has a better prediction accuracy than the existing methods, such as multi-layer perceptron(MLP), support vector regression(SVR), convolutional neural network(CNN) and long short-term memory(LSTM). The proposed model can provide strong support for the health management and maintenance strategy development of turbofan engines.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
Repair and maintenance costs are the most important factors affecting decision making about substituting agricultural machineries. This decision is made based on the economic life （time） of machineries. In this rese...Repair and maintenance costs are the most important factors affecting decision making about substituting agricultural machineries. This decision is made based on the economic life （time） of machineries. In this research, condition monitoring of MF285 and MF399 tractors was performed using engine oil analysis to find the optimum life time of tractor substitution in comparison with the breakdown maintenance method in Iran. All recorded information about fixed and variable costs were selected as data base and analyzed. Data were divided （classified） based on period of annual working time. Using power regression analysis led to find mathematical models for the optimum time life definition. Cumulative working time （X） was selected as independent and cumulative costs based on definite percent of initial price （Y） was considered as dependent variable and a power law equation was found to express the costs of both MF399 and MF285 tractors as a function of working time. Results showed that in CM method, average of economic life was 13 and 11 years for MF399 and MF285, respectively. It was also found that in BM method, economic life wasl0 and 8.5 years for MF399 and MF285, respectively.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
Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degra...Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.展开更多
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
The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through acceler...The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.展开更多
In view of the difficulty in determining remaining useful life of plant new variety right in economic analysis, Weibull Survival Analysis Method and Gaussian Model to were used to study how to accurately estimate the ...In view of the difficulty in determining remaining useful life of plant new variety right in economic analysis, Weibull Survival Analysis Method and Gaussian Model to were used to study how to accurately estimate the remaining useful life of plant new variety right. The results showed that the average life of the granted rice varieties was 10.013 years. With the increase of the age of plant variety rights, the probability of the same residual life Ttreaching x was smaller and smaller, the reliability lower and lower, while the probability of the variety rights becoming invalid became greater. The remaining useful life of a specific granted rice variety was closely related to the demonstration promotion age when the granted rice variety reached its maximum area and the appearance of alternative varieties, and when the demonstration promotion age of the granted rice variety reaching the one with the maximum area, the promotion area would be decreased once a new alternative variety appeared, correspondingly with the shortening of the remaining useful life of the variety. Therefore, Weibull Survival Analysis Method and Gaussian Model could describe the remaining useful life＇s time trend, as well as determine the remaining useful life of a concrete plant variety right, clarify the entire life time of varieties rights, and make the economic analysis of plant new varieties rights more accurate and reasonable.展开更多
Thermereconomic Optimum Effective Life Cycle(TOELC)is a new idea.It is used to study the problem of what time to update equipment to achiev good economics.A Mathematical model has been established to study this proble...Thermereconomic Optimum Effective Life Cycle(TOELC)is a new idea.It is used to study the problem of what time to update equipment to achiev good economics.A Mathematical model has been established to study this problem.The TOELC has been applied to calculate the updating of gas turbine blades.It is proved that the result is beneficial.A general idea and a new method developed in this paper will be having some great meanings in practical equipment operation.展开更多
Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a criti...Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine （RVM） is a data-driven algorithm used to estimate a battery＇s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery＇s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.展开更多
Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the fail...Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.展开更多
Predicting the life of Ni-Cd battery for electric multiple units(EMU)can not only improve the safety and reliability of battery,but also reduce the operating costs of EMU.For this reason,a life prediction method based...Predicting the life of Ni-Cd battery for electric multiple units(EMU)can not only improve the safety and reliability of battery,but also reduce the operating costs of EMU.For this reason,a life prediction method based on linear Wiener process is proposed,which is suitable for both monotonic and non-monotonic degraded systems with accurate results.Firstly,a unary linear Wiener degradation model is established,and the parameters of the model are estimated by using the expectation-maximization algorithm(EM).With the established model,the remaining useful life(RUL)of Ni Cd battery and its distribution are obtained.Then based on the unary Wiener process degradation model,the correlation between capacity and energy is analyzed through Copula function to build a binary linear Wiener degradation model,where its parameters are estimated using Markov Chain Monte Carlo(MCMC)method.Finally,according to the binary Wiener process model,the battery RUL and its distribution are acquired.The experimental results show that the binary linear Wiener degradation model based on capacity and energy possesses higher accuracy than the unary linear wiener process degradation model.展开更多
As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of im...As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of imperfect maintenance activities usually assumed that maintenance activities have a single influence on the degradation level or degradation rate, but not on both.Aimed at this problem, this paper proposes a new degradation modeling and RUL estimation method taking the influence of imperfect maintenance activities on both the degradation level and the degradation rate into account. Toward this end, a stochastic degradation model considering imperfect maintenance activities is firstly constructed based on the diffusion process. Then, the Probability Density Function（PDF） of the RUL is derived by the convolution operator under the concept of First Hitting Time（FHT）. To implement the proposed RUL estimation method,the Maximum Likelihood Estimation（MLE） is utilized to estimate the degradation related parameters based on the Condition Monitoring（CM） data, while the Bayesian method is utilized to estimate the maintenance related parameters based on the maintenance data. Finally, a numerical example and a practical case study are provided to demonstrate the superiority of the proposed method. The experimental results show that the proposed method could greatly improve the RUL estimation accuracy for the degrading equipment subjected to imperfect maintenance activities.展开更多
Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability ...Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.展开更多
The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation app...The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed recently.However,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack robustness.Hence,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR model.The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy.Subsequently,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs.Finally,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction confidence.Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach.The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.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
As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power systems.The Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to...As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power systems.The Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems.Particle Filter(PF)is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability.However,there are problems that particle weights cannot be updated in the prediction stage and particles degradation.To settle these issues,an innovative technique of F-distribution PF and Kernel Smoothing(FPFKS)algorithm is proposed.In the prediction stage,the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time.Meanwhile,a first-order independent Markov capacity degradation model is established.Moreover,the kernel smoothing algorithm is integrated into PF,so that the variance of the parameters of capacity degradation model keeps invariant.Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Liion batteries.展开更多
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.
基金co-supported by the National Natural Science Foundation of China(Nos.52272403,52402506)Natural Science Basic Research Program of Shaanxi,China(Nos.2022JC-27,2023-JC-QN-0599)。
文摘The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.
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the National Natural Science Foundation of China(Nos.51505288 and 51875359)the TBT Project of Shanghai(No.18TBT003)the Project of Shanghai Telecom(No.17C1ZA0069SH301)
文摘Turbofan engine is a critical aircraft component with complex structure and high-reliability requirements. Effectively predicting the remaining useful life(RUL) of turbofan engines has essential significance for developing maintenance strategies and reducing maintenance costs. Considering the characteristics of large sample size and high dimension of monitoring data, a hybrid health condition prediction model integrating the advantages of autoencoder and bidirectional long short-term memory(BLSTM) is proposed to improve the prediction accuracy of RUL. Autoencoder is used as a feature extractor to compress condition monitoring data. BLSTM is designed to capture the bidirectional long-range dependencies of features. A hybrid deep learning prediction model of RUL is constructed. This model has been tested on a benchmark dataset. The results demonstrate that this autoencoder-BLSTM hybrid model has a better prediction accuracy than the existing methods, such as multi-layer perceptron(MLP), support vector regression(SVR), convolutional neural network(CNN) and long short-term memory(LSTM). The proposed model can provide strong support for the health management and maintenance strategy development of turbofan engines.
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.
文摘Repair and maintenance costs are the most important factors affecting decision making about substituting agricultural machineries. This decision is made based on the economic life （time） of machineries. In this research, condition monitoring of MF285 and MF399 tractors was performed using engine oil analysis to find the optimum life time of tractor substitution in comparison with the breakdown maintenance method in Iran. All recorded information about fixed and variable costs were selected as data base and analyzed. Data were divided （classified） based on period of annual working time. Using power regression analysis led to find mathematical models for the optimum time life definition. Cumulative working time （X） was selected as independent and cumulative costs based on definite percent of initial price （Y） was considered as dependent variable and a power law equation was found to express the costs of both MF399 and MF285 tractors as a function of working time. Results showed that in CM method, average of economic life was 13 and 11 years for MF399 and MF285, respectively. It was also found that in BM method, economic life wasl0 and 8.5 years for MF399 and MF285, respectively.
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.
基金supported in part by the National Natural Science Foundation of China(U2034209)the Postdoctoral Science Foundation of Chongqing(cstc2021jcyj-bsh X0047)+1 种基金the Fundamental Research Funds for the Central Universities(2022CDJJMRH-008)the National Natural Science Foundation of China(62203075)
文摘Dear Editor,This letter focuses on the remaining useful life(RUL)prediction task under limited labeled samples.Existing machine-learning-based RUL prediction methods for this task usually pay attention to mining degradation information to improve the prediction accuracy of degradation value or health indicator for the next epoch.However,they ignore the cumulative prediction error caused by iterations before reaching the failure point.
基金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supported by the National Key Research and Development Project(Grant Number 2023YFB3709601)the National Natural Science Foundation of China(Grant Numbers 62373215,62373219,62073193)+2 种基金the Key Research and Development Plan of Shandong Province(Grant Numbers 2021CXGC010204,2022CXGC020902)the Fundamental Research Funds of Shandong University(Grant Number 2021JCG008)the Natural Science Foundation of Shandong Province(Grant Number ZR2023MF100).
文摘The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.
基金Supported by the National Natural Science Foundation of China(71273264)the Fundamental Research Funds for the Central Welfare Scientific Research Institutes of China(2015-14)~~
文摘In view of the difficulty in determining remaining useful life of plant new variety right in economic analysis, Weibull Survival Analysis Method and Gaussian Model to were used to study how to accurately estimate the remaining useful life of plant new variety right. The results showed that the average life of the granted rice varieties was 10.013 years. With the increase of the age of plant variety rights, the probability of the same residual life Ttreaching x was smaller and smaller, the reliability lower and lower, while the probability of the variety rights becoming invalid became greater. The remaining useful life of a specific granted rice variety was closely related to the demonstration promotion age when the granted rice variety reached its maximum area and the appearance of alternative varieties, and when the demonstration promotion age of the granted rice variety reaching the one with the maximum area, the promotion area would be decreased once a new alternative variety appeared, correspondingly with the shortening of the remaining useful life of the variety. Therefore, Weibull Survival Analysis Method and Gaussian Model could describe the remaining useful life＇s time trend, as well as determine the remaining useful life of a concrete plant variety right, clarify the entire life time of varieties rights, and make the economic analysis of plant new varieties rights more accurate and reasonable.
基金This paper is supported by The Science Foundation of China Aviation
文摘Thermereconomic Optimum Effective Life Cycle(TOELC)is a new idea.It is used to study the problem of what time to update equipment to achiev good economics.A Mathematical model has been established to study this problem.The TOELC has been applied to calculate the updating of gas turbine blades.It is proved that the result is beneficial.A general idea and a new method developed in this paper will be having some great meanings in practical equipment operation.
基金co-supported in part by the National Natural Science Foundation of China (Nos. 61301205 and 61571160)the Natural Scientific Research Innovation Foundation at Harbin Institute of Technology (No. HIT.NSRIF.2014017)
文摘Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine （RVM） is a data-driven algorithm used to estimate a battery＇s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery＇s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.
基金Projects(51475462,61174030,61473094,61374126)supported by the National Natural Science Foundation of China
文摘Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.
基金Project(2017 YFB 1200801-12)supported by the National Natural Science Foundation of China。
文摘Predicting the life of Ni-Cd battery for electric multiple units(EMU)can not only improve the safety and reliability of battery,but also reduce the operating costs of EMU.For this reason,a life prediction method based on linear Wiener process is proposed,which is suitable for both monotonic and non-monotonic degraded systems with accurate results.Firstly,a unary linear Wiener degradation model is established,and the parameters of the model are estimated by using the expectation-maximization algorithm(EM).With the established model,the remaining useful life(RUL)of Ni Cd battery and its distribution are obtained.Then based on the unary Wiener process degradation model,the correlation between capacity and energy is analyzed through Copula function to build a binary linear Wiener degradation model,where its parameters are estimated using Markov Chain Monte Carlo(MCMC)method.Finally,according to the binary Wiener process model,the battery RUL and its distribution are acquired.The experimental results show that the binary linear Wiener degradation model based on capacity and energy possesses higher accuracy than the unary linear wiener process degradation model.
基金co-supported by the National Science Foundation of China(NSFC)(Nos.61573365,61603398,61374126,61473094,and 61773386)the Young Talent Fund of University Association for Science and Technology in Shaanxi,Chinathe Young Elite Scientists Sponsorship Program(YESS)by China Association for Science and Technology(CAST)
文摘As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of imperfect maintenance activities usually assumed that maintenance activities have a single influence on the degradation level or degradation rate, but not on both.Aimed at this problem, this paper proposes a new degradation modeling and RUL estimation method taking the influence of imperfect maintenance activities on both the degradation level and the degradation rate into account. Toward this end, a stochastic degradation model considering imperfect maintenance activities is firstly constructed based on the diffusion process. Then, the Probability Density Function（PDF） of the RUL is derived by the convolution operator under the concept of First Hitting Time（FHT）. To implement the proposed RUL estimation method,the Maximum Likelihood Estimation（MLE） is utilized to estimate the degradation related parameters based on the Condition Monitoring（CM） data, while the Bayesian method is utilized to estimate the maintenance related parameters based on the maintenance data. Finally, a numerical example and a practical case study are provided to demonstrate the superiority of the proposed method. The experimental results show that the proposed method could greatly improve the RUL estimation accuracy for the degrading equipment subjected to imperfect maintenance activities.
基金Project(71371182) supported by the National Natural Science Foundation of China
文摘Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.
基金Supported by National Key Research and Development Project of China(Grant No.2020YFB2007700)State Key Laboratory of Tribology Initiative Research Program(Grant No.SKLT2020D21)+2 种基金National Natural Science Foundation of China(Grant No.51975309)Shaanxi Provincial Natural Science Foundation of China(Grant No.2019JQ-712)Young Talent Fund of University Association for Science and Technology in Shaanxi(Grant No.20170511).
文摘The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical systems.Owing to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed recently.However,the following problems remain in existing methods:1)Most network models use raw data or statistical features as input,which renders it difficult to extract complex fault-related information hidden in signals;2)for current observations,the dependence between current states is emphasized,but their complex dependence on previous states is often disregarded;3)the output of neural networks is directly used as the estimated RUL in most studies,resulting in extremely volatile prediction results that lack robustness.Hence,a novel prognostics approach is proposed based on a time-frequency representation(TFR)subsequence,three-dimensional convolutional neural network(3DCNN),and Gaussian process regression(GPR).The approach primarily comprises two aspects:construction of a health indicator(HI)using the TFR-subsequence-3DCNN model,and RUL estimation based on the GPR model.The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy.Subsequently,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs.Finally,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction confidence.Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach.The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.
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co-supported by Aeronautical Science Foundation of China (No. 20183352030)Fund Project of Equipment Pre-research Field of China (No. JZX7Y20190243016301)
文摘As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power systems.The Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems.Particle Filter(PF)is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability.However,there are problems that particle weights cannot be updated in the prediction stage and particles degradation.To settle these issues,an innovative technique of F-distribution PF and Kernel Smoothing(FPFKS)algorithm is proposed.In the prediction stage,the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time.Meanwhile,a first-order independent Markov capacity degradation model is established.Moreover,the kernel smoothing algorithm is integrated into PF,so that the variance of the parameters of capacity degradation model keeps invariant.Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Liion batteries.
