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China Ocean Engineering 2024年第2期38卷 198-209页

作者： WU Qi-bing CAI Bao-ping FAN Hong-yan WANG Guan-nan RAO Xi GE Weifeng SHAO Xiao-yan LIU Yong-hong CNOOC Safety Technology Services Co. Ltd.Tianjin 300456China College of Mechanical and Electronic Engineering China University of PetroleumQingdao 266580China CNOOC EnerTech Safety&Environmental Protection BranchTianjin 300456China

Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health ***,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating *** complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL *** RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is *** performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data *** on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance *** example of a subsea tree system is given to verify the effectiveness of the proposed method.

关键词： remaining useful life Wiener process dynamic Bayesian networks maintenance subsea Christmas tree system

Enhanced wear prediction of tunnel boring machine disc cutters for accurate remaining useful life estimation using a hybrid model

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Frontiers of Structural and Civil Engineering 2024年第4期18卷 642-662页

作者： Xinghai ZHOU Yakun ZHANG Guofang GONG Huayong YANG Qiaosong CHEN Yuxi CHEN Zhixue SU The State Key Laboratory of Fluid Power&Mechatronic Systems Zhejiang UniversityHangzhou 310027China Guangzhou Rail Transit Construction Engineering Consulting Co. Ltd.Guangzhou 510000China China Railway Engineering Equipment Group Co. Ltd.Zhengzhou 450016China

In tunnel construction with tunnel boring machines(TBMs),accurate prediction of the remaining useful life(RUL)of disc cutters is critical for timely maintenance and replacement to avoid delays and cost *** paper introduces a novel hybrid model,integrating fundamental and data-driven approaches,to enhance wear prediction of TBM disc cutters and enable accurate RUL *** fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques,showcasing superior prediction accuracy compared to single-mechanism ***,the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model,leading to a high-performance wear prediction *** actual field data from a highway tunnel project in Shenzhen,the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven *** hybrid model outperforms the other models,achieving the highest accuracy in predicting TBM disc cutter wear(mean absolute error(MAE)=0.53,root mean square error(RMSE)=0.64).Furthermore,this study thoroughly analyzes the hybrid model’s generalization capability,revealing significant impacts of geological conditions on prediction *** model’s generalization capability is also improved by expanding and updating the data *** RUL estimation results provided by the hybrid model are straightforward and effective,making it a valuable tool by which construction staff can monitor TBM disc cutters.

关键词： tunnel boring machine disc cutter wear prediction remaining useful life field data hybrid model

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

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Journal of Energy Chemistry 2024年第4期91卷 512-521页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chunsheng Hu School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernization The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Advanced Interdisciplinary Studies Ningxia UniversityYinchuan 750000NingxiaChina

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured *** data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low *** response,this study integrates domain knowledge into deep learning to enhance the RUL prediction *** demonstrate accurate RUL prediction using only a single charging ***,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging *** parameters inform a deep neural network(DNN)to predict RUL with high accuracy and *** trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged *** data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,*** its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and *** inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

关键词： Lithium-ion batteries remaining useful life Physics-informed machine learning

remaining useful life estimation based on Wiener degradation processes with random failure threshold

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Journal of Central South University 2016年第9期23卷 2230-2241页

作者： TANG Sheng-jin YU Chuan-qiang FENG Yong-bao XIE Jian GAO Qin-he SI Xiao-sheng High-Tech Institute of Xi＇an Xi＇an 710025 China

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.

关键词： condition based maintenance remaining useful life wiener process random failure threshold bayesian EM algorithm

remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

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Chinese Journal of Aeronautics 2020年第5期33卷 1517-1531页

作者： Kai ZHANG Peng ZHAO Canfei SUN Youren WANG Zewang CHEN College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Aviation Industry Corporation Leihua Electronic Technology Research Institute Wuxi 214000China Testing and Verification Center Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementShanghai 201601China

As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power *** remaining useful life(RUL)prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power *** Filter(PF)is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ***,there are problems that particle weights cannot be updated in the prediction stage and particles *** settle these issues,an innovative technique of F-distribution PF and Kernel Smoothing(FPFKS)algorithm is *** the prediction stage,the weights of the particles are dynamically updated by the F kernel instead of being fixed all the ***,a first-order independent Markov capacity degradation model is ***,the kernel smoothing algorithm is integrated into PF,so that the variance of the parameters of capacity degradation model keeps *** based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Liion batteries.

关键词： F-distribution Kernel smoothing Lithium-ion batteries Markov model Particle filter Prediction remaining useful life

remaining useful life prediction for multi-component systems with hidden dependencies

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Science China(Information Sciences) 2019年第2期62卷 57-72页

作者： Xiaopeng XI Maoyin CHEN Donghua ZHOU Department of Automation Tsinghua University College of Electrical Engineering and Automation Shandong University of Science and Technology

How can we predict the remaining useful life(RUL) of a dynamic system subject to multiple dependent degradations? Is it possible to address the above problem when the degradation information is obtained indirectly? According to a new type of state space-based model, we mainly develop an online RUL prediction method for the above system. In this model, the dependencies among different degradations can be reflected in a diffusion coefficient matrix. Considering that some industrial systems like blast furnaces are usually equipped with multi-sensors, an efficient information fusion strategy also plays an important role in predicting the RUL. Based on multi-dimensional observations, the hidden degradation states are identified through the sequential Kalman filtering. Meanwhile, the unknown parameters in the model are updated iteratively by the expectation maximization(EM) algorithm. At last, the RUL distributions are simulated through the Monte Carlo method, in which three types of failure structures with regard to the degradations are considered. The effectiveness of the proposed method is fully verified by a numerical example as well as a case study about the blast furnace.

关键词： remaining useful life hidden dependencies state space model multi-source information fusion failure structure

Deep Spatiotemporal Convolutional-Neural-Network-Based remaining useful life Estimation of Bearings

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 115-129页

作者： Xu Wang Tianyang Wang Anbo Ming Qinkai Han Fulei Chu Wei Zhang Aihua Li State Key Laboratory of Tribology Tsinghua UniversityBeijing 100084China High-Tech Research Institute of Xi’an Xi’an 710025China

The remaining useful life(RUL)estimation of bearings is critical for ensuring the reliability of mechanical *** to the rapid development of deep learning methods,a multitude of data-driven RUL estimation approaches have been proposed ***,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 ***,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 *** raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping ***,the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct ***,the RUL of the bearings is estimated using the GPR model,which can also define the probability distribution of the potential function and prediction *** on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR *** use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.

关键词： Bearing remaining useful life Continuous wavelet transform Convolution neural network Gaussian process regression

Robust remaining useful life Estimation Based on an Improved Unscented Kalman Filtering Method

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Computer Modeling in Engineering & Sciences 2020年第6期123卷 1151-1173页

作者： Shenkun Zhao Chao Jiang Zhe Zhang Xiangyun Long State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha410082China

In the Prognostics and Health Management(PHM),remaining useful life(RUL)is very important and utilized to ensure the reliability and safety of the operation of complex mechanical ***,unscented Kalman filtering(UKF)has been applied widely in the RUL *** a degradation system,the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional ***,in some special degradation systems,their monitored measurements have a linear relation with their degradation *** these special problems,it may bring estimation errors to use the UKF method ***,many uncertain factors can result in the fluctuations of the estimated results,which may have a bad influence on the RUL estimation *** a result,a robust RUL estimation approach is proposed in this paper to reduce the errors and randomness of estimation results for this kind of degradation ***,an improved unscented Kalman filtering is established utilizing the Kalman filtering(KF)method and a linear adaptive *** linear adaptive strategy is used to adjust its noise term ***,the robust RUL estimation is realized by the improved *** last,three problems are investigated to demonstrate the effectiveness of the proposed method.

关键词： remaining useful life unscented Kalman filtering state space model

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

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Journal of Energy Chemistry 2023年第7期82卷 1-17,I0001页

作者： Bin Ma Lisheng Zhang Hanqing Yu Bosong Zou Wentao Wang Cheng Zhang Shichun Yang Xinhua Liu School of Transportation Science and Engineering Beihang UniversityBeijing 102206China China Software Testing Center Beijing 100038China Institute for Clean Growth and Future Mobility Coventry UniversityCV15FBUnited Kingdom Dyson School of Design Engineering Imperial College LondonLondon SW72AZUK

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal *** estimation and prediction of battery health conditions are crucial for battery safety *** this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven *** on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate *** double exponential decay model is utilized as an empirical model to output highly real-time prediction *** Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time ***,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μ*** proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

关键词： State of health remaining useful life End-cloud collaboration Ensemble learningDifferential thermal voltammetry

Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

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Journal of Energy Chemistry 2023年第2期77卷 438-451,I0011页

作者： Vaishali Sawant Rashmi Deshmukh Chetan Awati Department of Technology Shivaji UniversityKolhapurMaharashtraIndia

Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge *** exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation ***,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for *** addition,system failures can be better addressed by accurately forecasting the lifecycle of ***,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and *** article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of *** review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor ***,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is ***,the general perspective for future directions is also presented.

关键词： Supercapacitors Energy storage materials Artificial neural network Machine learning Capacitance prediction remaining useful life