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Article
Engineering, Industrial
Zhenan Pang et al.
Summary: This paper proposes a condition-based prognostic approach for age-and state-dependent partially observable nonlinear degrading system. The proposed approach characterizes the dynamics and nonlinearity of the system degradation process using age-and state-dependent nonlinear diffusion process and state space model. The distribution of the remaining useful life is derived using extended Kalman filtering and expectation-maximization algorithm, and can be updated in real-time with new data.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Jiusi Zhang et al.
Summary: This article proposes a novel method for predicting the remaining useful life (RUL) of 18650 lithium-ion batteries in electric vehicles. The method involves adaptively estimating noise variables in the degradation model using the EM algorithm and accurately detecting capacity regeneration using the Wilcoxon rank sum test.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Industrial
Yu Wang et al.
Summary: The study of the remaining useful life (RUL) has gained momentum in recent years for ensuring system availability. The proposed general time-varying Wiener process (GTWP) considers the dynamic and multi-source variability of a degradation process jointly. A state-space model is constructed to depict the evolution of model parameters over time, and an approximate analytical form for the estimated RUL is derived under the concept of the first hitting time (FHT). The results from simulation cases and real-world data demonstrate the generalizability, accuracy, and faster convergence of the proposed model compared to existing homogeneous models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Jiusi Zhang et al.
Summary: Most supervised learning-based approaches assume that offline data and online data should have a similar distribution, which is difficult to satisfy in realistic remaining useful life prediction. To overcome this issue, a new transfer learning method called domain adaptation learning-oriented transfer learning (TL) is proposed. The method, called VLSTM-LWSAN, uses a local weighted deep sub-domain adaptation network to align fine-grained features between different degenerate stages, reducing the discrepancy between the target and source domains. Experimental results on an aircraft turbofan engine dataset demonstrate that VLSTM-LWSAN outperforms deep learning approaches without transfer learning and conventional transfer learning methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Sheng Xiang et al.
Summary: In the industrial field, accurately predicting the remaining useful life (RUL) of gearboxes and bearings is crucial for reliable machine operation. A novel multihierarchy network called cocktail long short-term memory (C-LSTM) is proposed to achieve this. It extracts time-frequency characteristics from vibration signals to construct a health indicator (HI) with distinct degradation trends, and uses C-LSTM to predict future HI points based on the known HI points. The proposed methodology shows higher predictive performance compared to traditional methods in gearbox and bearing datasets.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Electrical & Electronic
Huihui Hu et al.
Summary: In this article, a flux-controllable permanent magnet motor has been designed and optimized to achieve optimal torque component distribution under different flux leakage conditions. The motor allows for flexible adjustment and control of the air-gap magnetic field through a reasonable flux bridge design, effectively expanding the motor's speed range. The research focuses on analyzing and optimizing torque characteristics in combination with typical operation conditions.
IEEE TRANSACTIONS ON MAGNETICS
(2022)
Review
Engineering, Industrial
Yang Hu et al.
Summary: This paper reviews the state-of-the-art of PHM from the perspectives of Design, Development, and Decision (DE3), extracting research conclusions from 235 related publications and identifying gaps and challenges in existing PHM concerning DE3. It aims to provide clear directions for advancing PHM methodologies and maturing them into practical technologies.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Chemistry, Multidisciplinary
Hui Gao et al.
Summary: With the increase in electric private cars and charging facilities in residential areas, disorderly charging can affect the power supply efficiency and electricity quality in residential areas. This paper analyzes the factors influencing the unbalanced operation of distribution transformers and the electrical load characteristics in typical residential areas. A local orderly charging strategy for electric vehicles based on energy routers is proposed to achieve three-phase balance control of distribution transformers in residential areas.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
He Liu et al.
Summary: This article describes a RUL prediction method based on fractional order Levy stable motion (fLsm), which solves the issue of unclear LRD in integer-order models. By revealing the LRD characteristics of fLsm through stability index and integral kernel function, a degradation prediction model is established and verified through Monte Carlo simulation.
Article
Engineering, Marine
Yaser Dehghan et al.
Summary: This study aimed to determine the probability density function of wind speed and wave height in Nowshahr Port using different methods, revealing that the accuracy of the Weibull Pdf was generally higher than the Rayleigh Pdf, with the Lognormal Pdf being the most efficient in wind and wave data analysis.
Article
Engineering, Industrial
Jiusi Zhang et al.
Summary: This study proposes an adaptive approach based on Kalman filter and expectation maximum to accurately predict the remaining useful life (RUL) of a single lithium-ion battery without historical data and describe the uncertainty of parameter estimation. Experimental results demonstrate that this method outperforms existing conventional data-driven approaches.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Energy & Fuels
Meng Wei et al.
Summary: “The remaining useful life (RUL) prediction of lithium-ion batteries is a critical technology in energy storage systems and electric vehicles (EVs). In this paper, a RUL prediction framework based on stacked autoencoder and Gaussian mixture regression (SAE-GMR) is proposed to improve accuracy by extracting indirect health indicators (HIs). The proposed method is compared with existing methods and shows superiority in terms of RUL prediction.”
JOURNAL OF ENERGY STORAGE
(2022)
Article
Automation & Control Systems
Zhenan Pang et al.
Summary: Significant advances have been made in the estimation of remaining useful life (RUL) based on degradation data. The establishment of an applicable degradation model is crucial for accurate RUL estimation, but current research mainly focuses on age-dependent degradation models. It has been found that degradation processes in engineering can also be related to degradation states. Additionally, unit-to-unit variability and unobservable degradation states due to different working conditions and complex environments affect the accuracy of RUL estimation. To address these issues, an age-dependent and state-dependent nonlinear degradation model is developed taking into consideration unit-to-unit variability and hidden degradation states. The Kalman filter is used to update the hidden degradation states in real time, while the expectation-maximization algorithm is applied to adaptively estimate unknown model parameters. The proposed approach is validated through numerical simulations and case studies on Li-ion batteries and rolling element bearings.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Energy & Fuels
Adam Thelen et al.
Summary: Traditional model-based approaches for predicting the remaining useful life (RUL) of a rechargeable battery cell tend to fail when the capacity fade trend changes over time. To improve the accuracy of RUL prediction, we propose an approach that combines empirical model-based prediction with data-driven error correction. Experimental results show that the data-driven error correction effectively reduces prediction errors and provides more conservative uncertainty estimates.
Article
Automation & Control Systems
Jin-Zhen Kong et al.
Summary: In this article, an accelerated stress factors-based nonlinear Wiener process model is proposed to improve battery prognostics under different working conditions. The method enables online individual battery prognostics by updating model parameters and designing accelerated stress-relevant drift functions. RUL predictions are conducted at different discharge rates and temperatures to demonstrate the accuracy and robustness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Engineering, Electrical & Electronic
Xin Li et al.
Summary: This paper proposes an RUL prognostic approach for lithium-ion batteries (LIBs) based on a non-Markovian process using a fractional Brownian motion (FBM) model. The approach updates model parameters and estimates the uncertainty of RUL using an online Kalman filter and offline historical data.
JOURNAL OF POWER ELECTRONICS
(2022)
Article
Chemistry, Physical
Xiaoqiong Pang et al.
Summary: This paper proposes an interval prediction strategy for lithium-ion battery remaining useful life (RUL) based on fuzzy information granulation and linguistic description. By introducing fuzzy information granulation and linguistic description method, the proposed strategy overcomes the limitations of current numerical prediction strategies. Experimental results show that the model with linguistic description outperforms the model without linguistic description, achieving better prediction performance.
JOURNAL OF POWER SOURCES
(2022)
Article
Engineering, Industrial
Xiaowu Chen et al.
Summary: This research proposes a Wiener process-based degradation model that can adaptively learn degradation trends in different data sets. A long short-term memory (LSTM) neural network is used as the degradation trend function, and transfer learning is employed to update the parameters of the LSTM neural network. The model achieves accurate parameter estimation and outperforms previous Wiener process-based degradation models in predicting remaining useful life (RUL) based on numerical examples and real battery data.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Wanqing Song et al.
Summary: In this study, a degradation model based on a nonlinear drift function and Linear Multifractional Levy Stable Motion (LMSM) is developed for predicting the remaining useful life (RUL) of Cracking gas compressors (CGCs). The model captures the nonlinear, long-range dependence, multifractal, and non-Gaussian characteristics of the degradation process. A RUL prediction framework for CGCs is proposed and validated using real observation data.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Mathematics, Interdisciplinary Applications
Neha Gupta et al.
Summary: This article discusses the properties of multifractional Brownian motion (MFBM), including persistence of signs long range dependence (LRD) and persistence of magnitudes LRD properties. A generalization called nth order multifractional Brownian motion (n-MFBM) is introduced, which allows the functional parameter H(t) to take values in the range (n-1,n). Two representations of the n-MFBM are provided and their relationship is obtained.
FRACTAL AND FRACTIONAL
(2022)
Article
Engineering, Electrical & Electronic
Shiyu Zhang et al.
Summary: In this study, an improved machine vision algorithm was proposed to monitor the charging site of electric vehicles in real-time. The algorithm achieved fast and accurate detection of flames of different sizes, and effectively suppressed false alarms in various complex lighting environments.
WORLD ELECTRIC VEHICLE JOURNAL
(2022)
Review
Engineering, Multidisciplinary
Ming-Feng Ge et al.
Summary: This review discusses the importance of estimating the SOH and predicting the RUL of lithium-ion batteries, summarizes the current research status, methods classification, advantages and limitations, as well as future development trends and challenges.
Article
Automation & Control Systems
Xuewen Zhang et al.
Summary: Efforts have been made to develop an enhanced RUL framework with data self-generation for both noncyclic and cyclic degradation systems. The proposed method successfully improves RUL estimation accuracy through high-quality data generation and hierarchical data integration, achieving state-of-the-art results in prognostic tasks.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Environmental Sciences
Livia Salles Martins et al.
Summary: The widespread use of Li-ion batteries in electronic devices and vehicles highlights the importance of recycling for sustainable development. Understanding future trends in vehicle design, recycling methods, and policy making is crucial for sustainable growth.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Engineering, Industrial
Zhen Chen et al.
Summary: This paper proposes a two-phase Gaussian process degradation model with a change-point for products exhibiting two-phase patterns, allowing for parameter estimation and change-point detection, and deriving closed-form distributions for the first passage time and the remaining useful life, effectively capturing the characteristics and trends in degradation paths.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Multidisciplinary
Yuxiong Li et al.
Summary: This paper proposes an improved wiener-based RUL prediction method using improved Kalman filtering and an adaptive modification algorithm to reduce prediction errors. Through comparisons with experimental results, the superiority of this method in stability and accuracy in RUL prediction has been demonstrated.
Review
Computer Science, Information Systems
Hanwen Zhang et al.
Summary: Brownian motion is commonly used for degradation modeling and RUL prediction, but it is Markovian. In contrast, fractional Brownian motion is introduced to model practical degradations with long-range dependence, providing better accuracy in predicting RULs. The transition from BM to FBM for RUL prediction addresses the challenges posed by LRD in practical degradation processes.
SCIENCE CHINA-INFORMATION SCIENCES
(2021)
Article
Energy & Fuels
Bin Duan et al.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2020)
Article
Multidisciplinary Sciences
Peter M. Attia et al.
Article
Electrochemistry
Zhenyu Zhang et al.
INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE
(2020)
Article
Engineering, Mechanical
Han Wang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Article
Computer Science, Hardware & Architecture
Hanwen Zhang et al.
IEEE TRANSACTIONS ON RELIABILITY
(2019)
Review
Mechanics
Dandan Lyu et al.
Article
Engineering, Mechanical
Hanwen Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Review
Green & Sustainable Science & Technology
Huixing Meng et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2019)
Article
Energy & Fuels
Boyi Xiao et al.
Article
Automation & Control Systems
Qingqing Zhai et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2017)
Article
Engineering, Multidisciplinary
Hao-Wei Wang et al.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2016)
Article
Statistics & Probability
David M. Mason
STOCHASTIC PROCESSES AND THEIR APPLICATIONS
(2016)
Article
Energy & Fuels
Meru A. Patil et al.
Article
Engineering, Multidisciplinary
Datong Liu et al.
Article
Engineering, Industrial
Zhi-Sheng Ye et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2015)
Review
Engineering, Mechanical
Jay Lee et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2014)
Article
Chemistry, Physical
Yonghuang Ye et al.
JOURNAL OF POWER SOURCES
(2013)
Article
Computer Science, Interdisciplinary Applications
P. Mantalos et al.
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2012)
Review
Green & Sustainable Science & Technology
Ferit Kula et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2012)
Article
Engineering, Electrical & Electronic
Wenbin Wang et al.
MICROELECTRONICS RELIABILITY
(2011)
Article
Nanoscience & Nanotechnology
J. C. Arrebola et al.
JOURNAL OF NANOMATERIALS
(2008)
Article
Computer Science, Hardware & Architecture
ZH Chen et al.
IEEE TRANSACTIONS ON RELIABILITY
(2005)
