Degradation Model Research Articles

Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model

Aiming at the problem that the cycle life of the battery is hard to be accurately estimated, a segmented capacity degradation model is established based on the trend of capacity degradation rate. By applying a genetic algorithm (GA) to optimize the model parameters, GA-Weibull and GA-SVR (support vector regression) degradation models and their corresponding segmented models are established, and the battery life is calculated. Furthermore, a method taking the RMSE as the target is put forward to calculate the knee-point of capacity degradation. Finally, the life is predicted by segmented models. The results show that compared with unsegmented models, describing the process of capacity degradation by segmented models reduces the error notably, makes the determination coefficient closer to 1, and improves the model accuracy effectively. Later, the proposed method makes the fitting error of the segmented model smaller than that of Bacon-Watts method, thus obtaining a more accurate knee-point. In addition, it is found that there exists linear relationship between the estimated knee-point and the battery life. Finally, the prediction results based on segmented model show that the prediction accuracy of segmented GA-SVR model is higher, and the MAPE is only 4.32%. The relevant results can provide some guidance for the reliability evaluation and product quality management of lithium-ion batteries, which is conducive to establishing more accurate models for battery life prediction.

Kinetic modeling of the photocatalytic degradation of acetaminophen and its main transformation products

In this study, a kinetic model of the heterogeneous photocatalytic degradation of acetaminophen and its main transformation products is presented. Kinetic photocatalytic modeling and photon absorption rate modeling were included. Monte Carlo method was used to model the photon absorption process. Experiments were carried out in a reactor operated in batch mode and TiO2 nanotubes were used as photocatalyst irradiated with 254 nm UVC. Kinetic parameters were estimated from the experiments data by applying a non-linear regression procedure. Intrinsic expressions to the kinetics of acetaminophen degradation and its main transformation products were derived. Model, kinetics and photon absorption formulations and parameters proved to be affordable for describing the photocatalytic degradation of acetaminophen, but improvements should be done for better description of formation and oxidation kinetics of main transformation products. The model should be tested with other pharmaceuticals and emergent pollutants to calibrate it and evaluate its applicability in a wide range of compounds.

Bridge performance degradation model based on the multi-variate bayesian dynamic linear method

The degradation of bridge structural performance arises from the combined influence of various factors. Performance assessment and reliable prediction of bridge performance degradation through effective utilizing of detection information updates is a challenging problem. In this paper, the concept of performance indicators is redefined, employing to delineate bridge performance degradation. A bridge performance degradation model (the error ≤8%) is formulated, considering the multiple-variable Bayesian dynamic linear method (MBDLM) and revealing the coupling mechanisms among factors influencing bridge performance degradation. On this basis, the prediction performance of the model is quantitatively evaluated by three metrics: mean squared error, predictive mean squared error and mean absolute percentage error. A methodology is presented for the assessment, prediction, and maintenance reinforcement of in-service bridge structural performance degradation. This approach holds promise for future applications in safety assessments and the decision-making process for preventive maintenance of operational bridges.

Exponential Dispersion accelerated degradation modelling and reliability assessment considering initial value and processes heterogeneity

In the production and operation, inherent variability and uncertainty necessitate addressing unit-to-unit heterogeneity in initial performance values and degradation processes. This article presents a bi-stochastic exponential dispersion process (BS-ED) designed to account for heterogeneity in both initial performance values and degradation processes. First, based on the ED process, the time and acceleration covariates are introduced to form a nonlinear accelerated ED process, and a random effect coefficient associated with the accelerated stress is incorporated to consider the heterogeneity of the process. Meanwhile, through the modelling of degradation time-shift, a degradation model considering the stochastic initial value of the product performance is developed. To effectively conduct the statistic inference of the BS-ED process, an improved stochastic EM algorithm is proposed, and the information matrix and Ito calculus are combined to estimate the confidence intervals. Finally, the stability of the method is verified by simulation and analyzed by two real cases.

Hiddenly degrading performance prognosis based on recursive model incorporating double-layer rolling optimization strategy

An integrated and adaptive prognostic framework is proposed for hiddenly degrading actuator in the closed-loop system. Furthermore, the dual influence of degradation progress and operation time is considered in the established augmented model. It is important to obtain accurate and real-time distribution of degradation information under different degradation progress. Firstly, the expectation and variance of hidden index, unknown operation time parameter and degradation progress parameter are evaluated by designing a double-layer rolling optimization strategy. Then, the uncertainty distribution of degradation model is iteratively derived by employing Bayesian theorem. Finally, the distribution of remaining useful life (RUL) is calculated based on available distribution of degradation model and states. The effectiveness of proposed algorithm is demonstrated by a mold model of continuous casting driven by servo motor.

Degradation Kinetics of Antifouling Biocides in Sediment During the Spiking Equilibrium Phase

The new generation of antifouling biocides, as well as other emerging contaminants, has not yet been included in sediment quality guidelines or present standard protocols for sediment spiking. Most of these biocides have short half-lives in water, but little information is available regarding their degradation in sediments. Thus, there is a need to establish a reliable duration of the equilibrium phase for sediment spiking prior to sediment toxicity testing to determine the actual exposure concentrations during ecotoxicological tests. This study aimed to evaluate the degradation of DCOIT, Irgarol, Diuron, and Dichlofluanid during a spiking equilibrium phase of 24 h at three different time intervals (0, 6, and 24 h) and concentrations (10, 100, and 1000 ng g-1), by applying kinetic degradation models. The models presented a better fit for the 1000 ng g-1 treatments, in which the half-lives of DCOIT and Diuron were < 5 h, Dichlofluanid < 2 h, and Irgarol < 6h. Our results also indicate that, except for Dichlofluanid, the degradation rates of the other antifouling biocides were reduced dramatically after 6 h of equilibrium. Therefore, an equilibrium phase of 24 h (or greater than 6 h) was considered viable for sediment spiking. Our findings provide valuable information to guide future sediment toxicity tests using these compounds.

An integrated approach to analyzing matrix-crack-induced stiffness degradation and medium leakage in linerless composite vessels

This paper proposes an integrated analysis method for linerless composite vessels considering matrix-crack-induced stiffness degradation and permeation. Stiffness degradation models are obtained using machine learning regression, the data sets of which consist of finite element analysis (FEA) results and virtual samples. Then, the effective leakage conductance is obtained by using the linear elastic fracture mechanics and the micro-cracking leakage mechanism. The proposed integrated method is applied to analyze vessel structures designed by the grid theory, which has variable ply thicknesses and layup angles. Simulation results show that whether the structure at cryogenic temperature cracks and leaks more easily depends on its dimensions and stacking sequences.

Deep Koopman Operator-based degradation modelling

Developing reliable health indicators for industrial assets is essential for accurate condition monitoring, fault detection, and predicting the remaining useful lifetime. However, constructing such indicators is challenging, especially given the increasing complexity of industrial systems and the not well-understood degradation dynamics. Previously proposed autoencoder-based methods for unsupervised health indicator construction faced the difficulty of constraining the latent representation over the system’s lifetime to obtain trendable and prognosable health indicators. Koopman operator theory provides a natural solution to this challenge. In this work, we first demonstrate the successful application of the Deep Koopman Operator approach for learning the dynamics of industrial systems. This results in a latent representation that provides sufficient information for estimating the remaining useful life of the asset. Secondly, we propose a novel Koopman-Inspired Degradation Model for modelling the degradation of dynamical systems with control. The Koopman-based algorithms demonstrate superior or comparable performance with autoencoder-based approaches in predicting the remaining useful life of assets such as CNC milling machine cutters and Li-ion batteries.

Hygrothermal Degradation of Epoxy Electrical Insulating Material-Testing and Mathematical Modeling.

The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material's remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study.

Weibull distribution as a criterion of emergency levels

In the exploitation of machinery and equipment, one of the most important parameters is the dynamic state of the object. Observing residual processes is the most popular method of assessing the dynamic state. In particular, dynamic residual processes are a carrier of diagnostic information. Measuring the vibrations and noise of working mini-mining machines provides knowledge about the dynamics of the assessed machinery. Many years of knowledge and experience have allowed for the creation of diagnostic procedures. Within most machines used in the industry, the procedures considered standard allow for an effective diagnostic assessment and optimal management of mini-mining machine exploitation. The applicability of these standards is quite wide. However, there are machines with parameters that prevent the application of said standards. Mini-mining machines used in subterranean mining are one of these machines. In the case of diagnostic assessment of continuous miners, chain conveyors, as well as other machinery used in the technological chain, the usage of assessment brackets consistent with adopted standards is incorrect. In the diagnostic assessment of these types of mini-mining machines, an analysis of long-term trends is often used. The determination of acceptable vibration levels is obtained heuristically through observations of the damage up to the state of emergency. In the article, methods of mini mining machines state assessment using models of machine degradation, using Weibull distribution, are presented. The presented method has been used in real results of vibration measurement of underground machinery used in a coal mine. The analyses showed a large effectiveness of the presented method.

A Design Tool for Battery/Supercapacitor Hybrid Energy Storage Systems Based on the Physical–Electrochemical Degradation Battery Model BaSiS

A design toolbox has been developed for hybrid energy storage systems (HESSs) that employ both batteries and supercapacitors, primarily focusing on optimizing the system sizing/cost and mitigating battery aging. The toolbox incorporates the BaSiS model, a non-empirical physical–electrochemical degradation model for lithium-ion batteries that enables accurate simulations of battery performance and degradation under realistic operating conditions. The paper presents a detailed description of the parameterization, and validation process for the battery model, emphasizing the high accuracy and strong reliability of the battery aging prediction. The HESS design toolbox can be used to investigate the impact of various battery/supercapacitor configurations and energy management algorithms on the design, battery degradation, and system investment cost of the hybrid storage system. To illustrate the effectiveness of the design toolbox, a case study on Dynamic Moderation frequency support in the UK grid was conducted. For this use case, the application of hybrid storage energy systems is well suited due to the highly dynamic power regulation requirements in island grids with low inertia. By utilizing the fast response of supercapacitors, the stress on the battery caused by short-term high-power peaks can be significantly alleviated. In this way, the hybrid storage system effectively reduces either the battery size or the battery aging rate. In summary, this research highlights the crucial role of a comprehensive analysis in the design of hybrid energy storage systems, addressing both battery aging and overall system costs. The design toolbox can provide transparency regarding the design space and assist in determining the most suitable HESS configuration for a given application.

A functional data‐driven method for modeling degradation of waxy lubrication layer

AbstractWax is a prevalent lubrication material extensively employed in various engineering applications. Understanding the degradation characteristics of the waxy lubrication layer under diverse stress variables and levels is crucial for ensuring system security and reliability. Due to the unclear mechanism governing the degradation of the waxy lubrication layer under different stress variables, existing degradation models are unsuitable for modeling waxy lubrication layer degradation data. To address this challenge, we propose a functional data‐driven method leveraging dense observations of waxy degradation. Through extensive simulations and a case study, we demonstrate the superior performance and effectiveness of the proposed approach.

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

A model-data-fusion method for real-time continuous remaining useful life prediction of lithium batteries

Ensuring the reliable operation of electric vehicles relies on accurate predictions of the remaining useful life (RUL) for lithium batteries. Existing model-based prediction methods face limitations due to insufficient full-life experimental data, while online monitoring data-driven methods lack empirical guidance and interpretability of the training data. To address these challenges, a model-data-fusion prediction method is proposed. Firstly, the degradation process is characterized using a generalized Wiener process model. Secondly, A Whale Optimization Particle Search Filter (WOS-PF) is introduced for real-time parameter updating of the degradation model. This approach ensures that even with a small sample size, parameters can be reliably estimated. Finally, leveraging the continuously updated parameters, a probability density function is derived to predict the RUL of lithium batteries in real-time. Experimental results indicate that this method enhances prediction accuracy compared to other existing methods, leading to better real-time monitoring of lithium battery health.

Framework for lifecycle resilience assessment of earthquake-damaged coastal RC structures considering non-uniform corrosion

Reinforced concrete (RC) structures in seismic coastal areas often suffer from the coupled effects of corrosion and earthquakes due to prolonged exposure to harsh environmental conditions. Previous research on the seismic performance of buildings primarily focused on uniform corrosion(UC), disregarding the impact of varying concentrations of chloride-ions on corrosion rate. This discrepancy will overestimate the seismic performance of building structures. The framework proposed in this study aims to evaluate the resilience of RC structures to non-uniform story corrosion(NUSC). The framework encompasses an innovative functional recovery function and considers the influence of chloride-ions concentration on the corrosion rate, time-varying initial functional function, and degradation model for corroded reinforced concrete. NUSC model is established within the proposed framework via OpenSees software. The proposed framework integrates the seismic hazard, fragility, resilience, and recovery models. A nonlinear time history analysis method is employed to simulate the structural response to the seismic action. The findings demonstrate that NUSC leads to a reduction in seismic resilience by 7.3% and 10% compared to UC when considering service times of 35 and 55 years, respectively. This indicates an escalation in seismic risk as well as a decline in structural seismic capacity.

Two-stage Remaining Useful Life Prediction Based on the Wiener Process With Multi-feature Fusion and Stage Division

Remaining life prediction (RUL) is a critical link of maintenance decision-making, the accurate RUL prediction is an important means to monitor the operating status and achieve the safe operation of equipment. However, existing studies rarely considered the multi-stage characteristics of indicator fusion in the degradation process, and directly used the Wiener process to establish degradation model, which results in significant errors in RUL prediction results. Therefore, to solve above issues, a two-stage RUL prediction method of bearing based on the Wiener process model with data fusion and stage division is proposed in the paper. Firstly, the concept of multi-feature fusion is introduced to construct a comprehensive health indicator (CHI) that considers indicator performance. After that, a two-stage RUL prediction model based on the CHI is developed, and a method for detecting change points and dividing stages is proposed. Finally, the effectiveness and predictability of the proposed method and CHI are demonstrated based on the bearing test datasets.

Modelling and inference for a degradation process with partial maintenance effects

AbstractThis paper proposes a new way of modelling imperfect maintenance in degradation models, by assuming that maintenance affects only a part of the degradation process. More precisely, the global degradation process is the sum of two dependent Wiener processes with drift. Maintenance has an effect of the ‐type on only one of these processes: it reduces the degradation level of a quantity which is proportional to the amount of degradation of this process accumulated since previous maintenance. Two particular cases of the model are considered: perturbed and partial replacement models. The usual model is also a specific case of this new model. The system is regularly inspected in order to measure the global degradation level. Two observation schemes are considered. In the complete scheme, the degradation levels are measured both between maintenance actions and at maintenance times (just before and just after). In the general scheme, the degradation levels are measured only between maintenance actions. The maximum likelihood estimation of the model parameters is studied for both observation schemes in both particular models. The quality of the estimators is assessed through a simulation study.

Integrated Taylor–Couette Reactor Model for Heterogeneous Photocatalytic Degradation of AO7

Integrated Taylor–Couette Reactor Model for Heterogeneous Photocatalytic Degradation of AO7

Sensor degradation in nuclear reactor pressure vessels: the overlooked factor in remaining useful life prediction

Sensor degradation poses a critical yet ‘often overlooked’ challenge in accurately predicting the remaining useful life (RUL) of nuclear reactor pressure vessels (RPVs), hindering safe and efficient plant operation. This paper introduces an approach to RUL estimation that explicitly addresses sensor degradation, a significant departure from conventional methods. We model neutron embrittlement, a dominant degradation process in RPV steel, as a Wiener process and leverage real-world surveillance capsule data for insightful parameterization. Maximum likelihood estimation is utilized to characterize the degradation dynamics in the model. A Kalman filter then seamlessly integrates the degradation model with sensor measurements, effectively compensating for degradation-induced errors and providing refined state estimates. These estimates power a robust RUL prediction framework. Our results expose the profound impact of sensor degradation on conventional RUL predictions. By directly confronting sensor degradation, our method yields substantially more accurate and reliable RUL estimates. This work marks a significant advancement in the field of materials degradation, offering a powerful tool to optimize nuclear power plant safety and longevity.

Stress–strain characteristics of fire‐exposed recycled coarse aggregate concrete

AbstractConcrete sustainability and performance under extreme conditions are of growing interest in construction engineering. This study delves into the influence of recycled coarse aggregate (RCA) content and elevated temperatures on normal‐strength concrete containing RCA. Five different concrete compositions, featuring varying content of RCA (ranging from 0% to 100%), were examined. The heating and subsequent cooling followed the ISO‐834 temperature–time graph up to 800°C. The primary objective was to evaluate residual properties, including the stress–strain behavior, compressive and tensile strength, secant elastic modulus, peak strain, and bond strength of RCA concrete. The findings reveal a consistent decrease in both strength and stiffness parameters of RCA concrete with rising temperatures, while peak strain exhibits a rapid increase at elevated temperatures. Interestingly, RCA content had a negligible impact on the relative deterioration of high‐temperature exposed RCA concrete compared to that at ambient conditions. Moreover, the bond behavior closely resembled that of natural aggregate concrete when used in moderate proportions. Degradation models based on regression analysis of the data were used to quantify the bond strength reduction for RCA‐based concrete and the slip of rebar concerning various temperatures. Importantly, these models demonstrated consistency with those applicable to conventional concrete.