Degradation Model Research Articles

A cross-level vibration prediction of USM stator under electron radiation

Moving components exposed to electron radiation over longer durations are more prone to failure due to its complex changes from material properties to component characteristics. It involves multi-scale analysis, leading to current methods being deficient in accuracy and efficiency. In this paper, a cross-level vibration prediction method, which selects the ultrasonic motor (USM) stator as a typical component for Jupiter exploration, is proposed by incorporating the cross-scale changes of material properties based on the edge-based smoothed finite element method (ES-FEM). A cross-scale degradation model for exploring the material properties is constructed by establishing the correlation between the degradation of molecular chains and the mechanical properties of the epoxy resin. The ES-FEM is developed for investigating the vibration of the USM stator, by introducing the edge-based gradient smoothing technique (GST) to perform the strain smoothing operation in its stiffness matrix, offering superior accuracy and efficiency. The experiment of 1.2 MeV electron radiation under different electron fluences was carried out. It demonstrated that the present method can achieve higher accuracy and efficiency than the traditional one, while being closed to the experimental results with the frequency and amplitude errors of 0.03 % and 1.3 %, respectively.

Recoverability degradation of adhesion between soft matters under uniaxial cyclic bonding–debonding: Modified cohesive interface model and numerical implementation

The main objective of this work is to propose a partial recoverable cohesive interface model, coupled with a bi-potential contact algorithm, to simulate the phenomenon of adhesion recoverability degradation under cyclic bonding–debonding between hyperelastic bodies. For this end, the proposed adhesion recoverability degradation model is constructed by defining the recovery of interface damage during the rebonding when two bodies come into contact, and a degradation factor related to the number of bonding–debonding cycles is introduced into the fully recoverable adhesion model to reduce energy dissipation after multiple cycles. Recoverability degradation includes adhesive stiffness and strength degradation, which is physically described as parallel, series and mixed arrays of adhesive bonds. Then, a finite element framework coupling the adhesion recoverability degradation model and the bi-potential contact algorithm is proposed, with Mooney–Rivlin hyperelastic material used to describe the soft matters. This framework is implemented in an in-house finite element code, with numerical examples demonstrating the model’s reliability. The proposed approach could be applied to investigate interfacial adhesion effects in fields such as flexible electronics and intelligent robotics.

Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement

Most existing low-light image enhancement approaches predominantly focus on directly modeling the mapping between low-light images and normal-light images, but models trained on the single fixed data set often fail to achieve satisfactory results in complex real-world scenes. To address the limitations posed by limited data, this paper proposes a joint framework to learn spatially adaptive degradation and enhancement representations of bright-to-dark images and dark-to-bright images. Firstly, we design an illumination degradation model based on cycle consistent loss to learn how to degrade normal-light images into low-light reference images. The degradation model can synthesize a large number of normal/degraded-light pairs, which is beneficial for the training of the second-stage enhancement model. Subsequently, leveraging the self-luminance information of images, we construct a self-attention-based light enhancement model. The enhancement model is trained on mixed data, which combines both real normal/low-light image pairs and synthetic normal/degraded-light image pairs. In this way, the enhancement model can learn more features from a diverse set of images, enhancing its robustness. Extensive experiments on low-light image enhancement, low-light object detection, and nighttime image segmentation have demonstrated that our proposed method outperforms existing supervised and unsupervised methods across a variety of real-world scenes.

Resilience-oriented adaptive predictive maintenance optimization for continuous process manufacturing systems considering mission profile variation

Continuous process manufacturing systems (CPMSs) are typical phased mission systems that require high standards of operational stability, reliability, and safety. With the variation of production mission profiles, CPMSs are required to run in diverse modes or conditions in different phases; therefore, to meet these standards, maintenance decisions applied to CPMSs should be adapted to such variations. Considering that the concept of “resilience” provides a systematic solution to evaluate system adaptability via “disruption absorption” and “recoverability,” this paper proposes a CPMS resilience evaluation model and utilizes it as guidance for the optimization of CPMS predictive maintenance (PdM). The proposed method consists of the following steps: (1) applying a customized Seasonal Trend Decomposition model to predict the future trend of production mission profile variations, (2) assessing the production mission accomplishment capability of CPMS based on a Gamma process model of equipment performance degradation, (3) using disruption response ratio to evaluate CPMS resilience based on mission accomplishment capability, and (4) proposing a Simulated Annealing Q-Learning algorithm for adaptive PdM optimization, which keeps resilience above a threshold level while minimizing maintenance costs. The applicability and effectiveness of the proposed method are validated by an industrial case study of a nuclear fuel rod shielding component CPMS.

Assessing the Fatigue Stress Behavior of Starch Biodegradable Films with Nanoclay Using Accelerated Survival Test Methods

A destructive degradation model was applied on films made from different concentrations of starch, glycerol and nanoclay using various elongation levels as a stress variable at different stress times and stretch cycles. The log tensile quotient (logarithm of the tensile strength to the corresponding break cycle) was recorded as the response variable. The log tensile quotient increased, and the log exact break time decreased, as the elongation level increased. The treatment containing the highest starch and nanoclay and lowest glycerol content proved to be the most resistant to stress conditions and the most versatile in relation to the varying log tensile quotients, while the treatments containing the lowest nanoclay and highest glycerol contents, regardless of the starch concentration, manifested the lowest log tensile quotient at higher levels of log exact break time. According to multiple regression findings, the break cycle governed mostly the stress conditions in the degradation model, followed by the sample ID and the log exact break time. The term log tensile quotient, attempted for the first time on data concerning biodegradable films enhanced with nanoclay, seems very promising for deeper research due to its ability to retrieve predictive information from survival equations and to discriminate the difference between film structures.

Modeling Particle Versus SEI Cracking in Lithium-Ion Battery Degradation: Why Calendar and Cycle Aging Cannot Simply be Added

Degradation models are important tools for understanding and mitigating lithium-ion battery aging, yet a universal model that can predict degradation under all operating conditions remains elusive. One challenge is the coupled influence of calendar and cycle aging phases on degradation mechanisms, such as solid electrolyte interphase (SEI) formation. In this work, we identify and systematically compare three different SEI interaction theories found in the literature, and apply them to experimental degradation data from a commercial lithium-ion cell. In a step-by-step process, and after careful data selection, we show that SEI delamination without any cracking of the active particles, and SEI microcracking, where cycling only affects SEI growth during the cycle itself, are both unlikely candidates. Instead, the results indicate that upon cycling, both the SEI and the active particle crack, and we provide a simple, 4-parameter equation that can predict the particle crack rate. Contrary to the widely-accepted Paris’ law, the particle crack rate decreases with increasing cycles, potentially due to changing intercalation dynamics resulting from the increasing surface-to-volume ratio of the active particles. The proposed model predicts SEI formation accurately at different storage conditions, while simply adding the degradation from pure calendar and cycle aging underestimates the total degradation.

A framework for designing a degradation-aware controller based on empirical estimation of the state–action cost and model predictive control

Controlling the machine’s state of health (SoH) increases the accuracy of the remaining useful life estimation and enables the control of the failure time by keeping the system operational until the desired maintenance time is reached. To achieve system reliability through SoH control, the system controller must consider the impact of its actions on other parameters, such as degradation. This article proposes a structure for designing degradation-aware controllers for systems with available physical models. A system using this approach can learn autonomously, irrespective of the system’s physical structure and degradation model, and opt for control actions that enhance the system’s reliability and availability. To this end, first, a method is proposed to compute the cost associated with the actions taken by the controller. Second, a new cost function is introduced that incorporates the costs associated with degradation into the cost function utilized in model predictive control. In the third step, dynamic programming and deterministic scheduling are used to calculate the optimal action based on the defined cost function. Finally, the proposed control method is validated through simulation, demonstrating its ability to effectively manage machine degradation and achieve optimal performance according to production and maintenance plans.

Effect of drying on bioactive compounds in Eugenia uniflora fruit pulp

AbstractEugenia uniflora is a tropical species rich in bioactive compounds that are highly sensitive to processing conditions, particularly those involving heat. Drying is a widely used method for preserving fruit pulp which can affect the stability of these bioactive compounds. The aim of this study was to assess the impact of drying at different temperatures on the retention of phenolic compounds and carotenoids present in the pulp of E. uniflora, with the goal of optimizing the drying process to preserve the pulp's quality. The E. uniflora pulp was dried in an oven with air circulation at three temperatures (45, 65, and 85°C). During the drying process, the moisture content and concentration of phenolics, β‐carotene, and lycopene were measured over time. After 260 min of drying, phenolic compounds decreased by 63.97% (45 and 65°C) and by 59.62% (85°C). Carotenoids losses were even more pronounced exceeding 89%, for all temperatures, with β‐carotene reductions of 92.91%, 90.72%, and 91.11%, at 45, 65, and 85°C, respectively. Several well‐established drying models were tested to represent the moisture content over time. Two models exhibited a high adherence to the experimental data. Zero‐order, first‐order, and second‐order degradation models were used to describe the concentrations of phenolic compounds and carotenoids. For total carotenoids, the model that showed the best results was temperature‐dependent. The first‐order model provided the best fit for β‐carotene and lycopene, with high R2 values of 0.9852 (45°C), 0.9776 (65°C), and 0.9681(85°C) for β‐carotene, and 0.9776 (45°C), 0.9715 (65°C), and 0.9659 (85°C) for lycopene. These results indicate that higher temperatures accelerate the degradation of bioactive compounds, following a predictable dynamic that can be optimized through adjustments to the drying process.Practical applicationsThe fruit of Eugenia uniflora contains phenolic and carotenoid compounds with significant nutraceutical potential. However, the production of this fruit is seasonal, necessitating effective preservation methods. Drying, which involves the removal of water from the pulp, is a common procedure aimed at extending the fruit's conservation and shelf life. Despite its benefits, the drying process poses a challenge, as bioactive compounds like phenolic and carotenoids are sensitive to thermal processing. Their degradation during drying can lead to a reduction in the fruit's bioactive potential. Therefore, it is crucial to understand the kinetics of drying and the degradation of these bioactive compounds to optimize the drying process and maximize the fruit's nutraceutical value.

On the Understanding and Modeling of Non-Negligible Subthreshold-State Degradation (SSD) in Sub-20-nm DRAM Technology

On the Understanding and Modeling of Non-Negligible Subthreshold-State Degradation (SSD) in Sub-20-nm DRAM Technology

Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties

Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles. Electrochemical impedance spectroscopy (EIS) offers valuable insights into battery degradation analysis and modeling. However, previous studies have not adequately addressed the impedance uncertainties, particularly during battery operating conditions, which can substantially impact the robustness and accuracy of state of health (SOH) estimation. Motivated by this, this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis. By utilizing metrics such as impedance residuals and correlation coefficients, the proposed method effectively filters out invalid and insignificant impedance data, thereby enhancing the reliability of the input features. Subsequently, the extreme gradient boosting (XGBoost) modeling framework is constructed for estimating the battery degradation trajectories. The XGBoost model incorporates a diverse range of hyperparameters, optimized by a genetic algorithm to improve its adaptability and generalization performance. Experimental validation confirms the effectiveness of the proposed feature optimization scheme, demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.

Intelligent Radiometric Calibration System for Ocean Color Satellite Sensors

Abstract To achieve the integration of multiple ocean color (OC) sensors’ radiometric calibration tasks into a single system, we have developed an intelligent on-orbit radiometric calibration system called the Generalized Radiometric Calibration Entity for Ocean Color (Grace-OC). The system features real-time data downloading capabilities and integrates three calibration methods: onboard calibration, system vicarious calibration and cross calibration, enabling intelligent selection of on-orbit radiometric calibration methods tailored to the calibration sensors. Compared to other calibration systems, we have improved the universality and efficiency of the system by establishing high-spectral aerosols and Rayleigh lookup tables (LUTs) which are verified consistency through a comparative analysis with operational LUTs releasing by National Aeronautics and Space Administration (NASA) in this paper. Building upon this foundation, we have integrated a comprehensive analysis function for calibration coefficients to automatically construct degradation models of the radiometric measurement performance, and to achieve mutual verification between different calibration methods. We applied Grace-OC to HY1C/D and verified the feasibility of the intelligent selection calibration methods and the stability of the calibration system, achieving a calibration accuracy of up to 0.5%. Simultaneously, the precision of degradation models of the radiometric measurement performance is confirmed through Grace-OC, and the on-orbit radiometric calibration task was ultimately completed within 6 minutes for each per scene. Based on the above applications, Grace-OC has demonstrated its universality for various OC sensors, as well as the stability of on-orbit radiometric calibration tasks and the efficiency of operational speed.

An Uncertain Random Process-Based Degradation Model for Remaining Useful Life Prediction Considering Triple Uncertainty

An Uncertain Random Process-Based Degradation Model for Remaining Useful Life Prediction Considering Triple Uncertainty

A condition‐based maintenance optimization method with oscillating uncertain degradation process

AbstractCondition‐based maintenance (CBM) has gradually gained more attention, and the degradation process has been increasingly applied to maintenance optimization models. The insufficient data and the complex degradation process of the equipment conditions will contribute to epistemic uncertainty. Besides, the implementation of maintenance introduces oscillatory features into the equipment degradation process, deviating from a monotonically decreasing trend, complicating the optimization of CBM. In this article, to simultaneously address the problem of epistemic uncertainty and consider the influence of inspection and maintenance, we establish a new type of degradation model based on uncertainty theory to deal with epistemic uncertainty. Then an uncertain maintenance optimization model is proposed to give an optimal CBM strategy. Finally, a case study is provided to illustrate the proposed CBM optimization method.

Multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state

This study proposes a multi-objective adaptive energy management strategy for fuel cell hybrid electric vehicles considering fuel cell health state. By integrating rule-based control with multi-objective optimization methods, the strategy aims to improve system efficiency, extend the lifespan of proton exchange membrane fuel cells (PEMFC), and reduce operating costs. Based on a comprehensive model of PEMFC output characteristics and lifetime degradation, this study introduces an optimized point line (OPL) strategy. This strategy dynamically adjusts operating constraints according to the state of health (SOH) of the PEMFC, ensuring optimal vehicle performance throughout its lifecycle. To optimize the OPL strategy parameters, a particle swarm optimization algorithm with compression factor was employed, enhancing the strategy’s optimization efficiency, adaptability, and robustness to better handle various real-world operating conditions. The strategy was evaluated under US06 and WLTC driving cycles and compared with traditional power following (PF) and point line (PL) strategies. Results show that compared to the PL strategy, the OPL strategy achieved a 36.4% and 34.2% reduction in operating costs under US06 and WLTC cycles, respectively. Moreover, PEMFC lifetime degradation decreased by 44.9% and 39.4% in these cycles. In high-power regions, the average operating efficiency of PEMFC improved by 2%. The strategy demonstrated good adaptability to different driving conditions, providing an effective solution for optimizing the performance and durability of fuel cell hybrid electric vehicles.

A new multivariate gamma process model for degradation analysis

AbstractIn reliability engineering, it is frequently encountered that multiple performance characteristics (PCs) deteriorate simultaneously. The associated degradation processes are usually dependent and exhibit some heterogeneity from unit to unit, which makes the multivariate degradation modeling and reliability evaluation more challenging. To this end, we propose a new multivariate gamma process model. This model introduces a multivariate random vector, whose joint distribution is constructed by marginal gamma distributions and a copula function, to describe the unit‐to‐unit variability and the dependence among PCs. Meanwhile, it does not require all PCs to be inspected at the same time points in contrast to the traditional copula‐based degradation models. In addition, two reliability evaluation methods are developed. Model parameters are estimated by the stochastic expectation maximization algorithm, and a three‐step procedure is provided to initialize this algorithm. Subsequently, numerical simulations are implemented to verify the proposed methods. Finally, two examples are provided for illustration, and it is shown that the proposed model and methods scale well to the degradation data with different numbers of PCs. What is more, comparisons with several benchmark models are performed, and the superiority of the proposed model is well demonstrated.

Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.

A hybrid battery degradation model combining arrhenius equation and neural network for capacity prediction under time-varying operating conditions

Capacity degradation modeling and remaining useful life (RUL) prediction play a pivotal role in enhancing the safety of battery energy storage systems. Lithium-ion batteries utilized in the systems are subjected to intricate and variable operating conditions, including temperature, charging/discharging rates and depth, which result in time-varying degradation rates. However, most existing models for battery capacity prediction have the limitation of ignoring the quantitative effects of time-varying operating conditions on degradation. Therefore, a hybrid battery capacity degradation model combining Arrhenius equation and lightweight Transformer is constructed. The effects of operating conditions on capacity degradation rates are introduced by an improved Arrhenius degradation equation. The coordinate ascent method embedded with Newton descent is used for estimating the model parameters. Then, the unknown mapping relationships between the degradation statistical features extracted from monitoring data and the degradation factors are captured by the light-weight Transformer. Finally, a novel framework consisting of future capacity and RUL predicting is constructed based on sequential importance resampling (SIR). For the purpose of verification, the cyclic charge-discharge experiments of eight battery cells under two distinct operating profiles are designed and conducted. Compared with other models, the proposed model achieves the best prognostics performance on the tested cells.

Computational ghost imaging enhanced by degradation models for under-sampling.

Computational ghost imaging (CGI) allows two-dimensional (2D) imaging by using spatial light modulators and bucket detectors. However, most CGI methods attempt to obtain 2D images through measurements with a single sampling ratio. Here, we propose a CGI method enhanced by degradation models for under-sampling, which can be reflected by results from measurements with different sampling ratios. We utilize results from low-sampling-ratio measurements and normal-sampling-ratio measurements to train the neural network for the degradation model, which is fitted through self-supervised learning. We obtain final results by importing normal-sampling-ratio results into the neural network with optimal parameters. We experimentally demonstrate improved results from the CGI method using degradation models for under-sampling. Our proposed method would promote the development of CGI in many applications.

Novel N-ZnO/p-BN adsorption-photocatalytic composites with interfacial bonding for efficient synergistic degradation of pollutants in water

Exploring green methods for removing organic pollutants from water bodies has attracted significant attention. In this research, N-ZnO/p-BN adsorption-photocatalytic composites were prepared by a simple solvothermal method, and various analytical characterizations confirmed the formation of B-O-Zn bonds at the heterojunction interface that facilitated the separation of photogenerated carriers and revealed a type-II mechanism of charge transfer in the heterostructure. The photodegradation efficiency for oxytetracycline (OTC) by composites with different N-ZnO loadings was investigated under simulated sunlight, and the degradation efficiency of the optimal sample (50 wt% N-ZnO/p-BN) for 50 mg/L OTC reached 92.2 % at 120 min. Efficient pollutant enrichment, broadened light absorption range, high dispersion of the photocatalysts, and effective charge migration are all important factors for improving photocatalytic activity. Furthermore, the photocatalytic process was further investigated, experiments showed that the N-ZnO/p-BN composites were universally applicable under a variety of conditions and possessed the characteristics of low dosage and high degradable concentration. Finally, a rational photocatalytic mechanism was proposed for the degradation system and the degradation pathways of the main degradation model OTC were analyzed and deduced. Therefore, this study provides new ideas for designing high-performance adsorption-photocatalytic composites.

Remaining useful life prediction based on multi-stage Wiener process and Bayesian information criterion

Equipment will go through multiple degradation stages under complex operating conditions, and the single-stage degradation model cannot accurately describe the degradation process of the equipment at different stages, resulting in inaccurate remaining service life prediction results and reliability analysis. Therefore, this paper establishes a multi-stage Wiener degradation process model that considers measurement errors and includes three different forms of drift functions. First, by calculating the Bayesian information criterion (BIC) values of these three degradation models separately and analysing the variation trends of the BIC values, a method for detecting change-points is proposed to achieve stage division. Next, by comparing the BIC values of the three models, a method for adaptively selecting the optimal model for each stage is proposed. Then, based on the results of stage division and optimal model selection, approximate analytical expressions for the RUL of each stage are derived, and parameter estimation is performed using maximum likelihood estimation (MLE). Finally, the RUL prediction study using the proposed method is carried out through simulation cases and practical cases. The results show that the accuracy of the proposed method is higher than the existing research methods, verifying the effectiveness of the proposed method.