Degradation Model Research Articles

Generation diffusion degradation: Simple and efficient design for blind super-resolution

In the domain of computer vision, blind super-resolution is a key area focused on generating high-resolution images with enhanced visual quality from low-resolution counterparts affected by indeterminate degradation factors. This area is primarily advanced through self-supervised learning techniques utilizing GANs. Despite their prominence, GAN-based methods encounter challenges including unstable training dynamics and limited diversity, compounded by the intricate necessity to configure degradation models to mimic various blur effects and noise types. Lately, denoising diffusion models have shown promising results in image restoration, yet their sampling efficiency constraints impede their deployment in real-time scenarios. This study introduces the Generation Diffusion Degradation (GDD) model, a novel and efficient technique for replicating image degradation by applying random Gaussian noise in a sequential manner via a parametric Markov chain, followed by a progressive reconstruction of the initial image through a U-net-based noise predictor. This method adeptly mirrors the inherent degradation distribution observed in actual degraded images. Furthermore, we present an innovative training strategy that utilizes a composite loss function to train the GDD model, ensuring stable training, improving the authenticity of the generated degraded images, and precisely reflecting the degradation patterns of target images. Extensive experimental analyses underscore the superior performance of the proposed GDD model, both in objective metrics and subjective visual quality. The code is available at https://github.com/lgylab/GDD.

Modeling and prediction of degradation induced by light on monocrystalline silicon solar cell

Abstract In this work, the qualitative and quantitative study of degradation extent caused by LeTID on monocrystalline silicon solar cells under different treatment temperatures and irradiance intensities is carried out. A mathematical model of the relationship between the degradation temperature and irradiance intensity is established. The concept of equivalent temperature and equivalent irradiance intensity is introduced to establish the relationship between the constant temperature and irradiance in the laboratory and the changing temperature and irradiance outdoors, and the outdoor degradation prediction of LeTID of monocrystalline silicon cells is realized. Using this model can help the PV industry to predict the outdoor long-term performance of PERC solar cells.

Optimal Day-Ahead Scheduling of Fast EV Charging Station With Multi-Stage Battery Degradation Model

Optimal Day-Ahead Scheduling of Fast EV Charging Station With Multi-Stage Battery Degradation Model

Study of vibration suppression and energy harvesting for a Vibration-based Piezoelectric–Electromagnetic energy harvester with nonlinear energy sink

Vibration-based energy harvesting devices are prone to excessive displacement response. The effective vibration suppression is critical to the longevity and safety of energy harvesting systems. In this work, the vibration-based Piezoelectric–Electromagnetic energy harvester with nonlinear energy sink (NES-VBPEEH) is proposed to achieve the dual efficacy goals of vibration suppression and vibration energy harvesting. The electromechanical-coupled distributed governing equations are derived through Hamilton’s principle, Euler’s Bernoulli theory, Gauss’s law and Faraday’s law of electromagnetic induction. The output performances of the degradation model of the NES-VBPEEH are validated by previous work. Parameter analysis indicates that the external resistance of the piezoelectric energy harvester significantly affects the electrical damping of the system and ultimately affects the output response of the NES-VBPEEH. The external resistance and coil turns of the electromagnetic vibration energy harvester can significantly affect the output response of the NES-VBPEEH. The output power of both types of energy harvesters is maximized with respect to the resistance and the coil turns. The output response of the NES-VBPEEH exhibits certain nonlinear characteristics as the external excitation amplitude increases to a certain value. The damping and nonlinear stiffness of the NES can significantly affect the output power of the NES-VBPEEH, and appropriate parameter selection can help to achieve the dual efficacy goals of vibration suppression and vibration energy harvesting for the energy harvester.

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.

A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries.

We present a novel decision-making framework for accelerated degradation tests and predictive maintenance that exploits prior knowledge and experimental data on the system's state. As a framework for sequential decision making in these areas, dynamic programming and reinforcement learning are considered, along with data-driven degradation learning when necessary. Furthermore, we illustrate both stochastic and machine learning degradation models, which are integrated in the framework, using data-driven methods. These methods are presented as a valuable tool for designing life-testing experiments and for maintaining lithium-ion batteries.

A continuum damage mechanics model for fatigue degradation and failure of short fiber reinforced composites

The present study is concerned with the development and implementation of a continuum damage mechanics model describing the degradation and failure of short fiber reinforced composites subjected to static and fatigue loads. Towards a numerically efficient formulation, the model is defined entirely on the macroscopic scale, although being motivated by microstructural considerations. Based on experimental observations, an anisotropic linear elastic formulation is adopted as a base formulation. The elastic model is coupled with a damage formulation assuming that damage is driven by the approach of a state point in strain space to a Tsai-Hill type failure envelope. The model is implemented as a user defined subroutine into a finite element formulation. As a numerically highly efficient alternative, an enhanced Wöhler-Miner type post-processing procedure is proposed. Both numerical approaches are found in good agreement with experimental data obtained in coupon experiments as well as in experiments on specimens with more complex geometry and loading conditions.

Evaluation of the influence of an aggressive environment on the durability of the cement stone

The paper proposes methods for assessing the durability of building materials and structures based on Portland cement when exposed to aggressive environments that mimic the products of the vital activity of bacteria on building materials. To determine the main parameters of the model of degradation of building materials under the action of aggressive environments, a mathematical model has been developed in the form of integral and differential relations connecting these parameters. A technique for identifying the mechanical characteristics included in these models based on the solution of inverse biodegradation problems has been developed. The analysis of changes in the structure of the cement stone was carried out using the results of computed tomography, and the regularities of the distribution of pores in the cement stone from the time of exposure were obtained. Based on experimental and numerical studies, it has been established that the mechanism of destruction of cement stone obtained by the traditional method and activation in the vortex layer apparatus is different. The difference lies in the greater accumulation of cement stone interaction products in the activated sample, which is confirmed by a shift in porosity to less than 0.5 mm and a lower solubility value compared to the control composition. The compressive strength of the samples as a result of exposure for 28 days decreased by 37% and 20% for the control and activated compositions. The mass of the studied samples as a result of exposure decreased by 49% and 21%, respectively. On the basis of this mechanism, a mathematical model of the process of material degradation in an aggressive one is developed, taking into account changes in porosity and acidity concentration, and dependence of material strength reduction are obtained.

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression.

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

Optimizing multi-objective design, planning, and operation for sustainable energy sharing districts considering electrochemical battery longevity

The urgent need to address the global warming crisis has led to a paradigm shift in energy production from traditional fossil fuels to renewable sources such as geothermal, solar, and wind. This transition necessitates efficient energy management strategies to enhance renewable energy utilization, reduce microgrid dependency, and establish a more stable power grid. This study explores the novel integration of interactive energy sharing networks utilizing electrochemical battery storage, emphasizing detailed modeling of battery degradation, smart energy management, and multi-criteria decision-making. This research involves an innovative method combining Monte Carlo Tree Search technique and a multi-criteria approach for energy management in district communities. The research delves into the complexities of integrated multi-energy systems, considering structural designs, advanced modeling methods, and multi-criteria predictions. It also employs powerful machine learning methods to predict battery depreciation, demonstrating their superiority over standard semi-empirical models. The study also proposes deterministic and stochastic methods for intelligent energy management, considering unpredictable factors like weather and energy demand profiles. The research incorporates a comprehensive examination of interactive energy sharing networks, encompassing renewable-to-vehicle and vehicle-to-building interactions. Through an in-depth analysis this research highlights the advantages of multi-agent systems, time-of-use energy shifting, demand profile flattening, and micro-grid resilience.

A remaining useful life prediction algorithm incorporating real-time and integrated model for hidden actuator degradation

This paper proposed a prediction algorithm for the degraded actuator taking into account the impact of estimation error of hidden index in the closed-loop system. To this end, a unified prediction framework is established to evaluate the hidden degradation information and recursively update the degradation model parameters simultaneously. The advantage is that the prediction framework can comprehensively compensate the estimation error of hidden degradation index caused by system uncertainty. To jointly estimate the degradation information in avoidance of the impact of system uncertainty, a modified adaptive Kalman filter is designed, and the proof of stability is provided. With the priori estimate from the filter, the degradation model parameters are updated by the inverse filtering probability based on Bayes’ theorem. It is followed by the computation of the remaining useful life (RUL) prediction utilizing aforementioned hidden degradation information and the latest degradation model. The effectiveness of the proposed RUL prediction algorithm is demonstrated by the degraded actuator in the continuous casting process.

Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

This paper presents a maintenance model for optimal planning of pipeline network inspections subjected to corrosion and residual stress. The proposed model includes predictive degradation modeling, spatio-temporal reliability, and expected cost minimization. The pipeline networks are formed by linear segments and irregular zones such as elbows, weld joints, and flanges for putting pipes together and changing the direction of gas or liquid flow. The mean of Karhunen-Loève expansion is used to model space-variant corrosion, residual stress, and stochastic dependency in different exposure zones. Monte Carlo simulations are employed to compute the failure probability of individual components. Subsequently, a Bayesian network evaluates the failure probability of the complex pipeline network and identifies the most critical components. Depending on the objective or market demand uncertainties, a cost model is also proposed to optimize the inspection policy. The methodology is applied to a case study, and the results show that it can provide valuable insights to decision-makers to enhance complex systems' safety and reliability.

Real time conditioning monitoring of MOSFET using artificial neural network regression

The metal–oxide–semiconductor field-effect transistor (MOSFET) is a critical semiconductor device widely used in electrical and electronic systems. The performance of systems depends on the component’s reliability. More than 30% of failures of electronic and electrical systems are due to MOSFET failure. Industries are paying more apprehension towards the improvement of MOSFET reliability. Temperature stress is one of the key parameters to examine the reliability performance of semiconductor devices and to predict the lifetime of the components. This article provides a quick analysis of the MOSFET degradation models. The primary failure precursor metric that reflects the level of degradation is the MOSFET on-resistance R DS(on). In this paper, a linear, non-linear, and artificial neural network (ANN) deterioration model of MOSFETs is presented with the experimental data. The coefficient of determination (R-square), and root mean square error (RMSE) are used to compare the real data. Finally, it is demonstrated that the proposed model works well with experimental data. More than 500 data were trained for the proposed model. It can therefore predict the real-time conditioning monitoring of MOSFETs to increase the reliability of electrical systems.

Optimization and modeling of solar photocatalytic degradation of raw textile wastewater dyes using green ZnO-ED NPs by RSM

ABSTRACT This study aims to use green ZnO-ED NPs produced from Eleocharis dulcis (E. dulcis) extract to maximize solar photocatalytic degradation of raw textile wastewater. The optimization of photocatalysis was decided using the response surface methodology (RSM) as a function of ZnO-ED NPs mass load (0.1–2 g), initial concentration (10–100%), pH (4–9), and contact time (60–200 min). The maximum decolorization (87.34%) and COD removal (100%) were recorded at pH 7, time (60 min), ZnO-ED NPs dosage (2 g/L), and 10% of color concentrations with R2 coefficient of 0.78 at p < 0.05. FESEM analysis showed the presence of granules with smaller diameters than the diameter of the ZnO-ED NPs granules before SPD. EDX analysis revealed the presence of impurities like copper (Cu). XRD analysis indicated the purity of ZnO-ED NPs after SPD, as the values were all quite similar to the XRD values before SPD. The results of an AFM analysis presented that agglomerations of ZnO-ED NPs, in contrast, were somewhat homogeneous in size, nature, and dispersion before SPD.

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

A nonparametric degradation modeling method for remaining useful life prediction with fragment data

Condition-based maintenance (CBM) is an effective way to keep the safety of industrial equipment by predicting the remaining useful life (RUL) and scheduling the maintenance plan before failure. Condition monitoring data is the basis of health state evaluation and RUL prediction. In ideal cases, the monitoring data are collected consecutively starting from the healthy stage until the end of the lifetime. In real industrial cases, however, fragment data often exist due to the interruption of monitoring and/or the loss of sensor readings. The major characteristic of fragment data is that they only record a random period of degradation process. The initial degradation time information is generally lost. Therefore, it is unable to be modeled using the common time-dependent modeling framework. To deal with the above issue, this paper proposes a nonparametric degradation modeling method for RUL prediction with fragment data. In this method, a new state-dependent degradation modeling framework is constructed via two-step axis transform. It formulates the RUL using a function depending on the health state. Based on functional principle component analysis (FPCA), a principal analysis via maximum likelihood estimation (PAMLE) algorithm is developed to recover the missing data of failed units. In addition, a RUL prediction-oriented optimization (POO) algorithm is proposed to predict the RUL of in-service units based on a piece of fragment data. Consequently, the proposed method is capable of dealing with the issues of both data recovery and RUL prediction. The effectiveness of the method is demonstrated using a fatigue crack growth dataset and a lithium-ion (Li-ion) battery degradation dataset.

Fatigue life prediction method for reinforced concrete deck slabs subjected to repeated moving wheel loads and failing in shear

AbstractAs bridge deck slabs experience the direct impact of repeated concentrated wheel loads, they are more susceptible to fatigue‐related damage than other bridge components. In this context, a fatigue life prediction method for reinforced concrete (RC) bridge decks has been developed based on failure mechanisms of RC slabs tested under moving wheel loads. Initially, the formulas encompassed shear strength and S–N equations, accommodating variations in environmental and support conditions, are proposed to facilitate the fatigue life prediction of slabs exposed to constant moving wheel load. Subsequently, a model for shear strength degradation formulated using experimental findings on beam‐like elements is utilized in predicting the fatigue life of slabs subjected to stepwise moving wheel loads. The validity of the proposed method was established by comparing its results with previous experimental data. Notably, compared with the existing prediction equation, the proposed method exhibited more accurate fatigue life prediction results.

Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts

Spare parts redundancy is an effective way to improve system reliability and prolong system lifetime. Generally, preventive maintenance and inventory management of a standby system can significantly reduce operating and maintenance costs, particularly crucial in industrial systems. Additionally, during storage, spare parts may experience degradation failure due to internal mechanisms and sudden failure due to external shocks, complicating the health management of the standby system. Unfortunately, few existing studies consider the impact of spare parts’ competing failures in standby systems. To address this gap, this paper proposes a novel method for predicting the lifetime and optimizing the maintenance policy of nonlinear degradation standby systems, considering both degradation and sudden failures of spare parts. Firstly, we established the degradation model under the competing failure of spare parts and derived the analytical formula for life prediction of the standby system in the presence of competing failures, assuming that sudden failure follows the Weibull distribution. Furthermore, we developed a joint optimization model for replacement maintenance and inventory management based on predictive information, aiming to minimize the expected cost, in which the block replacement interval and the number of spare parts are treated as decision variables. Finally, the effectiveness and potential application value of the proposed method are verified through numerical simulation and a case study.

In Silico Evaluation of In Vivo Degradation Kinetics of Poly(Lactic Acid) Vascular Stent Devices.

Biodegradable vascular stents (BVS) are deemed as great potential alternatives for overcoming the inherent limitations of permanent metallic stents in the treatment of coronary artery diseases. The current study aimed to comprehensively compare the mechanical behaviors of four poly(lactic acid) (PLA) BVS designs with varying geometries via numerical methods and to clarify the optimal BVS selection. Four PLA BVS (i.e., Absorb, DESolve, Igaki-Tamai, and Fantom) were first constructed. A degradation model was refined by simply including the fatigue effect induced by pulsatile blood pressures, and an explicit solver was employed to simulate the crimping and degradation behaviors of the four PLA BVS. The degradation dynamics here were characterized by four indices. The results indicated that the stent designs affected crimping and degradation behaviors. Compared to the other three stents, the DESolve stent had the greatest radial stiffness in the crimping simulation and the best diameter maintenance ability despite its faster degradation; moreover, the stent was considered to perform better according to a pilot scoring system. The current work provides a theoretical method for studying and understanding the degradation dynamics of the PLA BVS, and it could be helpful for the design of next-generation BVS.

Snapshot spectral imaging based on aberration model-driven deep learning.

Coded aperture snapshot spectral imaging (CASSI) can capture hyperspectral images (HSIs) in one shot, but it suffers from optical aberrations that degrade the reconstruction quality. Existing deep learning methods for CASSI reconstruction lose some performance on real data due to aberrations. We propose a method to restore high-resolution HSIs from a low-resolution CASSI measurement. We first generate realistic training data that mimics the optical aberrations of CASSI using a spectral imaging simulation technique. A generative network is then trained on this data to recover HSIs from a blurred and distorted CASSI measurement. Our method adapts to the optical system degradation model and thus improves the reconstruction robustness. Experiments on both simulated and real data indicate that our method significantly enhances the image quality of reconstruction outcomes and can be applied to different CASSI systems.