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AbstractThe stochastic processes are a natural choice for describing the randomness in degradation processes caused by inherent randomness and environmental factors. This article proposes a new modified skew-normal distribution to capture measurement uncertainty, and then establishes a stochastic process-based degradation modeling framework, in which both degradation increments and measurement errors do not follow the Gaussian distributions. Taking the IG process as an example, the basic reliability indicators and the alarm probabilities caused by measurement errors are derived. In addition, a multi-stage parameter estimation algorithm based on moderate particle sizes and comparative tolerances is developed for this stochastic process-based degradation model under dual non-Gaussian assumptions. Finally, the effectiveness and advantages of the proposed model along with the parameter estimation algorithm are demonstrated by a simulation study and a case application, and it is particularly pointed out that the relative size of measurement errors has a significant impact on the precision of degradation reliability assessment. [ABSTRACT FROM AUTHOR]

The economic operation of small modular reactors will partly rely on managing and reducing inspection and maintenance activities while supporting new operational paradigms like load-following. Turbine control valves throttle the steam from the steam generator into the steam turbine while maintaining the pressure within the steam generator at a constant set point. Degradation of these components could impact the ability to manage electrical power production. Utilizing the Idaho National Laboratory Hybrid repository and the Oak Ridge National Laboratory TRANSFORM library developed for multiphysics simulations in Dymola/Modelica, an integral pressurized water reactor system was modeled based on the available specifications of the NuScale power module. The effects of various component degradation modes have been implemented into the model in order to simulate faulted plant data during both steady-state and load-following operations. The fault modes resemble different physical fault modes that may occur at an operating nuclear power plant; a leaking turbine control valve and a valve actuator failure due to loss of hydraulic pressure have been implemented. A neural network autoencoder is employed in conjunction with statistical analysis, namely, simple signal thresholding (SST) or sequential probability ratio testing (SPRT), to identify the presence of a fault. Fuzzy logic is additionally employed in a novel and promising manner to classify the state of the system based on the cumulative sum of the neural network residuals. SST and SPRT are both successfully validated using healthy data and proved capable of identifying both fault types; fuzzy logic identified the false positives and classified the faulted data correctly. [ABSTRACT FROM AUTHOR]

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model's adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model's potential as a reliable tool for real-time RUL prediction in industrial applications. [ABSTRACT FROM AUTHOR]

Step‐stress accelerated degradation test (SSADT) has become a prevailing approach to lifetime assessment for highly reliable products. In practice, many products suffer from multiple degradation processes that significantly contribute to failures. In this paper, we investigate the optimal SSADT plans for products subject to two dependent degradation characteristics modeled by a bivariate inverse Gaussian process. The drift parameter of each process is assumed to be influenced by a common stress factor. A bivariate Birnbaum‐Saunders (BVBS)‐type distribution is employed to approximate the lifetime distribution and facilitate the derivation of the objective function. The optimal plans are prescribed under three common optimality criteria in the presence of constraints on test units and inspections. A revisited example of fatigue crack is then presented to demonstrate the proposed methods. Finally, the sensitivity of the SSADT plans is studied, and the results exhibit fair robustness of the optimal plans. [ABSTRACT FROM AUTHOR]

In a polymer electrolyte membrane (PEM) fuel cell, the following degradation mechanisms are associated with the catalyst particles and their support: carbon support corrosion triggered by carbon and platinum oxidation, platinum dissolution with redeposition, and particle detachment with agglomeration. In this work, an electrochemical model for those degradation effects is presented as well as its coupling with a three‐dimensional computational fluid dynamics PEM fuel cell performance model. The overall model is used to calculate polarization curves and current density distributions of a PEM fuel cell in a fresh and aged state as well as the degradation process during an accelerated stress test with 30 000 voltage cycles. The obtained simulation results are compared to measurements on a three‐serpentine channel PEM fuel cell with an active area of 25 cm2 under various temperatures and humidities. The experimental data are obtained with a segmented test cell using respective degradation protocols and test conditions proposed by the United States Department of Energy. In addition to the temperature and humidity changes, the influence of geometry and material parameters on the degree of degradation and the resulting fuel cell performance is explored in detail. [ABSTRACT FROM AUTHOR]

Capacitors are components predominantly used to stabilize voltage, store energy, and lower electrical ripples. To improve the flexibility and capacitance of capacitors, we were motivated to develop hybrid film capacitors using two emerging thin film techniques, atomic and molecular layer deposition (ALD and MLD). Validation of its operational reliability in a power module or an electrical system is critical to facilitate its adoption. Equivalent series resistance (ESR) and capacitance are key performance characteristics (PC) of a capacitor, whose degradation path and process are nonlinear and dependent. Few studies have implemented the Wiener process (WP), to capture the dependency between the PC, based on the assumption of a normally distributed performance loss, notwithstanding that the performance loss may not be normally distributed. To address this concern as well as improve the accuracy of reliability prediction, a reliability framework is proposed. The gamma distribution is found to better fit the incremental PC degradation used in this work. The derived reliability model captured the non-linearity in the PC degradation path as well as its dependency using a selected Copula function. Akaike information criterion (AIC) was used to determine the most suitable Copula. Reliability estimation based on dependency consideration shows the least mean square error (MSE) compared to reliability estimate using a model that considers only one PC or assumes independent PCs. In addition, the hybrid capacitor was compared with an aluminum oxide dielectric layer capacitor, and the result was discussed. [ABSTRACT FROM AUTHOR]

In this case study, we illustrate the use of a data-driven degradation model in a nuclear-specific application called void swelling. Void swelling is a complex, radiation-induced degradation mechanism that changes the dimensions of materials and damages the structural integrity. Accurate modeling and prediction of void swelling processes is crucial in nuclear power plant (NPP) management and maintenance planning by providing a guideline on the future state of the materials subject to reactor irradiation. Using a Bayesian hierarchical piecewise linear regression framework with a real-world void swelling dataset, we address the following three research questions: (1) How can we construct a data-driven degradation model such that its predictions satisfy the physical properties of void swelling? (2) How can we measure the joint effect of multiple experimental factors on the swelling process? (3) How can we accurately predict the future swelling status under limited data availability? The results on a real-world void swelling dataset not only improve our understanding of the swelling process but also provide a useful reference for nuclear practitioners and degradation researchers. [ABSTRACT FROM AUTHOR]

Under fatigue load, insulated gate bipolar transistor (IGBT) experience structural damage and performance degradation, imparting the reliability of power electronic systems. To address this, the relationship between solder layer fatigue crack length and thermal resistance is derived based on heat transfer theory, firstly. The Darveaux model is used to characterize the evolution law of solder layer cracks in IGBT, and a new IGBT performance degradation model along with a method for estimating its undetermined coefficients is proposed. Then, considering the non-stationary characteristics of actual working conditions, a variable amplitude fatigue load model for IGBT is established using the response surface methodology. The rain flow counting method and linear cumulative damage citation are then employed to assess the extent of IGBT performance degradation. Finally, using a type of IGBT product as an example, power cycling thesis are conducted, and performance degradation modeling and analysis are carried out based on experimental data, verifying the effectiveness of the models and algorithms. [ABSTRACT FROM AUTHOR]

It is of paramount importance to ensure the safe operation of energy pipelines for pipeline owners and operators. Therefore, effective condition assessment of pipelines is imperative. For this purpose, there are a great number of models developed using various techniques. How to select a modeling approach and associated techniques to get the most of the effectiveness of the model under a condition with limited monitoring data and experience remains a big concern to pipeline operators. This paper provides a comprehensive review of the developed approaches and techniques for energy pipeline degradation condition assessment. The primary motivation behind this review is the pivotal role of condition assessment in energy pipeline integrity management and the proliferation of models and techniques, including statistical modeling, stochastic processes, machine learning, and deep learning, used for assessing pipeline degradation. This work aims to identify and assess the challenges and gaps inherent in the utilization of these condition modeling approaches. By systematically analyzing the current state of research and practice, this review not only highlights the strengths and limitations of various modeling approaches but also offers insights into future opportunities for enhancing the research and management practice in the field of pipeline integrity management. Our analysis offers valuable insights for researchers, practitioners, and policymakers in the domain of pipeline integrity management. It facilitates a better understanding of the complexities and intricacies of condition assessment, ultimately contributing to the development of more robust and effective strategies for safeguarding the integrity of energy pipelines. [ABSTRACT FROM AUTHOR]

Small sample sizes cause epistemic uncertainties in reliability estimation and even result in potential risks in maintenance strategies. To explore the difference between stochastic- and uncertain-process-based degradation modeling in reliability estimation for small samples, this study proposes a comparative analysis methodology based on the range of quantile reliable lifetime (RQRL). First, considering both unit-to-unit variability and epistemic uncertainty, we proposed the Wiener and Liu process degradation models. Second, based on the RQRL, a comparative analysis method of different degradation models for reliability estimation under various sample sizes and measurement times was proposed. Third, based on a case study, the sensitivities of the Wiener and Liu process degradation models for various sample sizes and measurement times were compared and analyzed based on the RQRL. The results demonstrated that using the uncertain process degradation model improved the uniformity and stability of reliability estimation under small-sample conditions. [ABSTRACT FROM AUTHOR]

This article mainly investigates a proactive replacement policy for a stochastically deteriorating system concurrently subject to two types of shocks. First, the closed-form representation of system reliability function suffering from both a degradation process and environmental shocks is derived based on the degradation-threshold-failure (DTS) modeling framework. An age- and state-dependent competing risks model with mutual dependence between the two failure processes is embedded into system reliability modeling, where two types of shocks are taken into consideration upon arrival of an external shock including a minor one and a major one. Based on which, a bivariate maintenance policy is put forward for the deteriorating system, where the system is proactively replaced before failure at a planned time, or at an appropriate number of minimal repairs, whichever takes place first. The expected long-run cost rate (ELRCR) is formulated, and optimal solutions are evaluated analytically for two special cases. Finally, an illustrative example is redesigned to validate the theoretical results, exploring the significance of two types of shocks and mutual dependence in system reliability modeling, and illustrating the potential applications in maintenance decisions in various manufacturing systems. [ABSTRACT FROM AUTHOR]

It is of paramount importance to ensure the safe operation of energy pipelines for pipeline owners and operators. Therefore, effective condition assessment of pipelines is imperative. For this purpose, there are a great number of models developed using various techniques. How to select a modeling approach and associated techniques to get the most of the effectiveness of the model under a condition with limited monitoring data and experience remains a big concern to pipeline operators.This paper provides a comprehensive review of the developed approaches and techniques for energy pipeline degradation condition assessment. The primary motivation behind this review is the pivotal role of condition assessment in energy pipeline integrity management and the proliferation of models and techniques, including statistical modeling, stochastic processes, machine learning, and deep learning, used for assessing pipeline degradation. This work aims to identify and assess the challenges and gaps inherent in the utilization of these condition modeling approaches. By systematically analyzing the current state of research and practice, this review not only highlights the strengths and limitations of various modeling approaches but also offers insights into future opportunities for enhancing the research and management practice in the field of pipeline integrity management.Our analysis offers valuable insights for researchers, practitioners, and policymakers in the domain of pipeline integrity management. It facilitates a better understanding of the complexities and intricacies of condition assessment, ultimately contributing to the development of more robust and effective strategies for safeguarding the integrity of energy pipelines.

In degradation modeling, stochastic processes often do not meet the classical properties necessary for traditional goodness‐of‐fit tests. This paper presents an initial investigation into employing the ACH depth function and its potential in degradation model selection. We commence by presenting various stochastic processes as degradation models and their selection criteria. Subsequently, we delve into the ACH depth function, highlighting its potential in this context. Through simulated data, we assess the application of this functional depth measure for model selection. The methodology's validity is further reinforced by its application to real‐world data, underscoring its effectiveness. [ABSTRACT FROM AUTHOR]

Degradation modeling using condition monitoring (CM) data is fundamental for prognostics and health management (PHM). However, due to the variations in manufacturing materials and operating environments, degradation heterogeneity makes life prediction difficult. Motivated by this problem, a flexible nonlinear Wiener process with random drift diffusion is proposed for degradation modeling. Different from traditional methods, this approach regards both drift and diffusion coefficients as random parameters to describe the heterogeneity of the degradation rate and volatility simultaneously. In addition, the model supports the selection of appropriate distribution types for the random parameters according to the statistical characteristics of the actual data to improve fitting performance. To effectively overcome the parameter estimation difficulties caused by model assumptions, we propose a two-stage maximum likelihood estimation (MLE) algorithm embedded with a distribution selection strategy to estimate the model parameters. Specifically, the method is first used to estimate the drift and diffusion coefficients of each unit. Then, the estimated coefficients are used to select the distribution types and perform MLE for parameters in the selected distributions. The effectiveness of the proposed parameter estimation algorithm is demonstrated with both simulated datasets and real turbofan engine datasets. A comparison of results show that compared to simplified versions and traditional methods, the proposed degradation model improves the fitting performance and life prediction accuracy. [ABSTRACT FROM AUTHOR]

The supervised super-resolution (SR) methods based on simple degradation assumptions (e.g., bicubic downsampling) have unsatisfactory generalization ability on real-world thermal images. To enhance the SR effect of real-world sceneries, we introduce an unsupervised SR framework for thermal images, incorporating degradation modeling and corresponding SR. Inspired by the physical prior that high frequency affects details and low frequency affects thermal contrast, we propose a frequency-aware degradation model, named TFADGAN. The model achieves image quality migration between thermal detectors of different resolutions by degrading different frequency components of the image from high-resolution (HR) to low-resolution (LR). Specifically, by adversarial learning with unpaired LR thermal images, the complex degradation processes of HR thermal images at low and high frequencies are modeled separately. Benefiting from the thermal characteristics mined from real-world images, the degraded images generated by TFADGAN are similar to LR thermal ones in terms of detail and contrast. Then, the SR model is trained based on the pseudo-paired data consisting of degraded images and HR images. Extensive experimental results demonstrate that the degraded images generated by TFADGAN provide reliable alternatives to real-world LR thermal images. In real-world thermal image experiments, the proposed SR framework can improve the peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM) by 1.28 dB and 0.02, respectively. [ABSTRACT FROM AUTHOR]

Capacitor banks (CBs) play a crucial role in energy storage and frequency control within autonomous microgrids. However, the impact of internal capacitor configurations, varying in terms of equivalent series resistance (ESR), capacitance, and rated voltage, on CB degradation, reliability, and peak current remains an understudied aspect. Moreover, the absence of a capacitance degradation coefficient in the standard MIL-HDBK-217 equations for predicting the reliability of electrolytic capacitors poses a significant challenge. To address these issues, this study examines a microgrid composed of diverse renewable energy systems, featuring nine distinct CB arrangements. The design of CBs considers both capacitance and peak output current individually. An evaluation is conducted to compare construction costs, lifetimes, and peak output currents across all layouts. Additionally, a novel formula is introduced to estimate the reliability and lifetime of CBs, while an existing formula for calculating CB peak output current is enhanced. The research explores the impact of ambient temperature and capacitor voltage on the reliability of various capacitor designs, proposing a novel framework for assessing CB reliability based on MIL-HDBK-338B, which accounts for both short-circuit and open-circuit faults. The practicality of these findings is confirmed through a comparison of experimental and simulation results. The inverter operation video, simulation, and all production data including PCB and processor codes are also attached.

In response to the issue of neglecting the inherent differences in samples in the traditional degradation analysis of long-life aerospace products, a degradation modeling method combining a frailty model is proposed to quantify the unobservable heterogeneity in random degradation processes. Firstly, the frailty term is described using a generalized inverse Gaussian distribution to more comprehensively capture random effects. Secondly, an inverse Gaussian degradation model is established, combined with the frailty model to consider the unobservable heterogeneity in practical use. Through maximum likelihood estimation, parameter estimation is carried out, and Bayesian theory is used to infer an independent frailty term in order to quantify differences between individual products. Finally, reliability analysis is conducted on a core chamber cooling control valve of a certain aerospace engine. The results indicate that neglecting the unobservable heterogeneity will lead to overly ideal reliability estimates, and quantifying random effects reasonably can make the model’s estimation closer to the actual situation.

Several factors, such as human error, environmental factors, and maintenance practices, contribute to the degradation of real-world industrial systems. Predicting system dynamics is challenging and necessitates high user confidence, as these factors contribute to both aleatoric uncertainty (inherent system variability) and epistemic uncertainty (due to limited information). Decision-making and risk assessment are frequently hindered by the inability of current artificial intelligence methods to generate interpretable uncertainty estimates. To address these constraints, we propose an analysis that employs Deep Gaussian Processes (DGPs), a robust framework for generating interpretable uncertainty distributions and capturing system variability. A rigorous mathematical foundation is essential to our approach, because it enables the selection of metrics that effectively capture the system’s degradation aspects. In addition to predicting the remaining useful life, these metrics, when used in conjunction with DGPs, facilitate the creation of a degradation model that is both accurate and dependable. This model also contributes to the improvement of system reliability and proactive maintenance. We demonstrate our approach’s practical efficacy by validating it on a real-world industrial semolina plant with four mills.

Abstract Marine nonindigenous species (NIS) are spreading at an alarming rate internationally through anthropogenic activities such as shipping and aquaculture, affecting local biodiversity and negatively impacting the ecosystem and human well‐being. Countries and international organizations have recognized this global threat and have begun implementing biosecurity management programs to ensure early detection, effective surveillance, and mitigation of marine NIS spread. Molecular techniques based on environmental DNA and RNA (eDNA/eRNA), collectively referred to as environmental nucleic acids (eNAs), have become a popular noninvasive tool for detecting NIS and monitoring biodiversity locally and globally. However, uncertainties about eNAs detection probabilities and the location of the source population impede the broad uptake of this tool in marine biosecurity programs. It's been hypothesized that most of these uncertainties can be explained by studying the molecules' dynamics within a marine environment and implementing eNAs distribution models. To contribute to further knowledge development in this area, our study reviews data from 20 recent reports on the degradation mechanisms and fate of eNAs in the marine environment. We classified the critical factors influencing eNAs' persistence that should be considered by biosecurity practitioners, outlining the complex interaction between the molecules' degradation processes and particular environmental conditions. To help guide the parameterization of eNAs distribution models, this review also summarizes and standardizes the marine decay rates of eDNA/eRNA from the literature. Finally, this manuscript outlines guidelines to help calculate accurate decay rates to build appropriate “fit‐for‐purpose” marine biosecurity tools for improved target detectability and greater resolution in assessing biodiversity.

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Successful modeling of degradation data is of great importance for both accurate reliability assessment and effective maintenance decision‐making. Many of existing degradation performance modeling approaches either assume a homogeneous population of units or characterize a heterogeneous population with some restrictive assumptions, such as pre‐specifying the number of sub‐populations. This paper proposes a Bayesian heterogeneous degradation performance modeling framework to relax the conventional modeling assumptions. Specifically, a Bayesian non‐parametric model formulation and learning algorithm are proposed to characterize the historical degradation data of a heterogeneous population of units with an unknown number of homogeneous sub‐populations and allowing the joint model estimation and sub‐population number identification. Based on the off‐line population‐level model, an on‐line individual‐level degradation model with sequential model updating is further developed to improve remaining useful life prediction of individual units with sparse data. A real case study using the heterogeneous degradation data of deteriorating roads is provided to illustrate the proposed approach and demonstrate its validity. [ABSTRACT FROM AUTHOR]

The increasing popularity of electric vehicles is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts electric vehicles and hybrid electric vehicles at a disadvantage compared to traditional internal combustion engine vehicles. This paper presents a prognostic-based control framework for hybrid electric vehicles to reduce the cost of operating hybrid electric vehicles by considering the degradation of energy storage systems. The strategy utilizes a degradation forecasting model of electrical components to predict their degradation pattern and uses the prediction to control hybrid electric vehicles via their energy management systems to reduce the degradation of components. A real-time controller hardware-in-the-loop is set up to run the proposed strategy. An hybrid electric vehicle model is developed on Typhoon (i.e., a real-time simulator), which is connected to two layers, energy management and degradation forecasting layer, deployed in Raspberry Pis, respectively. All these components are communicated through CAN communication, where the actual operating condition of the vehicle is sent from Typhoon to each Raspberry Pis to implement the proposed control strategy. With this approach, the cost of operating hybrid electric vehicles can be reduced, making them more competitive than their combustion engine counterparts shown in both numerical simulations and the CHIL experiment.

Having accurate quantitative and qualitative information about the state of forest stands, is necessary for any basic management and planning, to reduce the effects of forest degradation. The current study aimed to model the destruction of Hyrcanian forests under the effects of density and volume (per hectare) variables, using logistic regression. In total, 252 plots of 1000 m2 area were measured. In each sample plot, species name, Diameter at Breast Height (DBH), height, density, volume and the presence or absence of forest degradation were measured and recorded. To model forest degradation, logistic regression model was utilized and Omnibus test, log-likelihood and pseudo r-square (Cox&Snell and Nagelkerke) coefficients were used to evaluate the model. Results showed that the mean of density and volume of trees were 136.8 tree and 239.9 m3/ha, respectively. In addition, the results indicated that 46.82% of the study area was degraded. The results of correlation test showed that there was a srtong negative correlation between quantitative variables and the forest degradation. The independent variables of density and volume of trees were respectively explained 61.6 to 82.3% of the variance of the dependent variable (forest degradation). Among the input variables of the regression model, the effects of density and volume were significant on the forest degradation and it was possible to predict the changes of dependent variables (presence or absence of forest degradation).

The modern engineering systems often operate under varying environments and only partial information can be observed at discrete monitoring epochs. For such systems, few works have been done for the prognostics of health status using the available environment and monitoring information. Therefore, the aim of this article is to present a new health prediction method for modern engineering systems whose condition is partially observable under varying environments. A dynamic Gamma process is proposed to model the system degradation observations under changing environments. To describe the relation of system actual status to the observed information, a proportional hazard (PH) model integrating internal aging and external observations is presented for modeling the system hazard rate. To realize prediction of residual life of such systems, a matrix operation-based prognostic method is presented to calculate the closed-form solutions of health characteristics for the system. A case study of partially observable failing systems is demonstrated, and comparisons with other recent developed approaches are also given to show the effectiveness of the model. • Novel environment-varying degradation model. • PH model incorporating failure and environment processes. • Construction of a matrix-based approach for health estimation. • Closed-form expressions for health characteristics. • Comparisons of different published models via case studies. [ABSTRACT FROM AUTHOR]

The deep learning based super-resolution (SR) methods have recently achieved remarkable progress in the reconstruction of ideally simulated high-quality remote sensing image datasets. However, due to the large variation in image quality caused by the complex degradation factors, their performance decreases dramatically on real-world images acquired by different satellite sensors. To this end, we propose a cross-sensor SR framework that consists of a cross-sensor degradation modeling strategy for bridging the gap between the images obtained by the source and target sensors, and an edge-guided attention-based SR (EGASR) network to promote the learning of high-frequency feature representation. Specifically, we build a degradation pool on the low-resolution (LR) target sensor to produce a degraded training dataset simulated from the high-resolution (HR) images obtained by the source sensor. Furthermore, the EGASR network, which employs the edge-guided residual attention block (EGRAB) to introduce implicit edge prior to enhance edge-related information, is embedded in the cross-sensor SR framework for reconstructing HR results with sharp details. The proposed method is applied on images from the Chinese Gaofen (GF) satellite sensors and compared to several representative SR methods. An ideally simulated GF-2 LR/HR image set with only downsampling considered is first used to evaluate the effectiveness of the proposed EGASR network. Moreover, GF-2/GF-1 and GF-2/GF-6 cross-sensor SR datasets are constructed by synthesizing GF-2 degraded image pairs with the degradation pools estimated from the GF-1 and GF-6 images, respectively. The results show that: 1) the proposed EGASR model shows superiority in reconstructing textural details and edge features, and achieves the best results among the state-of-art SR methods involved in comparison; 2) the cross-sensor SR framework significantly promotes the model's ability to super-resolve real-world LR images acquired by the target satellite sensors, e.g., the NIQE values are improved by at least 30% and 34% on average with respect to other comparative methods for GF-2/GF-1 and GF-2/GF-6 datasets in the real experiments, respectively. Code is available at https://github.com/zhonghangqiu/EGASR. [ABSTRACT FROM AUTHOR]

This paper studies the statistical inference in a degradation model with imperfect maintenance. Technological or industrial devices subject to degradation undergo maintenance actions that reduce their degradation level. The underlying degradation process is a Wiener process with drift. Maintenance effects are assumed to be imperfect, described by an Arithmetic Reduction of Degradation (ARD1$$ AR{D}_1 $$) model. The system is regularly inspected and the degradation levels are measured. Four different observation schemes are considered so that degradation levels can be observed between maintenance actions as well as just before or just after maintenance times. The paper studies the estimation of the model parameters under the four observation schemes. Maximum likelihood estimators are derived for each scheme. The quality of the estimations is assessed and the observation schemes are compared through an extensive simulation and performance study. [ABSTRACT FROM AUTHOR]

• A framework is proposed to monitor the electrochemical performance and predict the durability of organic coatings. • A model based on the three-phase Wiener process is used to describe the coating degradation process. • The method based on change point detection is used to divide the degradation stage of the coating. • The reliability and durability of the coating system are evaluated using Fiducial Inference-Monte Carlo method. The deterioration of organic anticorrosive coating systems not only diminishes the lifetime of underlying metals but also incurs substantial economic losses and the risk of unforeseen catastrophes. Thus, rigorously monitoring and accurately predicting the lifetime of organic anticorrosive coatings are crucial. In this paper, we introduce a new approach for monitoring the electrochemical performance and predicting the durability of organic coatings. Initially, we propose a model grounded in degradation mechanisms that merges an aging kinetic model of the coating with the three-phase Wiener process, accurately representing the degradation trajectory and its inherent uncertainties. We then apply change point detection techniques, utilizing the t distribution test and the Schwarz Information Criterion, to delineate the three distinct phases of coating degradation. By defining new failure criteria based on degradation thresholds and stages, we develop a reliability model for coating systems and apply the Fiducial Inference-Monte Carlo method for numerical solutions and interval estimations. To corroborate the efficacy of our methodology, we designed electrochemical sensors for coatings and executed accelerated degradation experiments in the laboratory. The model parameters derived from the initial three sets of test data successfully predict the behavior of the fourth data set, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper discusses the challenge of evaluating the prognosis quality of machine health index (HI) data. Many existing solutions in machine health forecasting involve visual assessing of the quality of predictions to roughly gauge the similarity between predicted and actual samples, lacking precise measures or decisions. In this paper, we introduce a universal procedure with multiple variants and criteria. The overarching concept involves comparing predicted data with true HI time series, but each procedure variant has a specific pattern determined through statistical analysis. Additionally, a statistically established threshold is employed to classify the result as either a reliable or non-reliable prognosis. The criteria include both simple measures (MSE, MAPE) and more advanced ones (Space quantiles-inclusion factor, Kupiec's POF, and TUFF statistics). Depending on the criterion chosen, the pattern and decision-making process vary. To illustrate effectiveness, we apply the proposed procedure to HI data sourced from the literature, covering both warning (linear degradation) and critical (exponential degradation) stages. While the method yields a binary output, there is potential for extension to a multi-class classification. Furthermore, experienced users can use the quality measure expressed in percentage for more in-depth analysis. • Method for the evaluation of the quality of a prediction is discussed. • Several metrics are proposed. • Developed approach can be applied for both Gaussian and non-Gaussian data. • Proposed algorithm is applicable for second and third regimes. • Both simulation and real data sets are analyzed. [ABSTRACT FROM AUTHOR]

• A global adaptive group maintenance policy for multi-component systems is devised. • Maintenance is delayed to provide sufficient buffer time for resource scheduling. • PdM and OM are integrated dynamically over an infinite time horizon. • A heuristic grouping algorithm is developed to alleviate computation complexity. Intelligent preventive maintenance powered by health data analytics is essential to ensure operation safety and performance of diverse industrial equipment. Despite the rapid advancement of preventive maintenance methodologies in recent several decades, their implementation to global dynamic maintenance of complex systems across infinite time horizon, in particular leveraging real-time prognosis information to realize adaptive group partition and ordering, has been largely an under-explored domain. To this end, this study devises a generic global-dynamic group maintenance policy for multi-component degrading systems. As opposed to existing models, the policy automatically renews the entire system health information instantly upon the completion of each group maintenance task; therefore, it realizes the dynamic union of (a) predictive group maintenance and (b) unplanned opportunistic maintenance over an infinite time horizon for the first time, such that to promote decision precision and efficiency. A dynamic grouping algorithm is designed to explore the structure of optimal maintenance solutions. The applicability is exemplified by comparative experiments that show substantial advantages over heuristic policies. [ABSTRACT FROM AUTHOR]

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems.

The evidential variable shows the powerful ability on the parameter description, so we have cooperated it with the linear Wiener process to model linear degradation processes. However, the degradation paths of some products exhibit two‐phase patterns over the running time, which cannot be modeled by the linear Wiener process. Hence, it is developed into an associated evidential variable‐based two‐phase Wiener process. In order to utilize the proposed model, the related evidence obtaining, evidence fusion and performance inference approaches are constructed. A simulation study and an actual case are performed to verify the superiorities of the proposed method. The proposed method provides higher accuracies of the useful life, degradation, and reliability evaluations, due to utilizing the evidential variable and considering the two‐phase degradation conditions. [ABSTRACT FROM AUTHOR]

While the growth of unmanned aerial vehicle (UAV) usage over the next few years is indisputable, cooperative operation strategies for UAV swarms have gained great interest in the research community. Mission capabilities increase while contingencies can be mitigated through intelligent management between the operating swarm and the available fleet. The importance of observing the system reliability and of risk assessment grows because the dysfunction of one asset within a system of systems endangers the superordinate mission goals of the operating UAV swarm. Thus, not only is trajectory planning beneficial for usage optimization, but prognostic and health management (PHM) methods, including diagnostics and prognostics, also enable situational awareness and condition-driven asset management to achieve higher mission reliability. The novelty of this work is the observation of asset states based upon a generically modeled multi-component degradation behavior and the integration of PHM methods with real-time capabilities in order to support decision making during mission execution in a highly dynamic and event-based environment. The developed simulation enables the testing and comparison of different maintenance strategies that are integrated into the simulation to show and discuss the effectiveness and benefits of real-time-capable PHM methods. [ABSTRACT FROM AUTHOR]

Fault detection and degradation modeling are two main concerns in condition-based maintenance (CBM). The initial machine degradation is called first predicting time (FPT) or incipient failure time. FPT is typically assumed prior information. FPT detection aims to provide such prior information for subsequent degradation modeling in CBM. Moreover, the majority of existing methodologies regard FPT detection and degradation modeling as two separate tasks. A generic framework for integration of the FPT detection with degradation modeling is proposed in this article via fusion of spectrum amplitudes in the frequency domain to realise FPT detection and degradation modeling in a unified manner. First, a generalised health index is constructed using the sum of weighted spectrum amplitudes. Second, two properties are proposed to describe FPT detection and degradation modeling. Third, these two properties and their constraints are mathematically formulated as a quadratic programming model to find optimal weights for the fusion of spectrum amplitudes automatically. Finally, three illustrative examples are used to demonstrate the superiority of the proposed methodology over some existing commonly used sparse measures and a machine learning method in the FPT detection and degradation modeling. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) have been recognized as a promising power generation source for a wide range of automotive, stationary, and portable electronic applications. However, the durability of PEMFCs remains as one of the key barriers to their wide commercialization. The membrane electrode assembly (MEA) as a central part of a PEMFC, which consists of a proton exchange membrane with a catalyst layer (CL) and gas diffusion layer (GDL) on each side, is subject to failure and degradation in long-running and cycling load conditions. The real-time monitoring of the degradation evolution process through experimental techniques is challenging. Therefore, different numerical modeling approaches were proposed in the literature to assist the understanding of the degradation mechanisms in PEMFCs. To provide modeling progress in the addressed field, this paper briefly discusses the different degradation mechanisms occurring in the MEA. In particular, we present a detailed review of MEA degradation modeling research work, with special attention paid to the physical-based models (mechanistic models). Following the most recent relevant literature, the results showed that the combination of microstructure component models with macro-scale comprehensive PEMFC models provides a better understanding of degradation mechanisms when compared to single-scale degradation models. In this sense, it is concluded that in order to develop an accurate and efficient predictive degradation model, the different relevant scales ranging from nano- to macro-sized scales should be considered, and coupling techniques for multiscale modeling have to be advanced. Finally, the paper summarizes the degradation models for different MEA components. It is highlighted that the GDL chemical degradation models that describe damage accumulation are relatively limited. The paper provides a useful reference for the recent developments in the MEA degradation modeling of PEMFCs. [ABSTRACT FROM AUTHOR]

Machine degradation is a complex, dynamic and irreversible process and its modeling is a leading-edge technology in prognostics and health management (PHM). In recent years, machine learning algorithms have been widely used to model machine degradation. However, these degradation models are not physically interpreted so that their extended uses are reduced and weakened. Aiming at solving this problem and visualizing informative features learned from degradation data, in this paper, a generalized machine degradation modeling methodology is proposed by integrating multiple-source fusion with genetic programming (GP). A composite fitness function of GP tailored for machine degradation modeling is innovatively designed. Afterward, multiple process sensor data, such as temperature, pressure, currents, etc., and non-process sensor data, such as vibration and acoustic signals, can be respectively modeled and fused into structurally interpreted health indicators from the time domain and the frequency domain. Moreover, the proposed methodology can automatically select informative frequency components and sensors, and provide transparent modeling architecture for early fault detection and subsequent monotonic degradation assessment. Another benefit of the proposed methodology is that complicated data preprocessing and manual feature extraction are not required anymore. Hence, the proposed methodology would have many potential applications and it is easy to implement for online machine degradation modeling. A gearbox run-to-failure dataset (non-process data) and an aircraft engine degradation dataset (process data) are studied to verify the effectiveness of the proposed methodology. Comparisons show that structurally interpreted health indicators constructed from the proposed methodology are superior to state-of-the-art works.

Having accurate quantitative and qualitative information about the state of forest stands, is necessary for any basic management and planning, to reduce the effects of forest degradation. The current study aimed to model the destruction of Hyrcanian forests under the effects of density and volume (per hectare) variables, using logistic regression. In total, 252 plots of 1000 m2 area were measured. In each sample plot, species name, Diameter at Breast Height (DBH), height, density, volume and the presence or absence of forest degradation were measured and recorded. To model forest degradation, logistic regression model was utilized and Omnibus test, log-likelihood and pseudo r-square (Cox&Snell and Nagelkerke) coefficients were used to evaluate the model. Results showed that the mean of density and volume of trees were 136.8 tree and 239.9 m3/ha, respectively. In addition, the results indicated that 46.82% of the study area was degraded. The results of correlation test showed that there was a srtong negative correlation between quantitative variables and the forest degradation. The independent variables of density and volume of trees were respectively explained 61.6 to 82.3% of the variance of the dependent variable (forest degradation). Among the input variables of the regression model, the effects of density and volume were significant on the forest degradation and it was possible to predict the changes of dependent variables (presence or absence of forest degradation). [ABSTRACT FROM AUTHOR]

In a manufacturing system, lot sizing and maintenance are interdependent and interact with each other. Few studies jointly investigated production lot sizing and maintenance management considering system degradation. However, during the production process, the system and critical component performance will undergo inevitable degradation over time. For example, equipment wears out due to both its own internal causes and the external environment. To monitor the degradation process, interval inspection is usually performed to obtain information about the system degradation and nonlinear degradation is more general. Thus, based on the nonlinear degradation of the production system, this study developed a joint optimization model of production lot sizing and preventive maintenance (PM) thresholds with the goal of maximizing profit per unit of time. The maintenance decision follows the control limit principle, i.e., the choice between preventive maintenance (PM), corrective maintenance (CM), or neither (do nothing) is based on the magnitude of degradation. A simulation algorithm is proposed to obtain the optimal lot-sizing allocation and PM thresholds. The effectiveness of this joint optimization model algorithm is illustrated by numerical examples and the results show that the maximum profit per unit time can be obtained by reasonably formulating PM thresholds and production lot sizing. [ABSTRACT FROM AUTHOR]

Abstract Environmental DNA (eDNA) analysis methods have been developed to detect organism distribution and abundance/biomass in various environments. eDNA degradation is critical for eDNA evaluation. However, the dynamics and mechanisms of eDNA degradation are largely unknown, especially when considering different eDNA sources, for example, cells and fragmental DNA. We experimentally evaluated the degradation rates of eDNA derived from multiple sources, including fragmental DNA (internal PCR control [IPC]), free cells (from Oncorhynchus kisutch), and resident species. We conducted the experiment with pond and seawater to evaluate the differences between freshwater and marine habitats. We quantified the eDNA copies of free cells, fragmental DNA, and resident species (Cyprinus carpio in the pond and Trachurus japonicus in the sea). We found that eDNA derived from both cells and fragmental DNA decreased exponentially in both the sea and pond samples. The degradation of eDNA from resident species showed similar behavior to the cell‐derived eDNA. We evaluated three degradation models with different assumptions and degradation steps and found that a simple exponential model was effective in most cases. Our findings on cell‐ and fragmental DNA‐derived eDNA provide fundamental information about the eDNA degradation process and can be applied to quantify eDNA behavior in natural environments.

Prognostics and system health management (PHM) has attracted increasing attention from both scholars and engineers with interests in improving the reliability, availability, and profitability of industrial systems. Recognized as the two prominent challenges in the prognostics of complicated degrading systems, temporal nonlinearity and stochastic dynamics have incentivized numerous research on nonlinear degradation modeling-based prognostic approaches such as diffusion-process-based models. Comparatively, much less research on how to incorporate the corresponding prognosis information into the decision making on health management has been conducted. In this paper, an optimal condition-based maintenance (CBM) policy for stochastic degrading systems characterized by a diffusion-process-based model has been presented. The CBM policy is firstly converted into a Markovian decision process (MDP) based on the expected discounted cost function (EDCF) within the infinite horizon. Then, the structural properties of the optimal CBM policy have been thoroughly investigated, and the value-based iteration algorithm to solve the optimal CBM problem is proposed. It is proved that the optimal CBM policy for a periodically inspected nonlinear degrading system governed by a diffusion process is a control limit policy, which neither increases in the degradation state nor decreases in the age of the system. Finally, the proposed optimal CBM policy has been illustrated and validated by a case study on the gyroscopes. • An optimal CBM policy for a kind of nonlinear stochastic degrading systems is proposed. • The structural properties of the optimal CBM policy have been thoroughly investigated. • A control limit policy has been proved as the optimal CBM policy. • A value-based iteration algorithm to solve the optimal CBM problem is presented. [ABSTRACT FROM AUTHOR]

Proton-exchange membrane fuel cells (PEMFCs) are regarded as promising alternatives to internal combustion engines (ICEs) to reduce pollution. Recent research on PEMFCs focuses on achieving higher power densities, reducing the refueling time, mitigating the final price, and decreasing the degradations, to facilitate the commercialization of hydrogen mobility. The design of bipolar plates and compression kits, in addition to their coating, can effectively improve performance, increase durability, and support water/thermal management. Past reviews usually focused on the specific aspect, which can hardly provide readers with a complete picture of the key challenges facing and advances in the long-term performance of PEMFCs. This paper aims to deliver a comprehensive source to review, from both experimental, analytical and numerical viewpoints, design challenges, degradation modeling, protective coatings for bipolar plates, and key operational challenges facing and solutions to the stack to prevent contamination. The significant research gaps in the long-term performance of PEMFCs are identified as (1) improved bipolar-plate design and coating, (2) the optimization of the design of sealing and compression kits to reduce mechanical stresses, and (3) stack degradation regarding fuel contamination and dynamic operation. [ABSTRACT FROM AUTHOR]

Electronic packaging solder joints are the key parts of mechanical fixation and electrical interconnection between electronic chips and printed circuit boards, which are prone to failure and lead to electronic device failures under the action of vibration environment stress. In regard to the failure characterization and degradation modeling of electronic packaging solder joints under vibration load, this paper adopts environmental stress tests, builds a vibration failure test platform, designs a vibration load excitation spectrum, and obtains solder joint degradation data under vibration stress; it uses the square root amplitude, form factors, and kurtosis factors to characterize the solder joint degradation process, which effectively identify the solder joint degradation node, and improve the data monotonicity of the solder joint degradation process; the Wiener process is used to build a solder joint multi-stage degradation model. Based on the EM algorithm, the hyperparameters of the degradation model have been optimized, and the universal test of the Wiener degradation model with multiple samples is carried out in accordance with the LB index. The analysis shows that the effectiveness of different samples is as high as 87.5%, which verifies the universality of the Wiener degradation model; Based on the crack morphology subject to the solder joint test and the features of the solder joint in the multi-stages, the paper analyzes the crack propagation behavior of the solder joint, and clarifies the failure mechanism of the solder joint.

Electrification in the transportation industry is becoming more important to face global warming and replace fossil fuels in the future. Among the available energy sources Li-ion battery and proton exchange membrane fuel cell (PEMFC) are the most promising energy sources. Therefore, employing them in fuel cell hybrid electric vehicles (FCHEVs) to combine their advantages is one of the favorable solutions. However, they still face a major challenge residing in their aging that cause the drop of system performance. On one hand, the degradation is the result of the interaction between several aging mechanisms that react differently with various operating conditions. On the other hand, a hybrid system requires an essential energy management strategy (EMS) for fuel economy and optimal power share. At the end, this EMS has an important impact on the lifetime of sources in term of reducing or favorizing the degradation. Therefore, it is important to consider the degradation in the objectives of the designed EMS. Since the degradation is usually neglected when designing an EMS, this paper tends to review the possible methods for designing a health-conscious EMS. Hence, this paper presents a summary of the main fuel cell (FC) and Li-ion battery aging mechanisms as well as the useful degradation models for state of health estimation. In addition, the existing works that consider the degradation of on-board energy sources in their approaches for increasing their durability are classified and analysed. Remaining challenges are detailed along with a discussion and outlooks about current and future trends of health-conscious EMS.