Degradation Model Research Articles

A remaining useful life prediction method of aluminum electrolytic capacitor with adaptive degradation model selection

The degradation process of aluminum electrolytic capacitors(AECs) usually exhibits characteristics such as non-linearity and multi-stage. These degradation features lead to the difficulty to accurately predict the remaining useful life(RUL) of the whole degradation process of AECs. To address this, by dividing the capacitor degradation process into two stages, a two-stage RUL prediction method for AECs considering multiple degradation models is proposed in this paper.In the offline parameter estimation phase, the initial degradation model parameters of two stages are estimated using two-step maximum likelihood estimation combined with particle swarm optimization(PSO) algorithm. In the dynamic parameter updating phase, a sequential Bayesian method is used to update the model parameters. To select the optimal degradation model, an evaluation method based on historical RUL similarity is proposed to calculate the fitness of each model. Finally, the effectiveness of the method is verified on NASA’s accelerated degradation data set and several widely used methods are used for comparison. The experimental results show that the proposed method has higher accuracy, which proves the superiority of the method.

Spare Parts Management Strategy of High-Speed Railway Running Department Based on Performance Prediction

Spare parts management is a critical aspect of high-speed train health management, playing a vital role in maximizing in-service time and minimizing maintenance costs. However, traditional spare parts management methods, which rely solely on historical experience and suggest spare parts quantities or ratios in equipment manuals, often lack practicality and fail to meet real-world demands. To address these limitations, this paper proposes a performance prediction-based spare parts management strategy for high-speed trains. The strategy comprises three main components. First, a performance degradation model is developed using performance evaluation results to define a performance degradation envelope. Next, the required quantity or ratio of spare parts for multiple devices in different performance states is determined using the expected performance score method. Finally, the timing of spare parts orders is scientifically optimized by accounting for production and transportation lead times. To demonstrate the effectiveness of the proposed strategy, we conducted experiments using the spare parts management of a specific high-speed train running gear as a case study and compared it with existing spare parts management methods.

A Deep Unfolding Network for Multispectral and Hyperspectral Image Fusion

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/HS fusion method is a representative deep learning paradigm due to its excellent performance and sufficient interpretability. However, existing deep unfolding-based MS/HS fusion methods only rely on a fixed linear degradation model, which focuses on modeling the relationships between HRHS and HRMS, as well as HRHS and LRHS. In this paper, we break free from this observation model framework and propose a new observation model. Firstly, the proposed observation model is built based on the convolutional sparse coding (CSC) technique, and then a proximal gradient algorithm is designed to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as MHF-CSCNet, where the proximal operators are learned using convolutional neural networks. Finally, all trainable parameters can be automatically learned end-to-end from the training pairs. Experimental evaluations conducted on various benchmark datasets demonstrate the superiority of our method both quantitatively and qualitatively compared to other state-of-the-art methods.

Model uncertainty quantification of a degradation model of miter gates using normalizing flow-based likelihood-free inference

Degradation modeling is essential for failure prognostics of miter gates and subsequent risk-informed maintenance or operational decision-making. Typically, degradation models are formulated based on certain assumptions and simplifications and may not fully capture the complexity of underlying degradation mechanisms. Even minor errors in such models may lead to significant discrepancies in failure prognostics due to error accumulation over time. Aiming to improve the accuracy of the degradation model and ensure reliable failure prognostics, this article proposes a degradation model updating framework for miter gates using normalizing flow-based likelihood-free inference, accounting for both model parameter uncertainty and model form uncertainty (i.e., model bias). The proposed work consists of two phases: the offline training phase and the online updating phase. During the offline training phase, the unknown model bias term is modeled as a random noise with uncertain standard deviation. Synthetic data are then generated using a physical model based on prior knowledge of the uncertain model parameters and model bias. The synthetic data are utilized to train an inference model, which has a summary network for data compression and a conditional invertible neural network for parameter estimation. In the online updating phase, the trained inference model uses strain observations to obtain posterior samples. The Gaussian mixture model and dual particle filter techniques are utilized to recursively update posterior samples, thereby improving the estimation accuracy. Subsequently, model bias is inversely estimated using the degradation model, which uses the updated model parameters and the gap length from the inference model. Following that, a regression model is constructed to correct the biases inherent in the simplified degradation model. This process is implemented iteratively over time to perform continuous model updating and failure prognostics. Results of a case study demonstrate the efficacy of the proposed framework in reducing both model parameter uncertainty and recovering model biases and thereby improving the accuracy of failure prognostics of miter gates.

Study on the Degradation Model of Service Performance in Railway Steel–Concrete Composite Beams Considering the Cumulative Fatigue of Steel Beams and Studs Based on Vehicle–Bridge Coupling Theory

The steel–concrete composite beam, as a structural form that combines the advantages of steel and concrete, has been applied in railway engineering. However, with the increase in railway operation time, the degradation pattern of the service performance of steel–concrete composite bridges remains unclear. This paper proposes a method for calculating the long-term service performance of railway steel–concrete composite beams, considering the cumulative fatigue damage of steel beams and studs, based on the vehicle–bridge coupling theory and Miner’s linear cumulative damage criterion. The proposed method is validated using measured data from an in-service steel–concrete composite railway bridge with spans of 40 + 50 + 40 m. The calculated mid-span vertical displacement and the first two natural frequencies of the composite beam deviated from the measured results by only 2.1%, 7.7%, and 9.5%, respectively. The research results can provide a basis for extending the service life of composite beams and preventing the occurrence of safety accidents.

Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg-Based Implants.

Computational models of electrochemical biodegradation of magnesium (Mg)-based implants are uncertain. To quantify the model uncertainty, iterative evaluations are needed. This presents a challenge, especially for complex, multiscale models as is the case here. Approximating high-cost and complex models with easy-to-evaluate surrogate models can reduce the computational burden. However, the application of this approach to complex degradation models remains limited and understudied. This work provides a workflow to quantify different types of uncertainty within biodegradation models. Three surrogate models-Kriging, polynomial chaos expansion, and polynomial chaos Kriging-are compared based on the minimum number of samples required for surrogate model construction, surrogate model accuracy, and computational time. The surrogate models are tested for three computational models representing Mg-based implant biodegradation. Global sensitivity analysis and uncertainty propagation are used to analyze the uncertainties associated with the different models. The findings indicate that Kriging proves effective for calibrating diverse computational models with minimal computational time and cost, while polynomial chaos expansion and polynomial chaos Kriging exhibit greater capability in predicting propagated uncertainties within the computationalmodels.

Parametric identification of coefficients for a model of fatigue stiffness degradation of a composite material

The problem of finding the fatigue characteristics of a composite material based on test results is considered. The results of endurance tests of unidirectional polymer composite materials with different initial stiffness, breaking stress and working cycle stress were used as the initial data. As a mathematical model of stiffness degradation, a nonlinear ordinary differential equation with five unknown parameters is used, reflecting characteristic changes in the properties of the material. It is required to find such parameter values that the solution of the differential equation should describe the available test results with sufficient accuracy. The solution procedure is reduced to the problem of optimizing the objective function, the value of which characterizes the achieved accuracy. As optimization methods, a method simulating the behavior of a flock of moths and a method of sequential reduction of the search set were used. A step-by-step algorithm for finding unknown model parameters is proposed, and numerical results of processing input data containing information on changing the elasticity modulus of the composite material in the course of applying load cycles are presented.

Research on mechanical property degradation model of equipment compartment floor under atmospheric corrosion condition

A mechanical property degradation model of material fatigue life varying with stress level and corrosion time was developed in this study. An acidified NaCl solution was used to accelerate the corrosion of stainless-steel samples, and the relationship between laboratory-accelerated corrosion and atmospheric corrosion was established using corrosion thickness loss parameters. Based on the results of a pre-corroded fatigue test, lgN–S–T surface models and a modified version of Miner’s rule for calculating nonlinear mechanical damage accumulation were proposed. Taking a real part of a high-speed train as an example, critical operating mileages considering material fatigue performance degradation under corrosion conditions were calculated, which were consistent with failure mileages of the census results.

Performance degradation modeling of civil aviation engines based on component characteristic map optimization

In order to provide a theoretical basis for the degradation of the air path performance of civil aviation engines at the unit level, the CFM56-3 engine was used as the research object. First, on the basis of using the characteristic map scaling method to obtain the component characteristic equation, the selection process of the scaling reference point of the fan general characteristic map was optimized, and a surface fitting method of the characteristic map was proposed to construct an engine component-level benchmark performance model that meets specific speed conditions under steady-state conditions. Then, by introducing the fault factor to generate the fault coefficient matrix, the deviation of the engine monitoring parameters as the component efficiency decreases was calculated, and compared with the fingerprint map data of the GE training manual, it was verified that the engine steady-state performance model that integrates the fan characteristic map scaling reference point optimization and the characteristic map surface fitting method has good accuracy and use in the analysis of air path performance degradation.

A review of computational optimization of bone scaffold architecture: Methods, challenges, and perspectives

Abstract The design and optimization of bone scaffolds are critical for the success of bone tissue engineering (BTE) applications. This review paper provides a comprehensive analysis of computational optimization methods for bone scaffold architecture, focusing on the balance between mechanical stability, biological compatibility, and manufacturability. Finite Element Method (FEM), Computational Fluid Dynamics (CFD), and various optimization algorithms have been discussed for their roles in simulating and refining scaffold designs. The integration of multiobjective optimization and topology optimization has been highlighted for developing scaffolds that meet the multifaceted requirements of BTE. Challenges such as the need for consideration of manufacturing constraints, and the incorporation of degradation and bone regeneration models into the optimization process have been identified. The review underscores the potential of advanced computational tools and additive manufacturing techniques in evolving the field of BTE, aiming to improve patient outcomes in bone tissue regeneration. The reliability of current optimization methods is examined, with suggestions for incorporating non-deterministic approaches and in vivo validations to enhance the practical application of optimized scaffolds. The review concludes with a call for further research into artificial intelligence-based methodologies to drive the next generation of scaffold design and optimization.

Remaining useful life prediction method for jointless track circuits based on multivariate feature fusion and nonlinear Wiener process

Abstract Predicting the Remaining Useful Life (RUL) of track circuits is essential to ensure the safe and reliable operation of high-speed railways. In response to the challenges faced by current machine-learning-based RUL prediction methods, which struggle to represent the uncertainty in the probability distribution of RUL predictions, this paper suggests a hybrid-driven method for estimating remaining life. Firstly, the track circuit Health Index (HI) is constructed by feature dimensionality reduction and fusion of the original multivariate monitoring data through Kernel Principal Component Analysis (KPCA) and Autoencoder (AE); Secondly, the degraded state of the rail circuit is modelled using a nonlinear Wiener degradation model. Finally, the principle of First Hitting Time (FHT) is used to derive the Probability Density Function (PDF) of the anticipated RUL. The efficacy and superiority of the approach presented in this paper are validated by experimental research on the track circuit monitoring dataset. The method enhances forecast accuracy and reduces prediction uncertainty, offering robust technical support for track circuit maintenance decision-making.

Synergistic Effect of NiAl-Layered Double Hydroxide and Cu-MOF for the Enhanced Photocatalytic Degradation of Methyl Orange and Antibacterial Properties

This study synthesized NiAl-layered double hydroxide (LDH)/Cu-MOF photocatalyst using a simple impregnation method involving NiAl-LDH and Cu-MOF. The successful synthesis was confirmed through Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), zeta potential measurements, thermogravimetric analysis (TGA), ultraviolet diffuse reflectance spectroscopy (UV-DRS), N2 adsorption at −196 °C, and electrochemical impedance spectroscopy (EIS). Photocatalysts based on NiAl-LDH, Cu-MOF, and NiAl-LDH/Cu-MOF were used to remove methyl orange (MO) dye from contaminated water. The impact of various factors, including pH, dye concentration, and photocatalyst amount, on MO degradation efficiency was assessed. FTIR analysis was conducted both before and after dye degradation. The optimal degradation conditions were a photocatalyst dose of 25 mg and a pH of 3. Kinetic studies indicated that the degradation of MO dye onto NiAl-LDH/Cu-MOF followed a pseudo-first-order and an L–H or Langmuir–Hinshelwood model. The value of R2 = 0.94 confirms the validity of pseudo-first-order and Langmuir–Hinshelwood (L–H) kinetic models for the photocatalytic degradation of MO dye. This study highlights the importance of developing novel photocatalysts with improved degradation efficiency to protect the water environment. Antibacterial activity was also performed with antibacterial sensibility testing by disk diffusion to determine minimal inhibitory and bactericidal concentrations. In short, NiAl-LDH/Cu-MOF can be helpful for various biomedical and industrial applications.

Bearing remaining life prediction method based on ARAD -ELN and multi-stage wiener process

Abstract Stochastic process-based models are extensively utilized in health assessments and Remaining Useful Life (RUL) predictions of bearings. Nevertheless, bearings in actual operation undergo multiple degradation stages, each characterized by its unique trend of degradation. The application of a singular stochastic process for RUL prediction falls short of achieving optimal performance. Consequently, this paper introduces a multi-stage Wiener process-based approach for the prediction of bearings' RUL. Initially, to tackle the challenge of imbalanced sample sizes across different degradation stages of bearings, an ensemble learning-based neural network (ELN) enhanced by ARIMA Residual Anomaly Detection (ARAD) for the identification of bearing degradation stages is proposed. Subsequently, taking into account the temporal, unit-to-unit, and nonlinear variabilities of the degradation process at each stage, a Wiener process-based multi-stage degradation model for bearings is developed. A method for parameter estimation and updating, utilizing Kalman filtering and Maximum Likelihood Estimation (K-M) is introduced. Finally, the proposed model undergoes validation using both simulated data and the XJTU-SY bearing dataset. Experimental results of three RUL predictions show that the proposed method leads the benchmark model with RMSE values of 3.61, 2.92 and 7.24 respectively, affirming that the proposed model can precisely classify equipment degradation stages and predict RUL with high accuracy and stability.

Modeling of Catalyst Degradation in Polymer Electrolyte Membrane Fuel Cells Applied to Three‐Dimensional Computational Fluid Dynamics Simulation

ABSTRACTIn 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.

Degradation Modeling and RUL Prediction of Hot Rolling Work Rolls Based on Improved Wiener Process.

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.

Autophagy-Mediated Cellular Remodeling during Terminal Differentiation of Keratinocytes in the Epidermis and Skin Appendages.

The epidermis of the skin and skin appendages, such as nails, hair and sebaceous glands, depend on a balance of cell proliferation and terminal differentiation in order to fulfill their functions at the interface of the body and the environment. The differentiation of epithelial cells of the skin, commonly referred to as keratinocytes, involves major remodeling processes that generate metabolically inactive cell remnants serving as building blocks of the epidermal stratum corneum, nail plates and hair shafts. Only sebaceous gland differentiation results in cell disintegration and holocrine secretion. A series of studies performed in the past decade have revealed that the lysosome-dependent intracellular degradation mechanism of autophagy is active during keratinocyte differentiation, and the blockade of autophagy significantly alters the properties of the differentiation products. Here, we present a model for the autophagy-mediated degradation of organelles and cytosolic proteins as an important contributor to cellular remodeling in keratinocyte differentiation. The roles of autophagy are discussed in comparison to alternative intracellular degradation mechanisms and in the context of programmed cell death as an integral end point of epithelial differentiation.

Thermal reaction based mesoscale ablation model for phase degradation and pyrolysis of needle-punched composite

Needle-punched composites are highly valued for their exceptional resistance to interlaminar properties, ablation, and design flexibility, making them increasingly popular in aerospace thermal protection systems. This work investigates the mesoscale structural characteristics and thermophysical properties of needle-punched composites in ablation process. Oxyacetylene ablation experiments were carried out at different temperatures, and a mesoscopic needle-punched structure model was established based on the results of CT characterization. Further, Abaqus custom subroutine was used to reveal the ablation evolution mechanism of carbon fiber reinforced phenolic resin-based needle-punched composites. The results show that, at mesoscopic scale, the acicular fiber bundle perpendicular to the ablative surface accelerates the heat conduction to the interior of the material and promotes the thermal damage and performance degradation of the composite.

Prediction of machine state for non-Gaussian degradation model using Hidden Markov Model approach

Machinery health management becomes an essential issue in many sectors. The ultimate goal is to predict machinery degradation and accordingly plan maintenance actions. However, prediction becomes much harder if data is noisy. We propose a procedure for on-line prediction of the forthcoming machine state. This procedure is dedicated to the non-Gaussian (impulsive) health index (HI) data. It is based on a simplified degradation model with three machine states, i.e. healthy, warning and alarm, described in terms of a Hidden Markov Model (HMM). Using simulated trajectories we demonstrate that the α-stable HMM dedicated to time series with impulsive behaviour outperforms the classical Gaussian approach and can be an efficient alternative in such a case. In particular, the percentage errors of the predicted alarm state transition points decrease from 20%−45% to 1%−6%, if the α-stable HMM is used instead of the Gaussian one. We illustrate the proposed methodology for two datasets acquired during experiment on the VIBstand test rig and for a benchmark FEMTO dataset.

A novel performance prediction method for harmonic reducers based on the trend-enhanced Fisher’s discriminant ratio-Wiener process

Abstract Harmonic reducers, as core components of industrial robots, play a critical role in maintaining robot health. Performance degradation assessment (PDA) and remaining useful life (RUL) prediction are essential for ensuring the operational reliability of such robots. To address the multistage characteristics and stochastic uncertainties in the performance degradation of harmonic reducers, a performance prediction method based on Fisher’s discriminant ratio and Wiener process is proposed. Firstly, the health indicator (HI) construction is defined as an optimization problem based on Fisher’s discriminant ratio and trend monotonicity constraints. By leveraging the sum of the multiplication of frequency amplitudes and optimized weights, precise segmentation of the multistage degradation process over the entire lifecycle is achieved. Notably, the optimized weights can automatically identify the resonance frequency bands caused by damage. Subsequently, a nonlinear Wiener process model with a drift coefficient in the form of a power function is established. The probability density function (PDF) expression for RUL is derived based on the concept of first hit time (FHT). Additionally, a logarithmic likelihood function (Log-LF) for the unknown parameters in the degradation model is constructed. The experimental results indicate that the proposed method surpasses the other two Wiener process models in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), and cumulative relative accuracy (CRA). This provides robust support for preventive maintenance decision-making for harmonic reducers.

A Review of the Degradation Research on the Single-Lap Bolted Joint

Bolted joints, with their advantages of simple structure and convenient disassembly and assembly, are widely used in complex equipment fields such as aerospace systems and weaponry. Subject to complex mechanical loads, the contact surfaces may undergo nonlinear behaviors such as contact–separation and viscous slip, leading to the nonlinear degradation of the connection stiffness, which severely threatens the safety and reliability. These have driven research on bolted joints to span multiple disciplines, from interfacial micro-friction to macro-structural dynamics. Therefore, from the field of micro-friction to macro-dynamics, this review summarizes and analyzes three major degradation models, outlines the experimental development in connected structures, and provides an overview of the numerical analysis methods for degradation simulation. This paper also looks forward to the development directions for future research on the degradation of connected structures.