Degradation Data Research Articles

Remaining Useful Life Prediction for Power Storage Electronic Components Based on Fractional Weibull Process and Shock Poisson Model

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy.

Calcium Sulfate Disks for Sustained-Release of Amoxicillin and Moxifloxacin for the Treatment of Osteomyelitis.

The purpose of this in vitro study was to develop calcium sulfate (CS)-based disks infused with an antimicrobial drug, which can be used as a post-surgical treatment modality for osteomyelitis. CS powder was embedded with 10% antibiotic, amoxicillin (AMX) or moxifloxacin (MFX), to form composite disks 11 mm in diameter that were tested for their degradation and antibiotic release profiles. For the disk degradation study portion, the single drug-loaded disks were placed in individual meshes, subsequently submerged in phosphate-buffered saline (PBS), and incubated at 37 °C. The disks were weighed once every seven days and analyzed via Fourier-transform infrared spectroscopy, X-ray diffraction, energy dispersive X-ray spectroscopy, and scanning electron microscopy. During the antibiotic release analysis, composite disks were placed in PBS solution, which was changed every 3 days, and analyzed for antibiotic activity and efficacy. The antibacterial effects of these sustained-release composites were tested by agar diffusion assay using Streptococcus mutans (S. mutans) UA 159 as an indicator strain. The degradation data showed significant increases in the degradation of all disks with the addition of antibiotics. Following PBS incubation, there were significant increases in the amount of phosphate and decreases in the amount of sulfate. The agar diffusion assay demonstrated that the released concentrations of the respective antibiotics from the disks were significantly higher than the minimum inhibitory concentration exhibited against S. mutans over a 2-3-week period. In conclusion, CS-antibiotic composite disks can potentially serve as a resorbable, osteoconductive, and antibacterial therapy in the treatment of bone defects and osteomyelitis.

A multivariate student-t process model for dependent tail-weighted degradation data

Traditionally, the Gaussian assumption, implied by the Wiener process, is widely admitted for modeling degradation processes. However, when degradation data exhibit heavy tails, this assumption is not suitable. To overcome this limitation, this article proposes a novel class of tail-weighted multivariate degradation model, which is built upon Student-t process. The model is able to account for both between-unit variability and process dependency, while allowing the adjustment of tail heaviness through tuning the parameter of the degree of freedom. For reliability assessment, we derive the system reliability function and present an efficient Monte Carlo method for its evaluation. Further, we introduce an expectation–maximization algorithm for parameter estimation and design a bootstrap method for interval estimation. Comprehensive simulation studies are conducted to validate the effectiveness of the inference method. Finally, the proposed methodology is applied to analyze two real-world degradation datasets.

A study on degradation and CO2 capture performance of aqueous amino acid salts for direct air capture applications

AbstractWe have previously proposed amino acid salts solutions as potential absorption liquids for direct air capture (DAC) of CO2 from the atmosphere. However, little is known about their relevant CO2 solubilities, CO2 mass transfer rates, and susceptibility to oxidative and thermal degradation under conditions relevant to DAC. We report here on the overall solubility of CO2 and CO2 mass transfer rates into a series of amino acid salts solutions. Additionally, the robustness of various amino acid salt solutions to thermal and oxidative degradation has been assessed.CO2 absorption rates into the amino acid salts solutions were observed to be in the same order of magnitude as aqueous monoethanolamine (MEA), with sarcosinate and lysinate solutions providing the fastest and slowest CO2 mass transfer rates at 25°C, respectively. Degradation data revealed that all amino acid salt solutions investigated in this study displayed elevated rates of thermal degradation at both 120 and 150°C relative to MEA. The opposite trend was observed with respect to oxidative degradation where all amino acid salt solutions showed a greater resistance to oxidative degradation than that observed for MEA under the conditions investigated here. Considering the degradation, CO2 absorption capacity, and CO2 mass transfer rate data, we propose the potassium salts of proline and sarcosine as the most promising amino acid salts (of those considered here) for further evaluation in DAC processes. Overall, this study provides valuable insight into the suitability of various amino acid salt solutions as absorption liquid for DAC. © 2024 The Author(s). Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

Few-shot reliability evaluation of tribopairs degradation based on active learning supported generative adversarial network

The high reliability of seawater hydraulic components depends on the tribological properties of key tribopairs. It is impractical to conduct a high number of lifetime tests in a short amount of time due to the limitation of the cost and test time constraints in practical engineering applications, which has a significantly impact on the accuracy of reliability evaluation. To solve this, the Generative Adversarial Network (GAN) is provided as a few-shot learning (FSL) technique for expanding the tribopairs degradation data set, which has the potential to generate high-quality samples. Considering the uncertainty and diversity, an active learning approach is proposed to selecting the most effective generated samples. Finally, reliability is finally evaluated based on Wiener process model via using actual data combine with generated data selected from GAN model. Simulations and friction characteristics experiment of polyether ether ketone (PEEK)/17-4PH stainless steel are applied to validate the proposed method, the results show that the proposed method could generate and choose samples that are highly similar to the real samples, the accuracy of reliability evaluation is effectively improved.

An EM‐based likelihood inference for degradation data analysis using gamma process

An EM‐based likelihood inference for degradation data analysis using gamma process

Degradation generation and prediction based on machine learning methods: A comparative study

In engineering practice, degradation analysis often suffers from the small-sample problem. To this end, several generative models are developed to expand the degradation data, based on which both the original data and the synthetic data are used to train neural networks for degradation prediction. However, these methods are rarely compared with each other, and the performances of competitive candidates are not explored. Given this, this paper reviews some machine learning-based methods and performs a comparison among them. Particularly, a segmented sampling method is proposed and the diffusion model is introduced for degradation generation. Results of both numerical simulations and case studies show that none of these methods can perform best in all cases, yet making use of synthetic data improves the predictive performance. Overall, the time-series generative adversarial network and the segmented sampling method are recommended for degradation generation, and the gated recurrent unit network is recommended for prediction.

How to Perform a Statistical Analysis of Non-Destructive Degradation Data to Study Crack Growth in Wind Blades as a Function of the Number of Cycles

Objective: The aim of this paper is to show the application of the Exponential Regression Model to study the failure time of a system over time. The data was taken from the paper (Wang et al., 2021). Theoretical framework: The linear regression model, where the response is associated with the explanatory variables by means of a linear model, is the best known. To formulate the model, it is necessary to specify a deterministic component and a random (stochastic) component. The exponential model should be used when it is assumed that risk is constant over time. Once the model has been specified, its parameters are estimated. In the absence of normal errors and especially in the presence of censoring, a more appropriate option is the maximum likelihood method. As the equations are non-linear in their parameters and do not have an analytical solution, it is necessary to use the Newton-Raphson numerical method. Due to the simplicity of the exponential regression model, few situations in practice are adequately adjusted by this model. The Weibull regression model is widely used in survival analysis (Bastos Lyra et al., 2008) Method: Data taken from the article (Wang et al., 2021) e foi utilizado a Regressão Exponencial para modelar os dados. Final Considerations: This result indicates that Exponential Regression is a very viable option when the aim is to monitor the number of cracks as a function of time to check whether a system will fail. In this specific Case Study, it is clear that one of the blades is better than the others and that there are some Probability Distribution models that are suitable for the model. Implications of the research: The use cases of Exponential Regression are more restricted in the scientific literature and because of this, this case study is interesting to show that this model is effective for treating data in the area of reliability. Originality/value: Despite being a well-known statistical tool, Exponential Regression has a specific application in monitoring the number of cracks over time in a system.

A hybrid reliability assessment method based on health index construction and reliability modeling for rolling bearing

AbstractThe assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings’ reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method.

Rotating machine bearing health prognosis using a data driven approach based on KS‐density and BiLSTM

AbstractRolling element bearings are vital components within rotating machinery, making them a central focus of maintenance in the prognostics and health management sector. This involves closely monitoring their condition to accurately predict the remaining useful life, increasing reliability while minimizing unexpected breakdowns, thereby enabling cost savings through planned maintenance, and enhancing operational stability and security. To achieve this goal, it is necessary to build an online intelligent system for degradation monitoring and failure prognosis by the construction of a robust health indicator and making quantitative measure for bearing degradation. In this paper, an efficient and reliable approach is proposed to estimate the remaining useful life of bearing. A new prediction method is presented by the combination of kernel smoothing density (KS‐density) and bidirectional long short‐term memory (BiLSTM). Firstly, KS‐density smoothens the preliminarily estimated probability distribution function using machinery degradation data. Secondly, the obtained KS‐density is used in feed deep learning technique based on BiLSTM models. On this basis, the variation of the signal distribution models between the current faulty state and the normal conditions state is quantified for bearing health assessment. The effective recognition of bearing degradation by the proposed Weibull‐based health index is demonstrated through experimental validations utilizing run‐to‐failure datasets, provided by the centre for intelligent maintenance systems. The comparison with the literature's review show that the prediction results of the proposed approach are more accurate.

A Markov method for a mechanical system reliability assessment using discrete degradation data

Abstract A homogenous continuous-time Markov model (HCTMM) has the advantage of high accuracy and is usually used in reliability analysis. However, the transition intensities (rates) between any two states cannot be accurately obtained due to the discrete data obtained from a mechanical system and little expert knowledge. This paper proposes a method in which the transition intensity matrix of an HCTMM can be obtained indirectly using the actual discrete degradation data of a mechanical system. Firstly, the one-step transition probability matrix can be calculated by a hidden Markov model using the discrete degradation data. Secondly, the unit-time transition intensity matrix can be calculated from the one-step transition probability matrix. At last, an HCTMM is established to assess the reliability of a mechanical system based on the unit-time transition intensity matrix. Moreover, considering the maintenance activities cannot restore a mechanical system as a fire-new one, the random transition of a mechanical system after maintenance is expressed by a quasi-renewal process in this paper. Finally, three cases are studied to validate the proposed method.

Enhanced Fenton Degradation of Tetracycline over Cerium-Doped MIL88-A/g-C3N4: Catalytic Performance and Mechanism.

Since enormous amounts of antibiotics are consumed daily by millions of patients all over the world, tons of pharmaceutical residuals reach aquatic bodies. Accordingly, our study adopted the Fenton catalytic degradation approach to conquer such detrimental pollutants. (Ce0.33Fe) MIL-88A was fabricated by the hydrothermal method; then, it was supported on the surface of g-C3N4 sheets using the post-synthetic approach to yield a heterogeneous Fenton-like (Ce0.33Fe) MIL-88A/10%g-C3N4 catalyst for degrading the tetracycline hydrochloride drug. The physicochemical characteristics of the catalyst were analyzed using FT-IR, SEM-EDX, XRD, BET, SEM, and XPS. The pH level, the H2O2 concentration, the reaction temperature, the catalyst dose, and the initial TC concentration were all examined as influencing factors of TC degradation efficiency. Approximately 92.44% of the TC was degraded within 100 min under optimal conditions: pH = 7, catalyst dosage = 0.01 g, H2O2 concentration = 100 mg/L, temperature = 25 °C, and TC concentration = 50 mg/L. It is noteworthy that the practical outcomes revealed how the Fenton-like process and adsorption work together. The degradation data were well-inspected by first-order and second-order models to define the reaction rate. The synergistic interaction between the (Ce0.33Fe) MIL-88A/10%g-C3N4 components produces a continuous redox cycle of two active metal species and the electron-rich source of g-C3N4. The quenching test demonstrates that •OH is the primary active species for degrading TC in the H2O2-(Ce0.33Fe) MIL-88A/10%g-C3N4 system. The GC-MS spectrum elucidates the yielded intermediates from degrading the TC molecules.

Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

Abstract Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold

AbstractIn conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment.

Electrospun PAN/PANI/CNT scaffolds and electrical pulses: a pathway to stem cell-derived nerve regeneration

Biocompatible polymer-based scaffolds hold great promise for neural repair, especially when they are coupled with electrostimulation to induce neural differentiation. In this study, a combination of polyacrylonitrile/polyaniline (PAN/PANI) and Carbon Nanotubes (CNTs) were used to fabricate three different biomimetic electrospun scaffolds (samples 1, 2 and 3 containing 0.26 wt%, 1 wt% and 2 wt% of CNTs, respectively). These scaffolds underwent thorough characterization for assessing electroconductivity, tensile strength, wettability, degradability, swelling, XRD, and FTIR data. Notably, scanning electron microscopy (SEM) images revealed a three-dimensional scaffold morphology with aligned fibers ranging from 60 nm to 292 nm in diameter. To comprehensively investigate the impact of electrical stimulation on the nervous differentiation of the stem cells seeded on these scaffolds, cell morphology and adhesion were assessed based on SEM images. Additionally, scaffold biocompatibility was studied through MTT assay. Importantly, Real-Time PCR results indicated the expression of neural markers—Nestin, β-tubulin III, and MAP2—by the cells cultured on these samples. In comparison with the control group, samples 1 and 2 exhibited significant increases in Nestin marker expression, indicating early stages of neuronal differentiation, while β-tubulin III expression was significantly reduced and MAP2 expression remained statistically unchanged. In contrast, sample 3 did not display a statistically significant upturn in Nestin maker expression, while showcasing remarkable increases in the expression of both MAP2 and β-tubulin III, as markers of the end stages of differentiation, leading to postmitotic neurons. These results could be attributed to the higher electroconductivity of S3 compared to other samples. Our findings highlight the biomimetic potential of the prepared scaffolds for neural repair, illustrating their effectiveness in guiding stem cell differentiation toward a neural lineage.

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

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

Multi-task learning mixture density network for interval estimation of the remaining useful life of rolling element bearings

Existing remaining useful life (RUL) predictions of rolling element bearings have the following shortcomings. 1) Model-driven methods typically employ a sole model for processing the data of an individual, making it challenging to accommodate the variety of degradation behaviors and susceptible to abnormal interference. 2) Data-driven methods place greater emphasis on training data, and in reality, it can be challenging to acquire comprehensive data covering the lifecycle. 3) Many studies fail to give adequate attention to the assessment of RUL uncertainty. This paper proposes a multi-task learning mixture density network (MTL-MDN) method to address these issues. Firstly, the peak-of-Histogram (PoHG) is extracted and served as the novel health indicators. Secondly, multi-task learning dictionaries are constructed based on the evolution law of PoHG, thus combining both model-driven and data-driven strategies. Finally, a multi-task learning strategy is proposed with mixture density networks. It effectively accomplishes the collaborative learning objective of numerous degradation samples in the regression problem and accomplishes the uncertainty assessment of RUL. After analyzing the experimental and real-world degradation data of rolling element bearings throughout their lifecycle, and comparing it to other modern RUL prediction methods, it becomes evident that the proposed MTL-MDN method offers superior prediction accuracy and robustness.

Degradation index‐based prediction for remaining useful life using multivariate sensor data

AbstractThe prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy.

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacy-preserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5\% and 39.3\% for state-of-health estimation and remaining useful life estimation, respectively.

Stiffness reliability analysis of harmonic drive considering contact pairs wear

PurposeThe purpose of this paper is to analyze the stiffness reliability of harmonic drive (HD) considering contact pairs wear.Design/methodology/approachIn terms of theoretical calculation, the contact pairs wear of HD are calculated based on Archard wear formula and the relative motion characteristics of contact pairs. According to the motion trajectory of flexspline teeth, the teeth backlash and the number of meshing teeth, the meshing stiffness and overall stiffness of HD are analyzed considering the wear and its randomness of contact pairs. Combined with Monte Carlo Simulation, the stiffness reliability evaluation method considering contact pairs wear is proposed, and the result of this method is verified by the stiffness reliability result deduced from the stiffness degradation measurement data.FindingsConsidering contact pairs wear, during operation, the teeth backlash increases, the number of meshing teeth decreases, the meshing stiffness decreases, ultimately leading to a gradual decrease in the overall stiffness of HD. When only one type of contact pair wear is considered, the influence of flexspline and circular spline contact pair wear on HD stiffness reliability is greater. Compared with the stiffness reliability evaluation results obtained from the stiffness degradation data in the literature, the mathematic expectation of stiffness degradation failure life distribution obtained from the proposed method is relatively bigger.Originality/valueThe stiffness reliability evaluation method of HD considering contact pairs wear is firstly proposed. The stiffness reliability evaluation result from theoretical calculation is verified by the stiffness reliability results deduced from HD stiffness degradation measurement.