Performance Degradation Model Research Articles

Performance degradation model and life prediction method of spool pair caused by erosion wear

The erosion of solid particles in the fluid causes the wear of the metering edge of the spool pair, resulting in a decrease in system performance. This paper aims to develop a general method to evaluate performance degradation and life prediction of spool pairs induced by erosion. Considering the diversity of influencing factors and the complexity of operating conditions, traditional numerical calculation methods will consume huge computational costs. It is difficult to qualitatively and quantitatively analyze the evolution of metering edge by numerical calculation. The erosion prediction model (EPM) constructed by machine learning methods can be used to predict the wear profile discretized in time and space. Combined with the mathematical model of the valve-controlled cylinder, a performance degradation model (PDM) that can simulate real-time working conditions is established. Through the data exchange and iterative update between EPM and PDM, a complete digital platform for the calculation of slide valve durability is constructed. After comparing with the experimental results, it can be concluded that the proposed method can realize the performance degradation evaluation and life prediction of the spool pair caused by erosion wear under continuous working conditions.

Degradation and polarization curve prediction of proton exchange membrane fuel cells: An interpretable model perspective

Balancing the performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) is a significant challenge. Current data-driven technologies mainly focus on improving the accuracy of performance degradation prediction. However, these methods are often considered as black-box models, where the internal workings of the model are in an unknown state, making it challenging to explain the relationship of each feature with performance/durability. To address this challenge, a novel interpretable model for PEMFC performance degradation and polarization curve is proposed, incorporating feature selection interpretation and black-box model interpretation. The key innovation of this paper lies in transforming the black-box model into a dual interpretable transparent model both before and during the learning process. Additionally, the prediction and interpretation of PEMFC polarization curves are performed. The Boruta algorithm is employed to interpret the significance of each feature's impact on the model, filtering out key feature variables. Based on the Boruta-selected feature variables, the SHapley Additive exPlanations (SHAP) algorithm is used to calculate the marginal contribution of each numerical value to the predicted target. This provides an in-depth explanation of the internal workings of the model, analyzing how each predictive factor influences the model results. Through Boruta feature analysis, it is discovered that the cathode characteristics of PEMFC have a greater impact on performance degradation than anode characteristics. The SHAP algorithm interpretative analysis identifies the optimal flow rates corresponding to the best performance for the cathode hydrogen flow rate and anode oxygen flow rate as 0.090 to 0.13 NLPM and 0.39 to 0.50 NLPM, respectively. This indicates that the algorithmic framework has a certain physical meaning in explaining PEMFC performance degradation.

Reliability evaluation of spacecraft power generation performance with competitive failure processes under irradiation

AbstractThe performance of space power systems is crucial for space products as it determines the operational capabilities, endurance, and efficiency of satellites, spacecraft, and other extraterrestrial devices. Unlike reliability analysis in aerospace systems, studying spacecraft power generation performance requires consideration of both hardware and software aspects. Existing failure models do not fully capture the self‐recovery process of control programs. Therefore, this study presents an impact degradation model for space power systems that incorporates competitive failures under irradiation conditions. The model analyzes solar arrays and power controllers to derive a performance degradation model by considering the defect formation mechanism of amorphous semiconductor materials. Additionally, two shock types are defined based on redundancy backup in power controllers and scrubbing frequency in field‐programmable gate array (FPGA) units. Within the case analysis section, the research meticulously investigates and elucidates the correlation probabilities among varying proton irradiation doses, scrubbing frequencies, and the aforementioned shock types.

Data-driven integrated study on operational performance degradation detection and recovery control of VFDR

The Variable Flow Ducted Rocket (VFDR) serves as the preferred propulsion system for hypersonic aircraft. However, it is prone to health degradation issues due to component failures, aging, and changes in operational conditions, which significantly challenge flight safety. This study introduces a data-driven integrated control approach for modeling, diagnosing, and remedying performance deterioration. Initially, a method for open-set modeling of performance degradation is developed using the Linear Parameter-Varying (LPV) modeling technique and gap metric theory, tackling the hurdles of data collection, overlapping fault classes, and modeling of unidentified faults in the VFDR system; thus, providing vital data support for detecting performance degradation. Moreover, employing the LPV model for performance degradation, a hybrid deep neural network combining multi-scale CNN and LSTM is trained to diagnose system performance issues online using multi-dimensional time-domain sequential data. Subsequently, a smooth transition control method utilizing sliding mode compensation is applied to regain control of system performance based on diagnostic outcomes and a pre-established VFDR performance recuperation controller. Simulations of three types of performance deterioration scenarios caused by changes in actuators, sensors, and the system's operational environment are conducted to demonstrate the efficiency of the proposed approach. The simulation outcomes affirm that the proposed approach is capable of effectively modeling system performance deterioration, diagnosing faults, and controlling recovery, offering promising technical support for the secure operation of intricate closed-loop control systems with stringent precision and reliability requirements.

Study on the calcium dissolution of self-compacting recycled concrete

Considering the effects of the replacement rate of recycled coarse aggregate (RCA), content of fly ash (FA), and solution temperature, the accelerated calcium dissolution test of self-compacting recycled concrete (SCRC) was simulated by using ammonium chloride solution. The results show that the physical properties (the mass loss ratio and the dissolution depth) and the strength degradation of SCRC increase with the increase of the replacement rate of RCA and solution temperature. With the increasing content of FA, the physical properties decrease approximately linearly, while the strength degradation first decreases and then increases. For the SCRC with 50% RCA and a dissolution age of 56 days, the dissolution depth of SCRC with the FA contents of 10%, 20%, and 30% decreased by 3.85%, 10.00%, and 15.56% respectively compared with that of SCRC without FA, and the degradation rate of compressive strength also decreased by 11.60%, 25.91%, and 15.01%, respectively. The peak stress of the stress-strain curve of SCRC under axial compression is negatively correlated with the dissolution age, replacement rate of RCA, and solution temperature, while the peak strain is positively correlated with these variables. When the dissolution age is more than 28 days, the calcium silicate hydrate (C-S-H) gels produced by low-volume (10%, 20%) FA after complete hydration lead to an increase in peak stress and a decrease in peak strain of SCRC, while high-volume (30%) FA has the opposite effect due to incomplete hydration. Finally, the performance degradation model of SCRC under calcium dissolution was established.

Performance Degradation Modeling and Continuous Worktime Assessment of Ultrasonic Vibration Systems

In order to assess the stable operating duration of an ultrasonic vibration system, a reliability-based analysis method for the stability of the ultrasonic vibration system is proposed. Firstly, the failure mechanisms of the ultrasonic vibration system are analyzed, and the resonant frequency and amplitude are selected as two degradation features of the system. Subsequently, accelerated degradation experiments under different force loads were conducted, and the degradation model of the ultrasonic vibration system was established by comparing experimental data with degradation, distribution, and acceleration models. Finally, Copula functions were introduced to connect the two degradation features, resonant frequency, and amplitude, and lifetime curves were plotted under the influence of univariate and bivariate degradation factors. Through the analysis of the lifetime curves, the conclusion is drawn that the decay of amplitude is the primary indicator of system lifetime, and it is predicted that the developed ultrasonic vibration system can operate continuously and stably for 26.69 h. This research is of great significance for enhancing the reliability and lifespan management of ultrasonic vibration systems.

Accelerated Degradation Reliability Modeling Based on Multiple Uncertainty Wiener Processes

Numerous scholars' studies usually adopt the Wiener process to establish industrial robot performance degradation models and randomize the drift coefficients to characterize the differences in individual degradation rates, but they do not take into account the influence of measurement errors on the accuracy of the models. Aiming at the above problems, combining the Wiener process and the accelerated degradation model, the accelerated degradation reliability model of industrial robots based on the multi-uncertainty Wiener process is established, and by adding the factor of measurement error, the model is able to more accurately characterize the influence of external factors on the degradation process of industrial robots. Bayesian theory, Markov chain Monte Carlo method and Bootstrap method are used to estimate the unknown parameters in the model. Finally, the accuracy of the proposed parameter estimation method and model is verified by simulating the industrial robot performance degradation data with Monte Carlo method.

Numerical analysis of PEMFC stack performance degradation using an empirical approach

This paper proposes a performance degradation model based on empirical approaches, which can break down the power loss and quantify the influence of each aging phenomena from the overall performance degradation. With the presence of primary degradation mechanisms, the approach can be used to analyze the performance degradation of fuel cells under different degradation conditions. The aging parameters that represent hydrogen crossover, membrane resistance increase, and electrochemical active surface area (ECSA) loss are introduced to characterize the performance degradation. Based on the polarization behavior of proton exchange membrane fuel cells (PEMFCs), the variations of degradation stressors are determinated by fitting the aging parameters to the measuring polarization curves. The empirical approach is validated by a 150 h durability experiment and agrees well with all polarization curves. The results show that ECSA loss always maintains an apparent influence and hydrogen crossover accounts for 56% of the performance degradation in OCV.

Wheel/rail-force–based maintenance interval extension of the C80 series wagon

PurposeThis study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.Design/methodology/approachChinese wagons' existing maintenance strategy had left a certain safety margin for the characteristics of widely running range, unstable service environment and submission to transportation organization requirements. To reduce maintenance costs, China railway (CR) has attempted to extend the maintenance interval since 2020. The maintenance cycle of C80 series heavy haul wagons is extended by three months (no stable routing) or 50,000 km (regular routing). However, in the meantime, the alarming rate of the running state, a key index to reflect the severe degree of hunting stability, by the train performance detection system (TPDS) for the C80 series heavy haul wagons has increased significantly.FindingsThe present paper addresses a big data statistical way to evaluate the risk of allowing the C80 series heavy haul wagons to remain in operation longer than stipulated by the maintenance interval initial set. Through the maintenance and wayside-detector data, which is divided into three stages, the extension period (three months), the current maintenance period and the previous maintenance period, this method reveals the alarming rate of hunting was correlated with maintenance interval. The maintainability of wagons will be achieved by utilizing wagon performance degradation modeling with the state of the wheelset and the often-contact side bearing. This paper also proposes a statistical model to return to the average safety level of the previous maintenance period's baseline through correct alarming thresholds for unplanned corrective maintenance.Originality/valueThe paper proposes an approach to reduce safety risk due to maintenance interval extension by effective maintenance program. The results are expected to help the railway company make the optimal solution to balance safety and the economy.

Life Cycle Cost Method for Safe and Effective Infrastructure Asset Management

Existing maintenance management systems implement periodic inspections and diagnoses and perform maintenance to restore damaged facilities, making it difficult to establish a long-term and analytical budget plan. The Framework Act on Sustainable Infrastructure Management necessitates specific implementation plans for new implementation items. This study proposes a detailed method for estimating infrastructure management cost to overcome the limitations of the post-response maintenance system and establish a management plan for the Framework Act on Sustainable Infrastructure Management, considering the performance and cost effects in terms of the life cycle. The method was classified into the following stages: analysis of the performance degradation timing by deriving the performance degradation curve, analysis of proper construction methods by performance grade to establish a cost model for each member grade, representative life assessment of the establishment to determine the end-of-life of members, and analysis of optimal action timing for establishing short/mid- to long-term repair and reinforcement plans. The proposed method was applied to a water reservoir (99 reservoirs in Seoul, Korea). The performance degradation and cost prediction models for the target establishment were analyzed. The proposed method can be applied to the maintenance decision making of the management agency and is significant for efficient infrastructure maintenance.

Remaining useful life prediction of mechanical system based on improved adaptive fractional Lévy stable motion with statistical dependence measurement error

Prediction of remaining useful life (RUL) is a critical component of prognostics and health management (PHM) strategies for mechanical systems. Although some degradation models based on stochastic processes have played a pivotal role in the field of RUL prediction, the diffusion coefficients associated with these models are often fixed values that remain independent of the drift coefficient. Additionally, the measurement error is typically assumed to follow a Gaussian distribution that is independently and identically distributed with respect to the level of degradation. To address these constraints, an RUL prediction framework is proposed based on improved adaptive fractional Lévy stable motion (IAFLSM) with statistical dependence measurement error. The established IAFLSM model is capable of effectively characterizing the individual differences and time-varying uncertainties inherent in equipment degradation processes, with the nonlinear drift and diffusion coefficients exhibiting positive correlation in their variability. Moreover, the state space model is employed to realize the synchronous adaptive dynamic update of the drift coefficient and diffusion coefficient, thus accommodating the mechanical equipment degradation trajectory. Furthermore, a statistical dependence measurement error based on fractional Lévy stable motion is constructed, and the corresponding scale parameter incorporates a dynamic update mechanism with statistical correlation of degradation incremental behavior. The hidden variables related to performance degradation model are estimated through parameter estimation method and characteristic function. Expanding upon the proposed framework for RUL prediction, the quantification of the uncertainty intrinsic in the forecasting results is accomplished by employing the stability theorem of stable distribution and Monte Carlo technique. The RUL prediction framework is validated through the use of authentic truck rear axle full-life data and benchmark rolling bearing data. The comparative analysis results demonstrate the effectiveness and superiority of the proposed methodology.

Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process

To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction.

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cells (PEMFCs) are an alternative power source for automobiles that are capable of being cleaner and emission-free. As of yet, long-term durability is a core issue to be resolved for the mass production of hydrogen fuel cell vehicles that requires varied research in the range from sustainable materials to the optimal operating strategy. The capacity to accurately estimate performance degradation is critical for developing reliable and durable PEMFCs. This review investigates various PEMFC performance degradation modeling techniques, such as model-based, data-driven, and hybrid models. The pros and cons of each approach are explored, as well as the challenges in adequately predicting performance degradation. Physics-based models are capable of simulating the physical and electrochemical processes which occur in fuel cell components. However, these models tend to be computationally demanding and can vary in terms of parameters between different studies. On the other hand, data-driven models provide rapid and accurate predictions based on historical data, but they may struggle to generalize effectively to new operating conditions or scenarios. Hybrid prediction approaches combine the strengths of both types of models, offering improved accuracy but also introducing increased computational complexity to the calculations. The review closes with recommendations for future research in this area, highlighting the need for more extensive and accurate prediction models to increase the reliability and durability of PEMFCs for fuel cell electric vehicles.

New Shapeness Property and Its Convex Optimization Model for Interpretable Machine Degradation Modeling

Performance degradation modeling is promising to construct an advanced health index (HI). Currently, domain knowledge including monotonicity, trendabilty, and identifiability has been widely recognized as desirable properties to evaluate the suitability of an HI. Nevertheless, a quantitative criterion to evaluate the suitability of the curvature of an HI is still lacked, which can be used to describe different machine degradation rates. In this article, a new property of an HI named “shapeness” is proposed and it expects that an HI would have a monotonic degradation rate when the HI has a monotonic curve. Subsequently, a mathematical quantitative formulation and optimization model of the shapeness is put forward to be integrated with other desirable properties, which can formulate a composite optimization degradation model. Here, the main variables of the proposed optimization degradation model are weights that are used to fuse spectra lines of vibration signals so that the sum of weighted spectra lines can be used to form a generalized HI. Besides, it is proved that the proposed optimization model is a convex optimization problem so that a global optimal solution is uniquely guaranteed. Two run-to-failure case studies are conducted and some comparisons with state-of-the-art models show that the proposed HI has better performance.

Probability-Based Performance Degradation Model and Constitutive Model for the Buckling Behavior of Corroded Steel Bars

The corrosion of steel bars significantly influences the safety running of concrete bridges. The current bridge design code always ignores the changes in mechanical properties of the materials during their service life which results in an overestimation of the seismic capacity of concrete bridges Therefore, a hysteretic constitutive model that considers the influence of reinforcement corrosion is necessary. This study established a probability-based model for the mechanical property degradation of corroded steel bars using statistical analysis to estimate the mechanical properties under specific corrosion rates. The validity of the proposed degradation model was verified by a series of monotonic tensile tests on corroded steel bars. Moreover, a constitutive model of corroded steel bars based on the Reinforcing Steel Model in OpenSees was obtained. This model considers the influence of corrosion on the slenderness ratio, yield strength, ultimate strength, and elongation. A comparison with the experimental data demonstrated the accuracy of the proposed model. The proposed model is straightforward because its parameters can be predicted using existing research data. The proposed model can provide a basis for the seismic performance evaluation of corroded reinforced concrete (RC) members.

A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis

Condition monitoring and fault diagnosis play an important role in the safety and reliability of aero-engine. Digital twin (DT) technology, which can realize the fusion of physical space and virtual space, has significant advantages over previous researches that only focus on physical mechanisms or big data. In this paper, a novel DT approach based on deep multimodal information fusion (MIF) is proposed, which integrates information from the physical-based model (PBM) and the data-driven model. Two deep Boltzmann machines (DBMs) are constructed for feature extraction from sensor data and nonlinear component-level model simulation data, respectively. Whereby information from these two modalities is mapped into a high-dimensional space and forms a joint representation, and then combined with a multi-layer feedforward neural network to form the MIF model for real-time fault detection and isolation. In addition, an adaptive correction model for performance degradation is constructed by additionally analyzing the probability distribution of engine operation data. Compared with the traditional single-modality method, the proposed DT approach fuses the information of two key modalities and realizes the adaptive updating of the PBM model. The experimental results indicate that the proposed DT approach improves the accuracy of fault diagnosis and reduces the error of parameter prediction.

Capacity Credit Variations of Photovoltaic Power Generation During its Life Cycle

At present, the evaluation results of capacity credit of photovoltaic (PV) power generation are mainly static fixed values that are independent of time. Considering the degradation of the performance of the PV module, the capacity credit should be a time-varying value rather than a static value during the life cycle of the PV power plant. In this paper, the annual performance degradation model of a PV power station is established, and the time-varying capacity credit in the life cycle of the PV plant is simulated by calculating the year-by-year capacity credit. By adding a 100MW PV system to the IEEE-RTS 79 system for a case study, the results are as follows: the PV capacity credit is 46.04% without considering module performance decay, 45.36% in the first year when degradation is considered, and only 31.96% at the end of the life cycle. The average capacity credit over the life cycle is about 38.38%. It shows that the capacity credit varies greatly during the life cycle of PV modules, and cannot be ignored in the evaluation of capacity credit.

Performance Degradation Modeling and Its Prediction Algorithm of an IGBT Gate Oxide Layer Based on a CNN-LSTM Network.

The problem of health status prediction of insulated-gate bipolar transistors (IGBTs) has gained significant attention in the field of health management of power electronic equipment. The performance degradation of the IGBT gate oxide layer is one of the most important failure modes. In view of failure mechanism analysis and the easy implementation of monitoring circuits, this paper selects the gate leakage current of an IGBT as the precursor parameter of gate oxide degradation, and uses time domain characteristic analysis, gray correlation degree, Mahalanobis distance, Kalman filter, and other methods to carry out feature selection and fusion. Finally, it obtains a health indicator, characterizing the degradation of IGBT gate oxide. The degradation prediction model of the IGBT gate oxide layer is constructed by the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) Network, which show the highest fitting accuracy compared with Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and CNN-LSTM models in our experiment. The extraction of the health indicator and the construction and verification of the degradation prediction model are carried out on the dataset released by the NASA-Ames Laboratory, and the average absolute error of performance degradation prediction is as low as 0.0216. These results show the feasibility of the gate leakage current as a precursor parameter of IGBT gate oxide layer failure, as well as the accuracy and reliability of the CNN-LSTM prediction model.

Study on Wear Debris Distribution and Performance Degradation in Low Frequency Fretting Wear of Electrical Connector

Aiming at the problem of the deterioration of the contact performance caused by the wear debris generated during the fretting wear of the electrical connector, low-frequency fretting wear experiments were carried out on the contacts of electrical connectors, the accumulation and distribution of the wear debris were detected by the electrical capacitance tomography technology; the influence of fretting cycles, vibration direction, vibration frequency and vibration amplitude on the accumulation and distribution of wear debris were analyzed; the correlation between characteristic value of wear debris and contact resistance value was studied, and a performance degradation model based on the accumulation and distribution of wear debris was built. The results show that fretting wear and performance degradation are the most serious in axial vibration; the characteristic value of wear debris and contact resistance are positively correlated with the fretting cycles, vibration frequency and vibration amplitude; there is a strong correlation between the sum of characteristic value of wear debris and the contact resistance value; the prediction error of ABC-SVR model of fretting wear performance degradation of electrical connectors constructed by the characteristic value of wear debris is less than 6%. Therefore, the characteristic value of wear debris in contact subareas can quantitatively describe the degree of fretting wear and the process of performance degradation.

A stochastic convolution kernel transform-based model for evaluating bearing performance degradation

Bearing performance degradation assessment can effectively reduce the accuracy degradation and failure of robotic arms caused by bearing failures. Existing performance degradation models based on deep learning and machine learning models have not yet considered the enhancement of degradation assessment by fault-related decoupling in the signal. Therefore, this paper proposes a random convolution kernel transform-based bearing performance degradation assessment model, which enriches the characterization of bearing degradation trends by decomposing the VMD signal and extracting multi-dimensional sensitive features from the decomposed IMFs. The multi-scale characterization of bearing degradation is enriched by a large number of stochastic convolution kernel transforms. A ridge regression classifier is applied to balance accuracy and computational complexity to achieve an intelligent assessment of bearing degradation trends, and the effectiveness of the proposed method is verified on the XJTU-SY dataset, where the accuracy of the proposed method exceeds that of existing fault degradation trend assessment models.