Reliability Modeling Approach Research Articles

A Review of Reliability Research in Regional Integrated Energy System: Indicator, Modeling, and Assessment Methods

The increasing complexity of integrated energy systems has made reliability assessment a critical challenge. This paper presents a comprehensive review of reliability assessment in Regional Integrated Energy Systems (RIES), focusing on key aspects such as reliability indicators, modeling approaches, and evaluation techniques. This study highlights the role of renewable energy sources and examines the coupling relationships within RIES. Energy hub models and complex network theory are identified as significant in RIES modeling, while probabilistic load flow analysis shows promise in handling renewable energy uncertainties. This paper also explores the potential of machine learning methods and multi-objective optimization approaches in enhancing system reliability. By proposing an integrated assessment framework, this study addresses this research gap in reliability evaluation under high renewable energy penetration scenarios. The findings contribute to the advancement of reliability assessment methodologies for integrated energy systems, supporting the development of more resilient and sustainable energy infrastructures.

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

Modelling and Design Optimization of a Novel Compliant XY Positioner for Vibration-Assisted CNC Milling

Vibration-assisted machining, known as hybrid processing technology, offers several benefits over conventional machining methods. However, developing mechanical structure designs to generate a non-resonant frequency source remains challenging. The objective of this study is to propose a novel design for an XY flexure positioner by combining the pseudo-rigid-body model with the Lagrange technique, finite element analysis and Crayfish optimization algorithm. Firstly, the mechanism was designed by combining a hybrid amplifier and parallel driving mechanism integrated with right circular hinges to increase the natural frequency and precision for potential application to VAM CNC milling. Then, the analytical model was established by the pseudo-rigid-body and Lagrange method. Next, the theoretical result was verified by finite element analysis. The first natural frequency results of theory and FEM methods were found at 990.74 Hz and 1058.5 Hz, respectively. The error between the two methods was 6.4%, demonstrating a reliable modeling approach. Based on the analytical equations, the Crayfish optimization algorithm was utilized for optimizing the main design variables of the mechanism. Next, the prototype was fabricated. The results showed that the experimental and simulated frequencies were 1127.62 Hz and 1216.6 Hz, with an error between the two methods of 7.31%. Finally, the workpiece was installed on the prototype and a real vibration-assisted CNC milling process was carried out in the frequency range [700 Hz, 1000 Hz]. The best surface roughness of the specimen was achieved at a frequency of 900 Hz with a Ra of 0.287 µm. This demonstrates that the proposed XY mechanism is an effective structure for generating a non-resonant frequency source for vibration-assisted machining.

A new approach for reliability modeling in green closed-loop supply chain design under post-pandemic conditions: A case study

Climate change, pandemics, and economic crises have created complex challenges for supply chains. Managing such situations requires the development of reliable decision-making frameworks. In this paper, a multi-level, multi-product, and multi-period closed-loop supply chain is studied with environmental considerations. A bi-objective mixed-integer linear programming model is presented for facility location, flow allocation, and transportation mode determination. The objectives of the model are to minimize the total cost and maximize the reliability of suppliers to meet the needs of factories. In the area of reliability engineering, a new approach is defined for modeling the probability of supplier availability considering catastrophic failures caused by pandemics, economic sanctions, and other failure modes. Furthermore, the decision-maker can handle the emission of greenhouse gases by an upper-bound constraint. In order to face the simultaneous uncertainty of demand and the maximum CO2 emission allowed, a scenario-based two-stage stochastic programming approach is proposed. The improved version of the augmented ε-constraint method, known as AUGMECON2, is used to solve the proposed model. The efficiency of the model and the proposed solution approach are investigated through a real-world case study of a battery manufacturing company in Iran.

Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

AbstractIdentifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.

Development of a steady state electrothermal cosimulation model of SiC power modules

Silicon carbide power modules are suitable for high temperature, high voltage and high frequency applications. High operating temperature and high power density bring enormous challenges in chip parallel connection and junction temperature predicting. In this work, a steady state electrothermal cosimulation model is established based on Matlab electrical model and ANSYS thermal analysis model. An experimental study is conducted using a customized single die SiC module. The experimental and numerical results exhibit great agreement, with deviation consistently below 4 %. Then, comprehensive research is conducted to investigate the electrothermal performance of several dies in parallel configuration. The findings unequivocally validate the efficacy of the proposed model in accurately capturing the intricate electrical and thermal characteristics. The proposed numerical model offers a reliable and accurate electrothermal modeling approach for SiC power modules.

A Finite Element Modeling Approach for Analyzing the Cyclic Behavior of RC Frames

Numerous approaches and inputs for nonlinear finite element analysis used for modeling reinforced concrete systems under cyclic loads have been outlined. Practitioners usually take into account a number of significant factors when modeling, for example deciding upon the constitutive relations, the connection among concrete and steel reinforcement, and the mesh sensitivities, in order to accurately simulate the nonlinear response of reinforced concrete under cyclic loads. Therefore, it is essential to thoroughly assess popular modeling approaches and develop a reliable modeling approach with reliability, stability, and consistency. A modeling technique and practical guidelines for nonlinear finite element analysis of reinforced concrete frames subjected to cyclic loading are described in this study, taking into account the influence of parameters such as dilation angle, stiffness recovery, friction coefficient, and mesh sizes. The hysteretic force–displacement behavior and load-carrying capacity of reinforced concrete frames subjected to cyclic loading have been compared using numerical and experimental curves. Discrete fracture paths are specified in the model’s geometry to achieve an accurate depiction of the stress distribution and an adequate fit of the hysteretic behavior. The proposed modeling approach provides a precise representation of the stress distribution and hysteretic behavior while keeping computational costs reasonable.

Operational reliability analysis of remote operated vehicle based on dynamic Bayesian network synthesis method

In this study, we propose a reliability framework that combines the GO method with fault tree analysis and integrates human factor reliability analysis. Taking the application of remotely operated vehicle (ROV) in underwater oil and gas production as an example, a reliability model of ROV operation process is established. The human reliability in the ROV operation process is analyzed on the basis of Cognitive Reliability and Error Analysis Method (CREAM) extension approach. The influence of human factor reliability and equipment reliability on the system is compared and analyzed. The importance of each operation step is also compared and analyzed. A sensitivity analysis of the ROV during operation is performed. This study provides a theoretical basis for the improvement and maintenance of the operational reliability of ROV. The proposed reliability modeling approach solves the reliability analysis problem of large and complex operating systems.

Quantifying Bus Accessibility and Mobility for Urban Branches: A Reliability Modeling Approach

In order to quantitatively assess the realization degree of urban branch road function, an evaluation method based on reliability theory was established. Firstly, the main characteristics of urban branch road functions, that is, emphasizing accessibility while maintaining mobility, were analyzed. Based on common road network patterns, the branch road network between adjacent higher-level roads was selected as the research object. Public transportation, which is more representative of accessibility, was considered in the evaluation method. Then, a reliability model for branch road function evaluation was proposed, in which walking distance and travel time by bus were taken as the main indicators. Combining Dijkstra algorithm and Monte Carlo simulation, the model was solved. The feasibility of the method was verified through a case study of two branch road units. Then, influences of road network density, connectivity, and bus route layout on the reliability of branch road unit functions were clarified by sensitivity analysis. Results showed that the reliability of the branch unit exhibited a trend of initially increasing and then decreasing with the growth of road network density and connectivity, which indicated that excessively high road network density may hinder the performance of the branch road unit. Adjusting the layout of bus routes within branch road units and expanding the service area of bus routes can enhance reliability. Finally, some suggestions for optimizing branch road unit reliability were provided.

Model development for numerical analysis of the bonding strength for friction welded lightweight structures

The rotary friction welding (RFW) is a robust, precise, productive and economical joining process that is used in many areas of mechanical engineering to produce lightweight structures consisting of combinations of ferrous and non-ferrous materials, for instance aluminium alloy and steel. Crucial for the design of such lightweight structures is the knowledge about the bonding strength. The bonding strength is the result of the bond formation depending on the present transient kinematic, kinetic and thermal states during the welding process being directly determined by the welding process parameters. Despite several years of empirical research, no reliable numerical modelling approach exists for the RFW process to analyse the bonding strength based on these transient state variables. For this reason, an improvement of the bond formation and therefore an increase in the bonding strength can only be tested experimentally. The main motivation of this paper is to develop an appropriate modelling approach for the estimation of the bonding strength for friction welded lightweight structures manufactured of an aluminium alloy and a structural steel. Therefore, a couple of aluminium alloy and steel welding experiments with different process parameters were performed and subsequently analysed concerning to the resulting bonding strength. Moreover, all the welding experiments were simulated in regard to the corresponding welding process in order to determine the present kinematic, kinetic and thermal state variables, like the strain rate, the stress and the temperature. Thus, a model for the characterization of the bond formation can be developed, which allows a correlation between the bond formation and the resulting bond strength based on the chosen welding process parameters. Finally, the model will be examined and discussed in terms of its plausibility and applicability.

Examining the Effects of Local Sample Sizes on Spatial Transferability of Freight Production Models

Recent research in freight transportation planning has been exploring the spatial transferability of freight demand models, which can help the planning agencies of developing economies save the cost and time incurred in freight surveys. As the demand models are time-, cost-, and data-intensive, it is prudent to analyze the effects of sample size on the transferred model in a region. The findings and inferences from such analysis will save resources in freight data collection programs. Earlier, conventional models like ordinary least squares (OLS) regression were assessed for transferability. However, the predictive ability and transferability of such non-conventional models are not well studied. It is necessary to understand whether the extent of transferability of non-conventional models is greater than that of conventional models so that planning agencies can adopt more reliable modeling approaches. This paper investigates the spatial transferability of freight production models using OLS, robust regression, and multiple classification analysis (MCA). The results of the transferability assessment show that MCA models have better transferability using the naïve transfer method. In addition, transferability is assessed for different sample sizes to examine the variation in the extent of transferability. The MCA models have shown the least deviation, indicating that these models are preferred for transferability when the sample size is small.

Reliability analysis of the multi-state system with nonlinear degradation model under Markov environment

Research conducted on multi-state systems has garnered significant attention as a result of their engineering applications in the aerospace industry. The paper explores the intricacies of multi-state systems operating in complex environments and experiencing degeneration processes. A mixed shock model is introduced, which consists of multiple run shock models and extreme shock models. A state transition matrix is constructed using a Markov chain embedding method to establish a reliability model of hard failure process. In contrast to the traditional reliability modeling approach of hard failure process, this method incorporates time-dependencies. A numerical integration method is employed to derive the reliability function of the soft failure process based on the Gamma process. To validate the effectiveness of the developed methodology, an application of a micro-engine is utilized. Finally, compared with the Monte Carlo simulation, the developed model can be considered as a useful tool for the system manager to assess the reliability of the multi-state system with the nonlinear degradation process.

Neural-fuzzy machine learning approach for the fatigue-creep reliability modeling of SAC305 solder joints

The accuracy of reliability models is one of the most problematic issues that must be considered for the life of electronic assemblies, particularly those used for critical applications. The reliability of electronics is limited by the fatigue life of interconnected solder materials, which is influenced by many factors. This paper provides a method to build a robust machine-learning reliability model to predict the life of solder joints in common applications. The impacts of combined fatigue and creep stresses on solder joints are also investigated in this paper. The common alloy used in solder joint fabrication is SAC305 (Sn–Ag–Cu). The test vehicle includes individual solder joints of SAC305 alloy assembled on a printed circuit board. The effects of testing temperature, stress amplitude, and creep dwell time on the life of solder joints were considered. A two-parameter Weibull distribution was utilized to analyze the fatigue life. Inelastic work and plastic strain were extracted from the stress–strain curves. Then, Artificial Neural Networks (ANNs) were used to build a machine learning model to predict characteristic life obtained from the Weibull analysis. The inelastic work and plastic stains were also considered in the ANN model. Fuzzy logic was used to combine the process parameters and fatigue properties and to construct the final life prediction model. Then a relationship equation between the comprehensive output measure obtained from the fuzzy system and the life was determined using a nonlinear optimizer. The results indicated that increasing the stress level, testing temperature, and creep dwell time decreases reliability. The case of long creep dwell time at elevated temperatures is worst in terms of impact on reliability. Finally, a single robust reliability model was computed as a function of the fatigue properties and process parameters. A significant enhancement of the prediction model was achieved compared to the stress–life equations.

Reliable design of urban surface-underground integrated logistics system network with stochastic demand and social-environmental concern

Transforming urban goods movement from roads to underground is an emerging concept to improve supply chain performance and alleviate overcrowded environments. Existing literature merely discussed the underground logistics system (ULS) network design problem within a simplified boundary, whereas several critical system features (e.g., stochastic freight demand and co-modality) were omitted. To bridge the knowledge gaps, this paper proposes a reliable modeling approach to solve an urban surface-underground integrated logistics network (USUIL) design problem with a holistic consideration of cost-benefit objectives, road-ULS modal split, and demand uncertainties. To begin with, we introduce a city-wide hub-and-spoke USUIL network structure with a two-tier parallel distribution process. Five major external benefits of underground goods movement are formulated using real-world data. Next, a deterministic model is developed to optimize the network decisions including hub-satellite location-allocation, deployment of tunnels and pipelines, logistics service assignment, and flow routing. Then, to deal with the influences of case-wise demand uncertainties, the model is reformulated into a reliable one by incorporating the ideas of solution robustness and stochastic equivalents. A two-stage heuristic-based optimization tool that hybridizes the adaptive immune genetic algorithm, plant growth simulation algorithm, and Clarke-Wright saving algorithm was proposed to solve the problem. Finally, numerical experiments are conducted on the Beijing city case to validate the proposed method, and the best network schemes obtained from different models and scenarios are compared. Results show that a greater robust control factor in our model helps the network to lower the integrated freight transport cost while improving the social-environmental benefit at the expense of deploying more facilities. The reliable model performs well in balancing the conservativeness (i.e., less capacity insufficiency risk) and optimality (i.e., less cost due to redundant facilities) of the USUIL network design when faced with diverse demand cases.

An image reconstruction modeling approach for micro‐fibrous network of cellulose polymer separator concerning tensile and pore properties

AbstractCellulose is applicable for preparing lithium‐ion battery separators due to its excellent properties. Factors affecting the performance of the cellulose separator can be explored via modeling the micro‐fibrous structure, which can provide guidance for preparing design and optimization of separators. In this paper, a three‐dimensional modeling method based on digital image processing is proposed for random fibrous network of cellulose separator, with a strong focus on the fibrous parameters. Finite element simulation on elastic mechanics is conducted, and the modeling accuracy has been discussed, where the effect of the fiber diameter and quantity on the elastic property of fibrous separator is comprehensively investigated. The relationship between porosity and elastic modulus can be predicted by both modeling simulation and experiment with a small error, indicating that the modeling method is credible. The pore property, that is, porosity and tortuosity, of the constructed model is calculated and compared with related work to characterize battery performance of separator. The result has shown the feasibility of the fibrous model in pore study. This work has provided an effective and reliable modeling approach for fibrous network, which can be utilized to investigate the mechanical and pore properties of battery separators.

Living with Floods Using State-of-the-Art and Geospatial Techniques: Flood Mitigation Alternatives, Management Measures, and Policy Recommendations

Flood, a distinctive natural calamity, has occurred more frequently in the last few decades all over the world, which is often an unexpected and inevitable natural hazard, but the losses and damages can be managed and controlled by adopting effective measures. In recent times, flood hazard susceptibility mapping has become a prime concern in minimizing the worst impact of this global threat; but the nonlinear relationship between several flood causative factors and the dynamicity of risk levels makes it complicated and confronted with substantial challenges to reliable assessment. Therefore, we have considered SVM, RF, and ANN—three distinctive ML algorithms in the GIS platform—to delineate the flood hazard risk zones of the subtropical Kangsabati river basin, West Bengal, India; which experienced frequent flood events because of intense rainfall throughout the monsoon season. In our study, all adopted ML algorithms are more efficient in solving all the non-linear problems in flood hazard risk assessment; multi-collinearity analysis and Pearson’s correlation coefficient techniques have been used to identify the collinearity issues among all fifteen adopted flood causative factors. In this research, the predicted results are evaluated through six prominent and reliable statistical (“AUC-ROC, specificity, sensitivity, PPV, NPV, F-score”) and one graphical (Taylor diagram) technique and shows that ANN is the most reliable modeling approach followed by RF and SVM models. The values of AUC in the ANN model for the training and validation datasets are 0.901 and 0.891, respectively. The derived result states that about 7.54% and 10.41% of areas accordingly lie under the high and extremely high flood danger risk zones. Thus, this study can help the decision-makers in constructing the proper strategy at the regional and national levels to mitigate the flood hazard in a particular region. This type of information may be helpful to the various authorities to implement this outcome in various spheres of decision making. Apart from this, future researchers are also able to conduct their research byconsidering this methodology in flood susceptibility assessment.

Accurate, Efficient and Reliable Small-Signal Modeling Approaches for GaN HEMTs

Accurate, Efficient and Reliable Small-Signal Modeling Approaches for GaN HEMTs

Development and Challenges of Reliability Modeling From Transistors to Circuits

The integration density of electronic systems is limited by the reliability of the integrated circuits. To guarantee the overall performance, the integrated circuit reliability must be modeled and analyzed at the early design stage. This paper reviews some of the most important intrinsic aging mechanisms of MOSFETs and elaborates the physical mechanism of the coupling between aging effects. Then the progress in reliability modeling under static and dynamic operational voltages is reviewed. It is found that although these models can accurately predict the degradation in short term, they are with large errors for the long-term degradation prediction. Besides, for the circuit-level reliability modeling and simulation approach, there are still problems to be solved. This article aims to provide guidance for researchers and practitioners in integrated circuit field, and highlight the challenges for reliability research. It is of great significance to the optimization of the reliability of integrated circuits.

Daily streamflow forecasting by machine learning in Tra Khuc river in Vietnam

Precise streamflow prediction is crucial in the optimization of the distribution of water resources. This study develops the machine learning models by integrating recurrent gate unit (GRU) with bacterial foraging optimization (BFO), gray wolf optimizer (GWO), and human group optimization (HGO) to forecast the streamflow in the Tra Khuc River, Vietnam. For this purpose, the time series of daily rainfall and river flow at Son Giang station from 2000 to 2020 were employed to forecast the streamflow. The statistical indices, namely the root mean square error, the mean absolute error, and the coefficient of determination (R²), was utilized to evaluate the performance of the proposed models. The results showed that the three optimization algorithms (HGO, GWO, and BFO) effectively enhanced the performance of the GRU model.
 Moreover, among the four models (GRU, GRU-HGO, GRU-GWO, and GRU-BFO), the GRU-GWO model outperformed the other models with R² = 0.883. GRU-HGO achieved R² = 0.879, and GRU-BFO achieved R²=0.878. The results of this study showed that GRU combined with optimization algorithms is a reliable modeling approach in short-term flow forecasting.

Degradation mechanisms-based reliability modeling for metallized film capacitors under temperature and voltage stresses

As a key component in power electronic systems, the accurate reliability assessment of metallized film capacitors (MFC) is very critical to assure the system reliability and safety. In most operating conditions, the gradual capacitance decrease induced by dielectric film aging and the accumulation of each sudden capacitance loss by random self-healing events under elevated temperature and voltage are the main degradation mechanisms of MFC. However, most investigations tend to directly fit the MFC degradation data by some statistical means, which ignores the specific degradation mechanisms. Such treatments for degradation data often perform a lower modeling accuracy, which may also bring an obvious deviation of reliability assessment result. In this article, we design a degradation mechanisms-based reliability modeling approach for MFC under temperature and voltage stresses. The dielectric film aging is characterized by Wiener process, where the aging rate is dependent with temperature. The compound Poisson process is developed to capture the random self-healing events, where the arrival rate and each sudden capacitance loss are related to the operating voltage as well as the dielectric film aging rate. Finally, an ADT with temperature and voltage acceleration for MFC is conducted to validate the effectiveness of the proposed approach.