Perform Reliability Analysis Research Articles

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

A Comprehensive Review of SCADA-Based Wind Turbine Performance and Reliability Modeling with Machine Learning Approaches

The increasing reliance on wind energy to meet global energy demands has made wind turbine performance optimization and reliability a critical area of research. Supervisory Control and Data Acquisition (SCADA) data, which provides real-time operational insights into wind turbines, plays a pivotal role in predictive maintenance, fault detection, and energy output optimization. This review explores the current methodologies and advancements in wind turbine performance modeling and reliability analysis, with a particular emphasis on machine learning (ML) approaches. Existing studies that utilize SCADA data to implement various ML models, such as decision trees, neural networks, and ensemble learning techniques (bagging, boosting, stacking), are analyzed for their effectiveness in predicting turbine failures, improving energy efficiency, and optimizing maintenance schedules. Key findings from multiple studies are synthesized, highlighting the strengths, limitations, and real-world applications of these models. Challenges in data quality, model generalization, and the implementation of real-time ML-driven systems in wind farms are also addressed. This review aims to provide a comprehensive overview of the current state of SCADA-based wind turbine analysis and to offer a roadmap for future research that bridges the gap between data-driven models and their practical deployment in wind energy systems.

Physics-informed neural network classification framework for reliability analysis

Reliability analysis is crucial for quantitatively evaluating structural safety amidst uncertainties, laying the foundation for reliability-based design optimization aimed at augmenting structural reliability and reducing economic expenditures, thereby offering substantial engineering benefits. However, performing reliability analysis on intricate structures, especially those requiring time-intensive finite element models, presents a formidable challenge. This study develops an innovative physics-informed neural network classification (PINNC) model to tackle this issue. In the PINNC model, the loss associated with the structural output state (i.e., safety or failed state) is defined as classification loss. Additionally, the structural output value (i.e., actual structural response) is treated as critical physical information, with its associated loss termed as physical loss. To facilitate a separate calculation of physical loss and classification loss, a parametric sigmoid activation function is used, establishing a link between the structural output value and the structural output state. The total loss is calculated as a weighted sum of both physical and classification losses. Unlike conventional neural network classification models that solely focus on classification loss, the PINNC model integrates both physical and classification losses, markedly improving the model’s efficacy in structural reliability analysis. Moreover, an adaptive framework is developed to incrementally include samples proximal to the limit state surface as new training data, thereby reinforcing the PINNC model’s computational precision. The effectiveness of the proposed PINNC framework in overcoming reliability analysis challenges is demonstrated through various applications.

A novel efficient RFEM for reliability analysis and design of multi-line dynamically installed anchor for floating offshore wind turbines

A novel multi-line dynamically installed anchor was previously proposed by the authors to allow for the mooring of multiple floating offshore wind turbine, resulting in a significant reduction in the total number and cost of anchors required for floating wind farms. Considering the spatial variability of soil properties and the uncertainty of environmental loads, the present study performs reliability analysis and design of the multi-line dynamically installed anchor. Firstly, a strategy to repeatedly use fundamental random variables is proposed and validated for reducing the number of random variables used in Karhunen-Loève expansion in the simulation of random field of soil properties when the ratio of the soil domain dimension to the scale of fluctuation is large. Then, the efficiency, accuracy, and robustness of the RFEM (random finite element method) combined with K-MCS (Kriging model and Monte Carlo simulation) based on the proposed strategy are validated through examples of random capacity of foundations. Next, the random capacities and probabilistic VHMT failure envelopes of the multi-line dynamically installed anchor in spatially variable soil are investigated. Finally, the reliability design of multi-line dynamically installed anchors is conducted and compared with that of multi-line pile anchors, in which both the spatial variability of soil and the uncertainty of loads are condidered. The results show that the costs for multi-line dynamically installed anchors are obviously less than those of multi-line pile anchors.

Aerothermal Performance Robustness and Reliability Analysis of Turbine Blade Squealer Tip With Film Cooling

Abstract The uncertainty quantification in the turbine components' aerodynamic and heat transfer performances is widely considered to be the most challenging topic due to its intricate and nonlinear characteristics. This paper first proposes an efficient uncertainty quantification method based on an original parallel framework combining Polynomial Chaos Expansions (PCE) with two forms (stochastic response surface-based and Galerkin projection-based) and the Universal Kriging method. The rigorous mathematical tests are performed to verify the reliability and computational efficiency of the proposed method, and the results support that this method can dramatically reduce computational samples compared to the conventional PCE method while maintaining computational accuracy. Then, the genetic algorithm was introduced to establish an efficient uncertainty quantification framework, and it is applied to the aerothermal performance robustness investigation of the GE-E3 rotor blade tip with and without film cooling. Based on the findings of uncertainty quantification, the injection of cooling air drastically enhances the unstable tendency of the flow and thermal fields, resulting in the actual aerothermal performance of the squealer tip being much lower than that predicted by deterministic calculations. The setting of the film cooling, although effective in reducing the heat flux around the cooling holes, also induces more chaotic flow and thermal fields, leading to sharp heat flux fluctuations around the cooling holes. Finally, our novel reliability analysis algorithm, rooted in the quantification of uncertainty, corroborates the assertion that the introduction of coolant gas, while extending the operational longevity of turbine blades, confers only marginal improvements in the mitigation of lifespan variability. The comprehensive lifespan assessment elucidates that the mean operational longevity of the conventional squealer tip design stands at an estimated 16,169.44 h, accompanied by a standard deviation of 2,750.31 h. In stark contrast, the mean operational longevity of the squealer tip integrated with film cooling measures a significantly enhanced 17,035.17 h, exhibiting a standard deviation of 2,492.73 h. Consequently, the operational lifespan of the conventional squealer tip experiences a decrement of 10.17% in comparison to the anticipated mean lifespan, whereas the reduction for the film-cooled squealer tip registers at 5.36%.

Measurement insights and error analysis of electronic parameters for ultrasonic transducers

Abstract Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages in high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time-variant system, data of one single parameter collected from a certain test cannot match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory were illustrated, including the calculation methods and measuring circuits of electric signals. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g. effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quickly responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.

Reliability analysis of shallow foundation using the important-region-based method

ABSTRACT To improve the computational efficiency and accuracy is a constant pursuit for evaluating the stability of geotechnical structures built in spatially variable soils. This study proposes an important-region-based Karhunen – Loève expansion and first-order reliability method (KL-FORM-IR method) to perform reliability analysis for shallow foundation resting on spatially variable soil. The KL-FORM-IR method further improves the computational efficiency by ignoring the variability of the soil parameters outside the important region. An identification method for locating the important region of shallow foundation is first proposed, and the effect of the scale factor (K f), autocorrelation distance of soil properties, and foundation width (B) on the identification of important regions are investigated. For the purpose of validation, the proposed KL-FORM-IR method compared with the KL-FORM and KL-MCSM by performing reliability analyses based on the same numerical model. Conclusively, the KL-FORM-IR method can improve the computational efficiency and it contributes a new approach to the reliability analysis of shallow foundation resting on a spatially variable soil.

Reliability‐based resilience assessment method for mooring systems under mooring failure

AbstractOffshore floating structures rely heavily on their mooring systems, which can be disrupted by various events during long‐term operation. These could lead to a mooring failure, affecting the usual operations of the structure or even causing more severe hazards. Resilience provides a comprehensive evaluation of how the mooring system performs after a disaster, which is key to optimizing the structural design and operational safety. In this paper, we develop a general and user‐friendly method to quantitatively assess the resilience of mooring systems under mooring failure. We use the reliability index to represent the performance of the mooring system. We then derive its RV, ACI, and RCI, which are based on a system performance curve and reliability analysis. We also consider the effects of climate change and the corrosion of the mooring chain. These factors can significantly affect environmental loads, structural performance, and the recovery process. Moreover, an illustrative example is provided that guides us through the methodology. The proposed method is applied to assess the resilience of a certain mooring system in the South China Sea over a 30‐year service life under different failure scenarios. Our results indicate that overlooking climate change in the design and operation of the mooring system can lead to a significant overestimation of its reliability index and resilience value.

A decoupled probabilistic constrained topology optimization method based on the constraint shift

AbstractTopology optimization (TO) has recently emerged as an advanced design method. To ensure practical reliability in the design process, it is imperative to incorporate considerations of uncertainty. Consequently, performing reliability analysis (RA) during the design phase becomes necessary. However, RA itself constitutes an optimization problem. Combining these two optimization problems can result in inefficiency. To address this challenge, we propose a decoupled approach that integrates deterministic topology optimization (DTO) and RA cycles. The reliability‐based stress‐constrained TO (RBSCTO) problem is considered in this paper. The DTO constraint is derived based on shifting vectors derived from the previous cycle's RA outcomes, enabling low‐reliability constraint shift towards the feasible direction. The DTO is solved based on solid‐isotropic‐material‐with‐penalization (SIMP) and augmented Lagrangian method. Meanwhile, the optimization problem in RA is addressed using finite differences and the interior point method. To reduce the errors resulting from linear approximation and optimization in RA when the target reliability is very low, an outlier handling method is employed. Meantime, we utilize a probabilistic neural network to enhance the efficiency of reliability assessment. Comparative studies against traditional methods across four RBSCTO tasks are demonstrated to validate its effectiveness. Monte Carlo simulations are used to validate the reliability of results.

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.

A Corrosion- and Repair-Based Reliability Framework for Offshore Platforms

Offshore platforms are important infrastructures that often face severe environmental conditions, such as corrosion, throughout their lifetime. This can continuously decrease their structural robustness. Despite the availability of many anti-corrosion strategies, there is still a need for a sound management scheme that can systematically address the lifetime operation of offshore platforms under corrosion. To address this, the work here proposes a corrosion- and repair-based reliability framework for the lifetime operation of offshore platforms. A fixed offshore platform is designed based on current design codes for severe environmental conditions in a given return period, and the effect of corrosion on the structure’s serviceability is modeled. The results show that the extent of the corrosion depth and damage in different years highly affects the ability of a repair to restore a damaged element to its original design strength. The results also show that the residual reliability of the structural members under the splash zone becomes almost zero after the first 10 years of the operation period, implying that these members require quick repair strategies. This study establishes a management program for fixed offshore platforms subjected to long-term corrosion by performing reliability analyses on the components of the platforms and evaluating the maintenance of the components in the splash zone. In the absence of commonly accepted contemporary industry practice standards, this study proposes a corrosion growth model based on API-RP-2A, DNV, and NORSOK standards that can effectively evaluate code-based structural designs. The framework developed here can help offshore platform owners in their decision-making process for corrosion-based safety analysis.

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.

Optimising network resilience: a comprehensive study on reliability modelling and performance analysis of computer network topologies

Optimising network resilience: a comprehensive study on reliability modelling and performance analysis of computer network topologies

Development, cross-validation and greenness assessment of capillary electrophoresis method for determination of ALP in pharmaceutical dosage forms - an alternative to liquid chromatography.

Breast cancer treatment has made tremendous progress in recent years and new therapies are emerging continuously. Alpelisib (ALP) is a novel phosphoinositide-3-kinase (PI3K) inhibitor recently approved for human receptor-positive, human epidermal growth factor receptor 2-negative, PIK3CA-mutated metastatic breast cancer in combination with fulvestrant. ALP has been the subject of only a limited number of preclinical in vitro and in vivo studies using different chromatographic techniques. However, no research has been published on analyzing ALP using capillary electrophoresis (CE). The absence of pharmacopoeial monographs for ALP in both the European and United States Pharmacopoeias highlights the urgent need to develop a reliable analytical method for its quality control in both industry and regulatory authorities. In this work, we have developed a first-ever CE method for the determination of ALP in pharmaceutical dosage forms in just 1.4 minutes. This was achieved with a 25 mM borate buffer at pH 9.3, 30 kV separation voltage and 30 °C capillary temperature. The proposed method was validated according to the ICH guidelines regarding selectivity, linearity (r = 0.9988), precision (RSD < 5.9%), accuracy (bias < 3.0%) and robustness (RSD < 3.2%). It was applied to the pharmaceutical dosage form of ALP and was shown to be suitable for the reliable determination of ALP. Furthermore, to demonstrate the applicability of the CE as an alternative technique to more commonly used HPLC in the analysis of drugs, cross-validation of CE and HPLC methods was performed. Bland-Altman analysis showed that the average difference in determined concentrations between CE and HPLC over a range of 10-100 μg mL-1 was 0.87 μg mL-1 (p = 0.6390, N = 19) meaning that there is no difference in the performance of CE and HPLC in the determination of ALP in pharmaceutical dosage forms. The environmental impact of both methods was assessed using AGREE software and scores for CE and HPLC were calculated to be 0.74 and 0.51, respectively. Because of equally reliable analytical performance and greener analysis, CE should be considered as an alternative technique to HPLC in the analysis of ALP pharmaceutical dosage forms.

Electronic Reliability and Error Performance Analysis of RIS-Aided Communication Networks

Electronic Reliability and Error Performance Analysis of RIS-Aided Communication Networks

Optimising network resilience: a comprehensive study on reliability modelling and performance analysis of computer network topologies

Optimising network resilience: a comprehensive study on reliability modelling and performance analysis of computer network topologies

SHM-based digital twin calibration of a post-tensioned reinforced concrete bridge with vertically prestressed internal joints

Post-tensioned concrete box-girder bridges exhibit complex time-varying behavior due to factors like creep, shrinkage, and steel relaxation. Predicting these phenomena accurately at the design phase is a difficult task, often leading to underestimation of their impact. Particularly in bridges constructed using a balanced cantilever scheme and cast-in-place concrete, the aging process and the timing of load application can influence the time-dependent behavior of the structure. The combination of these effects can result in significant vertical displacements and serviceability issues that necessitate careful consideration. The Italian Bridge Guidelines introduced specific evaluations for post-tensioned concrete bridges, which often require the support of FE models for accurate safety evaluation. The paper reports the investigations carried out on a very challenging post-tensioned concrete box-girder bridge with vertically prestressed internal joints. The main objective is to calibrate a predictive FE model of the bridge by integrating information from design reports and drawings, static and dynamic non-destructive experimental tests as well as onsite inspections and digital surveys to characterize the presence of defects. The data obtained from the test campaign are utilized to build an accurate model, which is further calibrated to become an actual digital twin of the real structure employing ambient vibration tests (AVT) to identify the dynamic behavior of the structure. The digital twin thus obtained will serve as a tool for performing reliability analyses and predicting future serviceability or collapse issues.

Reliability-Based Service III Evaluation for Prestressed Girder Bridges Under Platoon Loads

Platooning may benefit heavy truck transportation through fuel savings, reduced congestion, enhanced safety, and lower emissions. In the future, platoons may be able to act as mobile-WIM stations, and their permit process and allowable load limits may differ from unregulated trucks in a convoy. Previous reliability-based studies have focused on the Strength I limit state and have shown that trucks can operate at weights exceeding standard legal load limits even with short headways at operating-level reliability. However, the Service III limit state often governs prestressed concrete bridges. The AASHTO Manual for Bridge Evaluation does not specify a target reliability index (β) nor reliability-based evaluation guidance for the Service III limit state. The work presented here performed reliability analyses to investigate implicit reliability indices (βImplicit) inferred from bridges designed according to current and past AASHTO criteria, as well as cracking probabilities. Design live loads were used to evaluate the Service III limit state for prestressed concrete NU I-girder bridges, optimally designed using LRFD and LFD/allowable stress design (ASD). Various span lengths, numbers, continuity conditions, prestress loss methods, and allowable tension stress levels were considered. Cracking probabilities ranged between 10% and 67%, which indicates that optimally designed bridges may crack during their service life. Although beyond the scope of the study, the present work suggests a reexamination of service behavior and performance is appropriate, using an alternate mechanistic approach to estimate potential cyclic damage and aid life-cycle assessment. Such assessments could provide a more rational framework for platoon operations while maintaining bridge health and safety.

Performance degradation and reliability analysis of a MEMS flow sensor with accelerated degradation testing

In this article, we research the performance degradation of a MEMS flow sensor with accelerated degradation testing (ADT). The degradation of temperature stress (Ts) on sensor is studied. Based on the performance in the aging test, resistance degradation ∆R is selected as a more suitable degradation characteristic parameter. Furthermore, the degradation process of the characteristic parameter results from resistivity decreases, in which Ts leads to stress relief within the bulk of the resistor. By implementing the Constant-stress accelerated degradation test (CSADT), we estimate the lifetime of this MEMS flow sensor based on the Arrhenius model. The calculated lifetime is 6.833 years for the 120°C group, 3.942 years for the150°C group, and 2.492 years for the180°C group. In addition, using statistics analysis, the Weibull distribution (WD) is proved to be the better mode for the MEMS flow sensor acceleration life with the Anderson-Darling (AD) being 235.910. The scale and shape of WD was evaluation. On the other hand, to verify the rationality of the distribution, the Standard residual error method and the Cox-Snell residual error method have been used separately, to perform the Goodness-of-fit tests. The AD value of Standard residual error and Cox-Snell residual error is 3.476. It proves that the selected life distribution model of this MEMS flow sensor is befitting. Finally, in this research the CSADT results are original and novelty. The lifetime distribution results reveal that considering the Goodness-of-fit tests can significantly improve the accuracy of reliability analysis of this MEMS flow sensor. Based on those results, a reliability method for this MEMS flow sensor is established.

Simulation of radiation environment and design of multilayer radiation shield for orbital exploration of Jupiter

The harsh radiation environment of Jupiter imposes significant constraints on the shield design of spacecraft. Due to these constraints, traditional aluminum shields cannot meet the radiation shielding requirements needed for the exploration of Jupiter. In this paper, the design for a highly efficient radiation shield structure is proposed that utilizes a combination of high-Z/low-Z elements. A typical exploration orbit of Jupiter is selected, and the energetic particle radiation environment is calculated using the D&G83 model. The Geant4 toolkit is used to evaluate the shielding performance of materials, and quantitative simulations are performed to analyze the shielding efficiency of bilayer and trilayer shield structures under an areal density constraint of 4.5 g/cm2. Based on the simulations, an optimal shield structure is determined. The simulation results show that the integral electron flux with energy greater than 3 MeV is approximately three orders of magnitude greater than that of geostationary orbit with an orbit of 4000 km at the perijove and 5420000 km at the apojove, resulting in an enormous total dose effect. The optimal shield structure is a bilayer structure that comprises of a tantalum outer layer with a density of 3.5 g/cm2 and a polyethylene inner layer with a density of 1.0 g/cm2. This shield has the ability to reduce the total radiation dose to 1559 Gy per year, which is 32 % lower than that of traditional aluminum shields. Findings in this research are critical for design of radiation shields, and can be used for performing reliability analyses and predicting the lifespan of spacecraft during the development process.