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With the aging of production systems, failure modes become more common resulting in additional maintenance costs. To reduce these costs excess, flexible and smart maintenance strategies should be considered. This article proposes an integrated condition-based maintenance method associating opportunistic and importance measure concepts (IMC). The objective of an IMC-based model is to determine the contribution of the system's components according to their criticality degree for reliability improvement and maintenance planning. The identification of the best maintenance planning consists of determining the expected minimal cost that guarantees the repair actions of a group of critical components in one shot. Therefore, determining IMC values is not so easy for complex systems, it requires knowing their operational structure, the determination of the reliability value of each configuration and finally calculating each component's IMC degree and ranking them for the prioritisation selection. For testing the proposed method, an industrial case study has been used, regarding a complex system whose components fail at random times. The system undergoes minimal repairs if one or more components fail accidentally or by decision after inspection actions. The numerical results show that the developed approach incurs minimal maintenance costs, and can be integrated as a decision-aid solution for manufacturers. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation of the impact of the deposition temperature on the HfxZr1−xO2 (HZO) ferroelectric layer of ferroelectric random access memory with TaN electrodes. This investigation mainly focuses on its electrical characteristics and compares the differences. It is revealed that the deposition temperature plays a crucial role in determining the crystal structure of HZO, which can exhibit a combination of tetragonal and orthorhombic phases or exist solely in one of the two phases. Furthermore, the grain size of HZO varies with the deposition temperature. These findings correspond well to the electrical measurement results, including leakage current, polarization, capacitance, and reliability tests. The study tracks the phase transition process during the operation of switching cycles when the phase transition process can be monitored as well. To better understand the observed differences, physical models that shed light on the underlying mechanisms affected by deposition temperature are proposed at the end of the article. [ABSTRACT FROM AUTHOR]

Developing accurate and computationally efficient algorithms for system performance metrics calculation is critical to implementing effective control and optimization in manufacturing system operations. In this paper, we propose a recursive decomposition/aggregation-based method for calculating the performance metrics of assembly/disassembly systems with multiple merge/split operations and sub-assemblies. It is assumed that the machines follow the exponential reliability model and the buffers are of finite capacity. To achieve this, we first consider assembly systems with multiple component lines merging at a single assembly operation. By decomposing the system into a set of virtual serial lines, we derive an analytical procedure to approximate the starvation and blockage probabilities of the merge operation, which are used to recursively update the parameters of the virtual serial lines. Then, the performance metrics of the original assembly system are approximated based on the corresponding machines and buffers in these virtual serial lines. Next, we extend the algorithm to assembly/disassembly systems with multiple merge/split operations and sub-assemblies. This is accomplished by identifying the so-called assembly/disassembly units formed based on the virtual serial lines and applying the calculations derived earlier recursively. Simulation experiments are carried out to justify the convergence, computational efficiency, and approximation accuracy of the proposed algorithms. An industrial case study is presented to demonstrate the theoretical methods in practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we derive a condition-based maintenance strategy for a degradation-shock dependence system subject to two causes of failure, degradation and random shocks. We develop a reliability model for a system under warranty for failure in which we consider the dependence between the degradation process and random shocks and do a warranty cost analysis based on the suggested model. We consider a degradation-shock dependence system that is under warranty with three separate services: repair, replacement, and preventive maintenance. These are triggered when a product degradation crosses one of three predetermined degradation thresholds. We also investigate the dependence between random shocks and degradation modelled by a time-scaled covariate factor. We study the random shock considering fatal shocks that require replacement and nonfatal shocks that require only repair and the degradation caused by nonfatal shocks. The condition-based maintenance strategy for a degradation-shock dependence system is a novel approach to analysing system degradation, with a goal of optimising warranty costs; we also minimise the expected total system cost to find an optimal warranty length. We present several numeric examples and real-world applications to illustrate our proposed approach. [ABSTRACT FROM AUTHOR]

In this article, we consider test for the two null hypotheses for H0:SX(t)=α$H_0: S_X(t)=\alpha$ and H0:SX(t)=SY(t)$H_0: S_X(t)=S_Y(t)$, two widely useful tests in reliability, based on ranked set sampling (RSS). We derive the likelihood ratio test as well as the associated exact and asymptotic results. Considering a fixed significance level and power of the test, we show that the proposed test statistic outperforms the existing test. In small sample cases, the proposed test leads to a much narrower confidence interval for the reliability function SX(t)$S_X(t)$. Then, the test statistics obtained from simple random sampling and RSS schemes are compared through which, the efficiency of using RSS is demonstrated. For illustration, we apply the proposed test to a degradation data from the reliability literature. Upon using RSS, the cost of measurement gets reduced and efficiency gets improved, suggesting the importance and use of RSS data in reliability experiments and their design. [ABSTRACT FROM AUTHOR]

The resilience and adaptability of complex networks is crucial in ensuring their functionality against disruptions. Particularly, maintaining network connectivity under various attack scenarios is a key aspect of such resilience. Network connectivity refers to the degree to which nodes within a network are interconnected and able to exchange information or resources. Its robustness reflects the ability of a network to maintain connectivity under various attacks. Such ability has profound physical significance, ensuring the stability and reliability of real-world systems. Currently, connectivity robustness assessments rely heavily on very time-consuming attack simulations. This paper introduces a graph neural network framework for network connectivity robustness learning (GCRL) to advance the study of network connectivity robustness. GCRL transforms initial degree distributions and network topology into informative embedding vectors, which are then processed by a robustness learning module mainly composed of multi-layer perceptron, achieving both high speed and precision. Our extensive experiments demonstrate the superior performance of GCRL obtained in less time compared to existing methods, especially in tough scenarios where test data distributions significantly differ from the training set. The framework also shows adaptability to networks of different sizes, making it a more generalized solution for complex network robustness learning. [ABSTRACT FROM AUTHOR]

As the foundation of circuits and electronic systems, the quality of printed circuit boards (PCBs) is directly related to the reliability of the whole system. At present, deep learning-based target detection technology has a broad application prospect in PCB defect detection. However, there are still some shortcomings in the process of detection. For example, the detection speed is slow, and the detection accuracy is not ideal for defects with small size. Therefore, this paper proposes an improved algorithm based on the YOLOv5s model to improve the detection of PCB defects. The algorithm enhances the connection between the shallow and high layers of the network by improving the backbone network structure. Meanwhile, a self-attention mechanism is introduced into YoloHead to enhance the feature extraction and the ability to focus on the global information of the feature map, and the self-attention mechanism is optimized by combining the computational characteristics of the SENet, the CBAM and the residual unit. While improving the accuracy of PCB defect detection, better computational efficiency is ensured. In subsequent experiments, this paper trains and tests the improved YOLOv5s algorithm using publicly available PCB defect datasets. The results show that the algorithm achieves a detection rate of 39.7 frames per second and a maximum average precision of 96.14%, which is a 4.68% improvement over the original YOLOv5s. [ABSTRACT FROM AUTHOR]

At present, there are two independent technologies and equipment for the test of the reciprocating compressor plate valve: the sealing test and blowing test. This method has single function and complicated test operation and may rely on theoretical calculations, which reduces the reliability of the test conclusion because the motion law of valve disk during opening and closing is extremely complicated. The existing test methods cannot meet the needs of more in-depth and accurate research, development and improvement of the plate valve. This study proposes a test device and method that can not only quantitatively test the performance of the plate valve but also qualitatively test the dynamic process of the suction valve plate, which can greatly reduce the testing time provide more intuitive and accurate valve plate motion tracks and valve paths. This paper takes the primary plate suction valve as the test object and adopts the automated multi-functional system to accurately obtain the valve's sealing property, effective flow area, thrust coefficient, flow coefficient, drag drop, valve gap Mach number, initial opening pressure difference, maximum opening, and residence time of the valve plate on the valve lift guard to provide necessary data for valve design and performance evaluation. The test results of valve opening time are verified by industrial reciprocating compressor, which proves the accuracy and practicability of the system. [ABSTRACT FROM AUTHOR]

For specific missions of spacecraft including on-orbit assembly, docking, grasp, etc., the presence of uncertainties leads to difficulties in the analysis of impedance force control systems and evaluations of safety for space manipulators. Therefore, in this study, a novel interval uncertainty-oriented impedance force control method for space manipulators with time-dependent reliability was established. The proposed method can accurately and rapidly characterize uncertainties. Additionally, it can reflect the reliability of a control system based on an overall time history. Once the uncertainties of the dynamic models and state responses are known, their uncertainty bounds can be obtained using an established non-probabilistic propagation method with high accuracy. An interval process model and the first passage theory are employed in the non-probabilistic time-dependent model. A joint reliability index is established based on the single reliability indexes, which can more efficiently evaluate the control system reliability than the individual indexes. A numerical example related to space manipulator is used to study the developed impedance force control method and verify the effectiveness of the method. The results from the response curves reveal validity of the proposed method, while the reduction in time proves efficiency of the method. • Uncertain impedance force control method for space manipulator is proposed. • A non-probabilistic reliability-based impedance force control method is proposed. • Uncertainty analysis of kinematics and dynamics is proposed with bounded parameters. • Uncertain impedance control is established using interval uncertainty propagation. • A non-probabilistic time-dependent reliability is constituted to assess response. [ABSTRACT FROM AUTHOR]

A R744 (CO 2) refrigeration system has been designed to cool down the Large Hadron Collider (LHC) silicon detectors ATLAS and CMS, located at CERN, Switzerland. The silicon detectors are subjected to high radiation levels. The system is composed of a pri- mary CO 2 trans-critical booster vapor compression loop operated with piston compressors, and an oil-free liquid pumped loop on the evaporation side, to preserve the detectors. To ensure the system's reliability, the cooling facility is designed to operate under a parallel operation mode of several modular 70 kW plant units providing evaporation temperature as low as -53 °C. This layout, is also useful in case of components failure and maintenance. A numerical model is developed using a dynamic simulation software Dymola that is based on the open source Modelica modelling language. The simulation results are proven on a first demonstration plant (System A) experimentally to explore the systems control logic and to validate the reliability of the system before it is built on the detectors side. In this paper the models development is explained and the results of the experimental validation of the numerical model are shown. [ABSTRACT FROM AUTHOR]

The use of redundancies or spares in a system is a widely adopted technique to enhance system reliability and reduce the risk of system failure. Redundancies are typically incorporated into systems at the component or system levels. It is a significant problem to allocate appropriate redundancies into a system from a set of available options for the same. In this paper, we establish sufficient conditions to compare the reliability of coherent systems of dependent components with different sets of active redundancy, whether at the component level or the system level, based on some stochastic orders. We have obtained the results for the component lifetimes following the proportional odds (PO) model (the Marshall–Olkin family of distributions) for any lifetime distribution as a baseline distribution. We have studied the problem in the most general setup, with the consideration of coherent system that includes most of the common system structures, the consideration of non-matching spares, the consideration of dependencies of the components with different associated parameters of the copulas, and the consideration of general distribution as the baseline distribution of the PO model. We provide examples satisfying the sufficient conditions of the theoretical results. Additionally, we illustrate some of the results using real-world data. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In system reliability, coherent systems play a central role since without them any system is considered flawed. A system is coherent if all the components are relevant to that system i.e. functioning of every component has an effect in the functioning of the system and the structure of the system is monotone. Monotone structure means that improving any component will not deteriorate the system. A mixed system is a stochastic mixture of coherent systems and any coherent system is a special case of a mixed system. In reliability engineering, one major problem is to compare various coherent and mixed systems so that the better system can be used to increase the overall reliability. Another important problem is to measure the complexity of systems. A highly complex system will naturally have a higher running and maintenance cost associated with it and it is desirable for a reliability engineer to have an understanding regarding the complexity of systems beforehand. In this paper, we address these two problems from an information theoretic approach. Extropy is a measure of information which is the dual of the famous Shannon entropy measure. Recently, a new measure related to extropy, called cumulative residual extropy (CREx), was introduced in the literature by Jahanshahi et al. (Probab Eng Inf Sci 1–21, 2019). This measure is based on the survival function of the underlying random variable and it has some advantages over extropy measure. In this work, we analyze the CREx measure for coherent and mixed systems and develop some comparison results among systems. We also obtain some bounds of CREx of coherent and mixed systems consisting of independent and identically distributed (iid) and dependent and identically distributed (d.i.d.) components. We propose a new divergence measure to calculate the complexity of systems having iid components. Also, we introduced a new discrimination measure to compare various coherent and mixed systems when pairwise comparisons by usual stochastic order is not possible. Finally, we discuss analysis of the CREx measure of coherent systems having heterogeneous components. We also provide applications involving redundancy allocation. Various numerical examples are considered for illustrative purposes. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive investigation into the performance of grid-connected photovoltaic (PV) power plants situated in a hot desert climate. The study employs a combination of simulation models and experimental data to assess the impact of two tracking systems – fixed and dual-axis – on the overall efficiency and energy yield of the PV installations. Through detailed simulations, we analyze the dynamic behavior of the PV systems under varying solar irradiance and the atmospheric conditions typical of hot desert environments. The dual-axis tracking system is compared against the conventional fixed-mount system to evaluate its effectiveness in maximizing energy production and improving the overall efficiency of the PV power plants. Experimental data collected from an actual grid-connected PV facility in a hot desert region is incorporated to validate the simulation results and enhance the accuracy of the study. The observed performance metrics include energy output, system reliability, and the impact of environmental factors on long-term sustainability. In general, in all weather conditions, solar tracking provides better yields than a fixed solar module, generating about 32% and 12% more energy in sunny and overcast weather conditions, respectively. Under sunny weather, the two-axis sun tracking system and a fixed tilt angle produce 333.228 kWh and 247.56 kWh of energy, respectively. The energy yield generated on a partly cloudy day by a two-axis sun tracking system and a fixed tilt angle PV system is 281.496 kWh and 257.856 kWh, respectively. In addition, the highest electricity gain for two-axis solar trackers versus fixed PV plants was found to be 34.6%, and the lowest value was found to be 9.16%. The experimental analysis shows that the efficiency of the polycrystalline PV module is 12% and 14% on sunny days and cloudy days, respectively. The comparison between fixed and dual-axis tracking systems contributes to the optimization of PV installations, enabling more informed decisions for sustainable energy production in regions characterized by high temperatures and intense solar radiation. To support the expanding applications of grid-tied PV systems with a sun tracker in the same or comparable location and climate conditions (Saharan environment), these studies have the practical utility of advising and offering helpful feedback to engineers, system designers, contractors, and investors. [ABSTRACT FROM AUTHOR]

To guarantee the safety and reliability of equipment operation, such as liquid rocket engine (LRE), carrying out system-level anomaly detection (AD) is crucial. However, current methods ignore the prior knowledge of mechanical system itself, and seldom unite the observations with the inherent relation in data tightly. Meanwhile, they neglect the weakness and nonindependence of system-level anomaly which is different from component fault. To overcome above limitations, we propose a separate reconstruction framework using worsened tendency for system-level AD. To prevent anomalous feature being attenuated, we first propose to divide single sample into two equal-length parts along the temporal dimension. And we maximize the mean maximum discrepancy (MMD) between feature segments to force encoders to learn normal features with different distributions. Then, to fully explore the multivariate time series, we model temporal-spatial dependence by temporal convolution and graph attention. Besides, a joint graph learning strategy is proposed to handle prior knowledge and data characteristics simultaneously. Finally, the proposed method is evaluated on two real multi-sensor datasets from LRE and the results demonstrate the effectiveness and potential of the proposed method on system-level AD. • A novel neural network based on segmenting and reconstructing temporal-spatial feature for system anomaly detection. • Segmenting operation is designed to overcome the weakness of anomaly, which is simple and universal for time series data. • A joint graph learning strategy and a novel temporal-spatial feature extraction module are proposed for multi-source data. • Experiments on two different real-world datasets are conducted and demonstrated the superiority of proposed method. [ABSTRACT FROM AUTHOR]

As for an operation system, having a solid and reliable composition structure is the basis for the effective implementation of combat tasks and the prerequisite for giving full play to the system effectiveness. In order to estimate the continuous running capability of the operation system architecture in case of under attack or faults occurrence, starting from the perspectives of point connectivity and edge connectivity, a reliability analytics method for the operation architecture is proposed based on the connectivity among its components. The proposed method is demonstrated by case studies, and can be used for operation systems design and contribution. [ABSTRACT FROM AUTHOR]

Reliability optimization is a process aimed at improving the effectiveness and efficiency of maintenance activities within an organization. This optimization can concern various aspects, including failure prevention, resource management, cost reduction, and improving the operational availability of equipment. In this work, our study aims to implement a comprehensive maintenance strategy for the 1600T press at Algal + company with the primary objectives of preventing breakdowns, minimizing unplanned downtime, optimizing productivity, extending the lifespan of equipment, ensuring safety, and improving the competitiveness and profitability of the company. The problem of reliability and availability of the 1600T press at the Algal + company can be addressed by a systematic approach to industrial maintenance. By adopting a holistic approach to industrial maintenance, Algal + company can improve the reliability and availability of its equipment, thereby reducing unplanned downtime and optimizing overall productivity. By systematically applying FMECA to the 1600T press, the optimization efforts become data-driven and focused on addressing the most critical risks. This, in turn, significantly contributes to optimizing the reliability and availability of the equipment, reducing unplanned downtime, and enhancing overall operational efficiency. [ABSTRACT FROM AUTHOR]

Today, with the growth of the population and increasing trend in the use of electrical energy, the importance of the reliability and stability of the power grid has increased. The ever-increasing development of the power grid and subsequent blackouts of the power grid can lead to serious problems in the daily life and economy of a country. In addition to economic damages, power losses in the power network can lead to dissatisfaction and decreased consumer confidence in the power grid. This research has been carried out to check the application of the genetic algorithm to calculate reliability indices including SAIFI, SAIDI, etc., and its impact on enhancing the reliability of the standard IEEE 33 and 69 bus distribution networks. Additionally, this study explores the GA effectiveness in minimizing both active and reactive power losses. The simulation results in MATLAB, show the constructive effect of applying the GA, shedding light on its potential to optimize the distribution network reliability and minimize power losses, offering valuable insights for power system optimization and reliability improvement. [ABSTRACT FROM AUTHOR]

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The reliability growth test (RGT) is a popular method to improve system reliability, but it can be time-consuming to implement in practice. So, the accelerated reliability growth test (ARGT) is developed. Since the immature design, the newly designed system is easily exposed to latent failures, which are more and more difficult to find with the increase of improvements. In addition, the constant stress accelerated reliability growth test (CSARGT) requires a long test time, and it's not suitable in practice. Therefore, a new step stress accelerated reliability growth test (SSARGT) method is proposed to stimulate the occurrence of latent failures and save test time. Firstly, the army materiel systems analysis activity (AMSAA) model is used to track the RGT under step accelerated stresses. Secondly, taking temperature as the accelerated stress, the Arrhenius model is adopted to establish the quantitative relationship between lifetime and stress level in the accelerated life test (ALT), and obtain the acceleration coefficient. Then, combined with the acceleration coefficient, the system reliability on the highest stress level at the end of SSARGT is extrapolated to the use condition. Finally, a case study on the high-voltage power supply system is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the reliability function of a weighted $k$ k -out-of- $n$ n system with multiple types of components. A weighted system with $M$ M different types of components is considered and two real-life scenarios for the operation of the system are defined. In the first one, it is assumed that the system operates if the accumulated weight of all active components is more than a predetermined threshold $k$ k , otherwise the system fails. In the second scenario, the system is assumed to operate if the total weight of the operating components of type $i$ i is more than or equal to the corresponding threshold ${k_i}$ k i , for at least $m$ m types of components, $m \le M$ m ≤ M. Our approaches in assessing the reliability function of the system lifetime rely on the notion of survival signature corresponding to multi-type weighted $k$ k -out-of- $n$ n system. The formulas for the proposed survival signature are presented and the corresponding computational algorithms are also given. To examine the theoretical results, the reliability function and other aging characteristics of a wind farm (system) consisting of three plants located in three different regions are assessed numerically and graphically. Finally, an allocation problem to determine the best choice for the distribution of each type in the weighted $k$ k -out-of- $n$ n systems is studied. [ABSTRACT FROM AUTHOR]

We explore the Redundancy Allocation Problem (RAP) under the objective of minimizing the cost of a production system of general topology in which system reliability is treated as a chance constraint. A novel simulation optimization-based solution method grounded in the concepts of the trust region and response surface methodology is proposed to efficiently solve the generalized RAP (GRAP) under random system survival times. The generalizability of the RAP model and efficiency of the solution method allows for our approach to be utilized in a wide variety of real-world applications. We demonstrate in a series of numerical experiments based on production systems of varying complexity that the finite convergence of the proposed method is much more efficient than the commonly-used genetic algorithm. It is shown that on a simple bridge network, only the proposed algorithm can find the true optimal solution to the GRAP under an allotted computational budget. On a complex network which includes series, parallel, and logical relationships, the proposed algorithm is also shown to find solutions to the GRAP which have substantially lower total system cost than those found by GA under a wide variety of scenarios. [ABSTRACT FROM AUTHOR]

Auxiliary functions in software systems, often overlooked due to their perceived simplicity, play a crucial role in overall system reliability. This study focuses on the effectiveness of agile practices, specifically the pair programming and the test-first programming practices. Despite the importance of these functions, there exists a dearth of empirical evidence on the impact of agile practices on their development, raising questions about their potential to enhance correctness without affecting time-to-market. This paper aims to bridge this gap by comparing the application of agile practices with traditional approaches in the context of auxiliary function development. We conducted six experiments involving 122 participants (85 novices and 37 professionals) who used both traditional and agile methods to develop six auxiliary functions across three different domains. Our analysis of 244 implementations suggests the potential benefits of agile practices in auxiliary function development. Pair programming showed a tendency towards improved correctness, while test-first programming did not significantly extend the total development time, particularly among professionals. However, these findings should be interpreted cautiously as they do not conclusively establish that agile practices outperform traditional approaches universally. As indicated by our results, the potential benefits of agile practices may vary depending on factors such as the programmer's experience level and the nature of the functions being developed. Further research is needed to fully understand the contexts in which these practices can be most effectively applied and to address the potential limitations of our study. [ABSTRACT FROM AUTHOR]

Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. [ABSTRACT FROM AUTHOR]

In system reliability analysis, researchers are interested in evaluating the characteristics of the lifetime distribution of components in a system based on system lifetime data. In collecting system lifetime data, a censoring scheme is often adopted due to time and budget constraints. In this article, we consider the situation where the system lifetime data from two different coherent systems are subjected to Type‐II right‐censoring, and we are interested in testing the homogeneity of component lifetime distributions based on Type‐II censored system lifetime data with known system structures. Based on the Mann‐Whitney U$$ U $$ test and empirical likelihood ratio tests developed for testing the homogeneity of component lifetime distributions with complete system lifetime data, we propose different non‐parametric test procedures using the permutation of the unobserved censored system lifetimes. We consider a restricted assumption on the censored lifetimes to reduce the permutations required in the computation. The computational approaches to obtain the critical values of the proposed test procedures are provided using the Monte Carlo method. A practical example is used to illustrate the proposed test procedures. Then, the power performance of the proposed test procedures is evaluated and compared through a Monte Carlo simulation study. The simulation results show that the proposed test procedures provide comparable power performance for Type‐II censored system lifetime data in contrast to the complete sample case under different scenarios. Finally, recommendations based on the simulation results and concluding remarks are provided. [ABSTRACT FROM AUTHOR]

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real‐time data anomaly detection system for small hydropower clusters (RDADS‐SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real‐time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real‐time data anomaly detection algorithm for small hydropower stations integrated with the Z‐score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived‐based hot‐standby RDADS‐SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision‐making. [ABSTRACT FROM AUTHOR]

It is the purpose of this paper to propose a novel clustering technique tailored to randomly censored data in reliability/survival analysis. It is based on an underlying mixture model of Weibull distributions and consists in estimating its parameters by means of a variant of the Expectation–Maximization method in the presence of random censorship. Beyond the description of the algorithm, model selection issues are addressed and we investigate its performance from an empirical perspective by applying it to possibly strongly censored (synthetic and real) survival data. The experiments carried out provide strong empirical evidence that our algorithm performs better than alternative methods standing as natural competitors in this framework. [ABSTRACT FROM AUTHOR]

In reliability engineering, it is frequently encountered that multiple performance characteristics (PCs) deteriorate simultaneously. The associated degradation processes are usually dependent and exhibit some heterogeneity from unit to unit, which makes the multivariate degradation modeling and reliability evaluation more challenging. To this end, we propose a new multivariate gamma process model. This model introduces a multivariate random vector, whose joint distribution is constructed by marginal gamma distributions and a copula function, to describe the unit‐to‐unit variability and the dependence among PCs. Meanwhile, it does not require all PCs to be inspected at the same time points in contrast to the traditional copula‐based degradation models. In addition, two reliability evaluation methods are developed. Model parameters are estimated by the stochastic expectation maximization algorithm, and a three‐step procedure is provided to initialize this algorithm. Subsequently, numerical simulations are implemented to verify the proposed methods. Finally, two examples are provided for illustration, and it is shown that the proposed model and methods scale well to the degradation data with different numbers of PCs. What is more, comparisons with several benchmark models are performed, and the superiority of the proposed model is well demonstrated. [ABSTRACT FROM AUTHOR]

Background: Infective endocarditis (IE) is a rare but severe infectious disease. Patients with IE are treated for weeks in the hospital and have profound impairments to their health. New treatment modalities increase options for outpatient care. Little is known about how patients perceive their disease and hospitalisation. We aimed to explore the needs of patients with IE during hospitalisation and the first few months after discharge. Methods: In this qualitative study, 20 patients (45–86 years of age) hospitalised due to IE in Swedish hospitals were interviewed a median of 112 (67–221) days after hospitalisation. Data were analysed with qualitative content analysis, identifying eight subcategories, two categories, and an overall theme. Results: The overall theme illuminated a spectrum of needs of patients suffering from IE, between treating the disease and meeting the person with the illness. The needs encompassed eight axes with dual focus on both medical excellence and person-centred care. Medical excellence was needed to optimally treat, supervise, and offer follow-up on this rare and severe disease; patients longed to come home, and there were issues of reliability in the healthcare system. Person-centred care was requested, including individualised information leading to knowledge, reorientation, the beginning of health restoration, and being met as a unique person. Symptoms of fatigue, wasting, and cognitive and mental distress were often neglected by the caregiver. Conclusions: This explorative study shows the patient's needs as important areas in a spectrum between medical excellence and person-centred care. Care in specialised units secure quality. Early discharge is requested by patients. Multiprofessional individualizing outpatient care needs to develop with preserved safety and medical excellence. The disease trajectory after discharge progresses slowly, and the possibility of mitigating its progress is still unclear. Person-centred care, screening for delayed restoration and rehabilitation after endocarditis are important fields for future studies. [ABSTRACT FROM AUTHOR]

Robustness of higher‐order network (HON) with dependency is different from first‐order network (FON), as the attack orders and the number of iterations are totally different. To measure the robustness of HON with dependency, this study proposed new attack strategies on node and edge failure through measuring the size of the giant component and global efficiency. And two real‐world systems of hazardous materials transportation accident causation system and Chinese high‐speed railway system with different network size were empirically studied. Results show that HON is more fragile to robustness attacks than FON. The number of attack iterations is uncertain in HON, which depends on the attack strategies and the network structure. Meanwhile, nodes and edges with high dependency have greater impacts on network robustness, providing valuable insights into capturing critical information. This study may be helpful to design the robust system, improve the system's reliability and serve as a valuable reference for future exploration of HONs robustness. [ABSTRACT FROM AUTHOR]

This study was conducted to assess the stakeholders' engagement in the Brigada Eskwela Program at Norala Central Elementary School. Descriptive-evaluative research design was used among seventy (70) randomly selected respondents composed of school heads, program coordinators, parents, and other stakeholders (community members, LGUs, NGOs) in the participating school. A set of researcher-made survey questionnaires was utilized in data gathering to assess the stakeholders' engagement in the three phases of the Brigada Eskwela Program: PreImplementation, Implementation, and Post-Implementation phase. The main data source of this study was validated by experts and had undergone a reliability test through pilot testing. Frequency, mean, and ANOVA repeated measures were used to analyze the result of the study. Findings revealed that stakeholders' engagement in the Brigada Eskwela Program is at a "very high level' in the pre-implementation and implementation phases. However, in the postimplementation phase, it was only at a "high level". Further, this study claimed that stakeholders' engagement in the three phases of the Brigada Eskwela Program has a significant difference implying that most stakeholders participated during the BEP implementation phase as compared to the pre-implementation and post-implementation phases. [ABSTRACT FROM AUTHOR]

Over the past few decades, wearable sensor technology has played a pivotal role in patient information acquisition. A new paradigm for unconstrained medical data collection emerged with noncontact sensors such as Kinect and industrial-grade RGB cameras. These innovations have enhanced patient experiences and offer vast potential for long-term monitoring, telemedicine, and remote healthcare in underserved areas. However, while many research efforts have zeroed in on specific components, such as sensor development or algorithm design, this has occasionally led to noncontact systems' reliability, accuracy, and connectivity challenges. Consequently, there is a pressing need for research that adopts a more holistic approach, ensuring the optimal integration of sensors and algorithms. In this study, we introduce a method of constructing a noncontact diagnostic system powered by deep learning vision algorithms that showcase strong resilience against viewpoint changes and obstructions in human motion identification and assessment. Using four RGB cameras, we capture human dynamics and leverage a pose estimator to generate comprehensive 3D human postures. Afterward, these postures are refined to aid in subsequent behavior prediction. Our multitask-trained model significantly bolsters the system's adaptability to posture discrepancies. Notably, this noncontact diagnostic system thrives in challenging environments, such as 360-degree surveillance, intricate situations, and low-light settings where traditional sensors often falter. In addition, we have assembled a multiview autism behavior dataset. Through it, our embedded deep learning algorithm showcases exemplary action category recognition (reaching up to 95.09%), highlighting further its practical implications. [ABSTRACT FROM AUTHOR]

Accurately analyzing the reliability of driveshaft systems is crucial in engineering vehicles and mechanical equipment. A complex system reliability modeling and analysis method based on a dynamic Bayesian network (DBN) is proposed to repair accurately and reduce the cost in time. Considering the logical structure of the drive shaft system, the reliability block diagram (RBD) of the manufacturing system is constructed in a hierarchical and graded manner, and a method of obtaining the Bayesian network (BN) directly from the RBD is adopted based on the conversion relationship between the RBD, fault tree and BN. A variable‐structure DBN model of the system is constructed based on a static BN extended in time series and incorporating dynamic reliability parameters of the components. Reliability analyses based on DBN reasoning, including reliability assessment, significance metrics, and sensitivity analyses, were performed to identify critical subsystems and critical components. This research contributes to enhancing product reliability, equipment utilization, and improving economic efficiency. [ABSTRACT FROM AUTHOR]

A hybrid maintenance strategy is proposed, combining reliability-centered and condition-based maintenance strategies to prevent engine failures. A case study on the lubrication deficiency of a ship's main engine bearings was conducted. The reliability-centered maintenance phase is based on reliability-increasing rules, equipment facts, and a knowledge base. Then, a strategy is developed where system parameters are continuously monitored. These parameters are evaluated in the inference engine, and maintenance recommendations are provided to the ship operator. The investment cost of th developed system is discussed using a real engine failure case. The study shows that the strategy can improve maintenance on existing ships and system reliability at low costs. The methodology offers a cost-effective approach for the sustainability of the main engine, as demonstrated by a real crankshaft failure report. Unlike existing practices, it provides influential maintenance decisions based on evaluating system parameters within four operation ranges. Simulations show it detects insufficient lubrication faults at low engine loads, superior to current applications. The proposed approach suits different types of existing and newly built ships, engines like dual-fuel, and technologies such as semi-autonomous ships. [ABSTRACT FROM AUTHOR]

Zirconia/alumina composites are a family of structural ceramics with excellent mechanical properties. In order to improve the engineering reliability of this kind of composites, the uniform spatial distribution of the two phases is necessary. One of the effective methods to achieve this goal is to synthesize uniform zirconia–alumina composite nano powders. Here, we report mechanochemical synthesis of zirconia–alumina composite nano powders, in which dehydration and crystallization of Zr–Y–Al hydroxide can be achieved under milling conditions, leading to the formation of the tetragonal zirconia phase with supersaturated Al3+. The mechanochemically synthesized powders have the uniform element distribution and a fine primary crystallite size of around 11 nm. After sintering at 1400°C, t‐ZrO2/α‐Al2O3 composite with the uniform phase distribution and grain size distribution could be obtained. To uncover the full benefits of these supersaturated composite powders in ceramic processing, future work could be done to break up the deagglomerations and improve the sinterability of the mechanochemically synthesized zirconia/alumina powders. [ABSTRACT FROM AUTHOR]

The integration of new battery technologies has become a focal point for distribution utilities, driven by decreasing costs and the need for fast responsiveness. Transportable battery energy storage systems (TBESSs) offer additional flexibility, allowing connection at multiple substations or grid feed points. However, concerns remain regarding their impact on distribution systems (DSs), particularly on protection devices (PDs). This study addresses these concerns by investigating the influence of TBESSs on the protection systems of a real-world distribution network. Given the lack of studies in the current literature on this topic, this research aims to fill this gap by examining the potential effects of TBESS integration on PDs, such as reclosers and fuses, within a DS. Utilizing a model based on real data from a Brazilian utility, we conducted simulations to analyze the effects of TBESSs in both charging and discharging modes on the protection systems of three feeders. The methodology involved assessing variations in the operation times and coordination of PDs to determine if TBESS integration would necessitate adjustments to existing protection configurations. The results demonstrated that TBESS integration resulted in only minor variations in PD operating times, typically within hundredths of a second, indicating a negligible impact on protection performance. Consequently, no significant modifications to the protection system are required to accommodate TBESSs. These findings suggest that TBESSs can be seamlessly integrated into existing distribution networks, maintaining system reliability and operational integrity. This study provides valuable insights and a robust procedure for utilities to analyze the integration of TBESSs, supporting the effective deployment of modern energy storage solutions in DSs. [ABSTRACT FROM AUTHOR]

Grid-forming inverters (GFMIs) have been identified as critical assets in ensuring modern power system reliability. Their ability to synthesize an internal voltage reference while emulating synthetic inertia has sparked extensive research. These characteristics have recently piqued interest in their capacity to provide blackstart ancillary services. The blackstart of a bulk power system poses significant challenges, namely the large transients from the energization of unloaded transformers, rotational motor loads, and long transmission cables, which have been effectively studied using conventional synchronous generators (SGs). The concept of an inverter-based resource (IBR)-based blackstart continues to be an open research area necessitating further investigations due to the known limitations of IBRs such as low short-circuit current capabilities. This paper presents a blackstart case study of a bulk power system investigating the performances of a conventional SG to a GFMI when utilizing hard switching methods. The paper qualitatively investigates the transient inrush currents from the transformer and rotational load energization sequences. Additional examinations into the significance of the GFMI's current-limiting schemes and voltage control loop compensator gains are presented. Furthermore, the harmonic distortions from the transformer energization sequence are also evaluated. Finally, a full network energization case is presented to demonstrate how both sources can provide blackstart provisioning services. The models are developed in EMTDC/PSCAD using real-world transmission planning data. [ABSTRACT FROM AUTHOR]

Probability of detection (POD) is an acknowledged mean of evaluation for many investigations aiming at detecting some specific property of a subject of interest. For instance, it has had many applications for Non-Destructive Evaluation (NDE), aimed at identifying defects within structural architectures, and can easily be used for structural health monitoring (SHM) systems, meant as a compact and more integrated evolution of the former technology. In this paper, a probability of detection analysis is performed to estimate the reliability of an SHM system, applied to a wing box composite spar for bonding line quality assessment. Such a system is based on distributed fiber optics deployed on the reference component at specific locations for detecting strains; the attained data are then processed by a proprietary algorithm whose capability was already tested and reported in previous works, even at full-scale level. A finite element (FE) model, previously validated by experimental results, is used to simulate the presence of damage areas, whose effect is to modify strain transfer between adjacent parts. Numerical data are used to verify the capability of the SHM system in revealing the presence of the modeled physical discontinuities with respect to a specific set of loads, running along the beam up to cover its complete extension. The POD is then estimated through the analysis of the collected data sets, wide enough to assess the global SHM system performance. The results of this study eventually aim at improving the current strategies adopted for SHM for bonding analysis by identifying the intimate behavior of the system assessed at the date. The activities herein reported have been carried out within the RESUME project. [ABSTRACT FROM AUTHOR]

Probability-arisen models play a considerable role in preparing a crucial stage for decision-making concerning reliability, engineering, and more closely related scenarios. Bearing in mind the consequential roles of probability-arisen models, we introduce and implement a new probabilistic model that has arisen by using the sine function, namely, the sine very flexible Weibull (SVF-Weibull) distribution. The proposed SVF-Weibull distribution is a result of a combination of the very flexible Weibull distribution with the sine-based strategy. For the SVF-Weibull distribution, point estimates are obtained. The assessment of the point estimates of the SVF-Weibull distribution is done via a simulation study. Finally, the consequential role of the SVF-Weibull distribution, illustrated by considering reliability and music engineering data sets. Furthermore, we implement some machine learning tools for predicting the reliability and music engineering data sets. The performances of the machine learning tools are assessed across many hidden variables. Our findings suggest that the artificial neural network method is more optimal than other methods for predicting the reliability and music engineering data sets. [ABSTRACT FROM AUTHOR]

Power quality is a critical issue in power systems that can affect the performance and reliability of sensitive equipment. The dynamic voltage restorer (DVR) is a widely used technology for improving power quality, but it mainly focuses on mitigating voltage sags and often neglects the issue of harmonic distortion. This paper proposes a DVR voltage compensation strategy based on the discrete Fourier transform (DFT). This strategy utilizes the DFT to analyze the voltage waveform, separating the fundamental and desired harmonic components. With this strategy, the DVR can effectively compensate for voltage sag and achieve selective harmonic compensation, thereby improving the power quality of the power system. Additionally, a method utilizing hardware circuitry and an LED as a timestamp is proposed to dynamically estimate digital siginal processor (DSP) resource occupancy rate in real‐time, ensuring effective compensation while minimizing resource occupancy. A 3 kW DVR experimental prototype was constructed and tested to evaluate the practicality of the proposed strategy. The simulation and experimental results show that the method proposed in this paper successfully realizes the compensation of voltage sag and harmonics in the power system, and improves the stability and reliability of the power system. [ABSTRACT FROM AUTHOR]

Corrosion is a major problem in the mining industry, significantly affecting the durability and reliability of mining equipment. In order to better evaluate and understand these effects, in the first part of this study, the analysis by the finite element method of the metallic structure of the rotary arm of an ERc 1400-30/7 bucket excavator type, produced in Romania in 1985, is presented. This paper presents an analysis of the impact of corrosion on the structural integrity of mining equipment. The focus of the research is on the use of advanced spectral analysis methods and the implementation of wavelet analysis. These methods allow an increase in the degree of accuracy of the information and results obtained. The study also focused on the impact of corrosion on the natural frequencies and structural changes in mining equipment, which were evaluated based on advanced methods of structural analysis. The obtained results show that corrosion deeply affects the dynamic behaviour of the metallic structure, leading to significant changes in the dominant frequencies. It was found that the measured dominant frequencies show a tendency to increase with the increase of structural degradation, an observation that is consistent with the physical aspects of the phenomenon. [ABSTRACT FROM AUTHOR]

Tool holders are one of the most important structures in transferring machine tools and energy for manufacturing in CNC lathe. Power servo tool holders influence kinematic accuracy and machining accuracy and so are vital to the transposition system. Reliability evaluation is also critical to guaranteeing and maintaining the accuracy of the transposition system. The first four statistical moments are derived to depict the transmission error and system characteristics. Considering the Edgeworth expansion with higher terms, reliability and reliability-based sensitivity evaluations using moments are proposed to assess system accuracy. Compared with different methods, the proposed method can represent higher statistical characteristics, helping to avoid underestimations of system reliability. Also, results calculated with the proposed method for the transposition system are in agreement with the results from the Monte Carlo simulation with 107 samples. The relative error of failure probability is 4.32%. Considering the plus–minus sign and values of results, reliability-based sensitivity represents the effects of the parameters' dispersions on system reliability. The reliability-based sensitivity indices can be utilized to optimize the system structure and to improve system accuracy, which can increase the system reliability from 98.34% to 99.99% in the transposition system of the power servo tool holder. [ABSTRACT FROM AUTHOR]

In true triaxial compression tests, all three principal stresses are imposed independently. This allows for a more comprehensive analysis of the material's mechanical properties. The end effect in true triaxial compression tests is a crucial phenomenon that impacts the accuracy and reliability of the test results. In this study, a series of true triaxial compression tests is conducted to examine the influence of the end friction on the mechanical properties. The laboratory results show that the presence of the end friction could bring about an apparent increase in rock strength and also restrict the deformation in each direction showing that the stiffness (the slope of the curves) increased slightly. The rock strength σ1,peak ${\sigma }_{1,\text{peak}}$ could be enhanced from 24.7%(σ2=σ3) $ \% \,({\sigma }_{2}={\sigma }_{3})$ to 90.7% $ \% $ (σ2=σ1 ${\sigma }_{2}={\sigma }_{1}$) when the end friction is increased, which is mainly caused by the lateral interface friction. The failure mode and fracture angle of the specimen are also influenced by the end effect, showing that under high friction conditions, the failure is more ductile, and a larger fracture angle is observed. At last, in comparison with the published experimental data, the actual specific friction angle 11° $11^\circ $ (corresponding friction coefficient is about 0.19) for the direct specimen–metal contacts in a true 3D test is numerically identified, which is empirically reasonable and higher than the tested range 0.146–0.157 obtained from double‐shear test system. [ABSTRACT FROM AUTHOR]

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

The excited, one-degree-of-freedom mechanical system with symmetrical and asymmetrical rigid constraints, is investigated in a two-parameter bifurcation plane ω–b. Decision conditions for the chattering-sticking motion of the system are given. The analytical solution of P(1, 1, 1) periodic motion and the linearization matrix of the Poincaré map are derived. The bifurcation characteristics of the P(1, 1, 1) periodic motion are analyzed theoretically by the position of the eigenvalue passing through the unit circle. The multiplicity and evolution of periodic motions of the system with symmetrical and asymmetrical constraints are explained by zones of their occurrence and stability in the plane of ω–b. Special attention is given to the complex dynamics near the singular points Si and the mutual transfer law of adjacent P(1,r,r) motion. The singular point is a codimensional-2 grazing bifurcation point related to P(1,r,r) type, and also a codimensional-2 flip-fold bifurcation point related to P(1,r + 1,r + 1) motion. The mutual evolution of adjacent P(1,r,r) motion is reversible only at the singular point Si. For symmetrical system, motions P(1,r + 1,r), P(2, 2r, 2r + 1), P(3, 3r + 1, 3r + 1) and their evolutions emerge in the ligulate zone. For an asymmetrical system, a cluster of abnormal basic groups gathers in the low-frequency. The symmetrical constraint model is a special case of the asymmetrical model. The obtained results have reference value for reducing component wear, noise, and improving the operational reliability of mechanical systems with clearance couplings. [ABSTRACT FROM AUTHOR]

When assessing the reliability of a system or the viability of a material, it is necessary to consider the stress conditions of the operating system. Let X be the strength of the system and Y be the stress of the system caused by the environment. In a stress–strength model, the system’s reliability is defined as P(X>Y), and due to ignoring environmental shocks or human failure, it is usually overestimated. To underestimate reliability, we assume that when X goes to the environmental conditions, the system’s strength reduces to ZX, where Z is a random variable on (0,1). We propose a modified reliability model with Ru=P(ZX>Y) for the gamma distribution. In this model, stress Y and the scale mixture of the strength of the gamma distribution, ZX, are modeled as independent random variables. The proposed model is compared with the traditional model through a simulation study. Additionally, the asymptotic distribution is used to construct an asymptotic confidence interval for Ru. Finally, we use two real datasets to demonstrate the practical applicability of our proposed method. [ABSTRACT FROM AUTHOR]

AbstractThis study introduces a novel approach to reinforcing notched aluminum plates using functionally graded composites (FGCs). By integrating volume fraction principles of Functionally Graded Materials (FGMs) into fiber-matrix mixture laws, we propose an advanced patch reinforcement strategy. The research employs the Cohesive Zone Model (CZM) and Extended Finite Element Method (XFEM) in ABAQUS to simulate patch debonding, crack initiation, and propagation. We compare linearly graded and FGM patches against non-graded counterparts, demonstrating superior load distribution and damage tolerance of graded composites. Three gradation concepts are explored: C-1 (peak fiber density at mid-thickness), C-2 (increased fiber density near patch edge), and C-3 (highest fiber density adjacent to the adhesive joint). Results reveal a critical interplay between adhesive debonding and crack propagation in the aluminum substrate. The study highlights the efficacy of graded composite patches in mitigating damage and offers significant insights into advanced reinforcement techniques for enhanced structural durability and reliability in engineering applications. [ABSTRACT FROM AUTHOR]

The ultra-high voltage direct current (UHVDC) transmission composed of modular multilevel converters (MMC) is an important technology for large-scale centralized transmission of renewable energy. In UHVDC system, temporary faults in the AC power grid system are a high probability fault, and the fault control strategy affects the safety and reliability of system operation. This paper studies on the condition of AC power grid faults occurring at the receiving converter station. Firstly, study the system characteristics after the occurrence of AC faults, and use theoretical analysis to derive the trend of DC voltage changes of each converter valve. Then, an AC fault ride-through control strategy with high power transmission capability is proposed with hierarchical connection structure, the strategy controls the system to synchronously reduce DC voltage and AC active power after a fault, maximizing the retention of the system's transmission capacity during the occurrence of faults, thereby reduce power shock in the system. Finally, a simulation model of the dual ended system has built based on the PSCAD simulation platform. The simulation results show that when a single-phase ground fault and a three-phase ground fault occur in the high valve group at the receiving station, the system can retain about 83% and 50% of the transmission capacity during the fault period, respectively. Meanwhile, there is no serious overvoltage or overcurrent phenomenon in the system. The simulation results verified the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The effective management of reactive power plays a vital role in the operation of power systems, impacting voltage stability, power quality, and energy transmission efficiency. Despite its significance, suboptimal reactive power planning (RPP) can lead to voltage instability, increased losses, and grid capacity constraints, posing risks to equipment and system reliability. Rigorous RPP methodologies can mitigate these challenges, resulting in tangible improvements in voltage profiles, system stability, and reduced losses. A comprehensive review of 20 technical articles published between 2020 and 2023 was conducted to compare and synthesize contributions to the field of RPP. The review highlighted the efficacy of strategic RPP approaches in reducing power losses, minimizing equipment malfunctions, and improving power quality, leading to substantial economic benefits—strategic planning approaches and integrating emerging technologies. For instance, examples include renewable energy sources and energy storage systems, which offer promising avenues for enhancing RPP and ensuring stability, reliability, and efficiency in power systems. [ABSTRACT FROM AUTHOR]