Reliability Optimization Research Articles

Novel study on active reliable PID controller design based on probability density evolution method and interval-oriented sequential optimization strategy

Vibration active control is a significant problem in practical engineering, but the influence of hybrid uncertainties in practical engineering cannot be ignored. For the uncertainties with adequate data, probability theory can be used while for the uncertainties with insufficient data, probability theory is limited and interval quantification can be used. Taking the widely used proportional-integral-differential (PID) control as an example, a novel study on controller design based on probability density evolution method and interval-oriented sequential optimization strategy considering interval and random uncertainties is proposed. Firstly, under a certain sample point of interval uncertainties, probability density evolution equation is utilized to calculate the probability density function of uncertain response considering only random uncertainties. In this way the probabilistic reliability corresponding to the sample point can be obtained. Then the hybrid reliability is defined as the average probability within the interval uncertainty and the adaptive dichotomy method is utilized to choose proper sample points of interval uncertainties, in which the reliability curve and the interval uncertainty axis is regarded as the judgement of convergence. Next the hybrid reliability is introduced to the optimization as a constraint and the PID gains reliable design optimization is established to balance the energy-consuming and safety. Finally, the sequential optimization strategy is adopted to improve the efficiency of optimization. In every iteration step of the optimization, the constraint of deterministic optimal design is adjusted according to the static reliability at the moment before the passage or reaching the maximum value. Three numerical examples are presented to demonstrate the accuracy and practicability of the proposed framework.

Replica fault-tolerant scheduling with time guarantee under energy constraint in fog computing

Fog computing offers stronger local computing power and reduces data transmission load, making it an ideal solution to meet the energy-saving and efficient requirements of intelligent connected vehicle applications. As intelligent networked vehicles and vehicle–road collaboration technologies advance rapidly, optimizing scheduling under fog computing architecture has become a prominent research area. However, existing studies primarily concentrate on parallel task scheduling with low energy consumption or high real-time performance, failing to address the requirement for high reliability in intelligent networked vehicle scenarios. To achieve time and reliability optimization for vehicular applications in fog computing architecture, this paper proposes a fog computing task scheduling algorithm and explores its extension using replication techniques. Subsequently, the algorithm underwent evaluation utilizing randomly generated directed acyclic graph models as well as real-life automotive application instances. The experimental findings indicate that in comparison to existing methods, the proposed algorithm exhibits a notable improvement in reliability while ensuring time optimization, thereby demonstrating a distinct level of advancement and practicality.

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL.

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal-mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model's robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy

Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.

Optimization of reliability and sensitivity analysis for flange structures

The study delves into the reliability-driven optimization design of the flanges, drawing from reliability design theory, advanced optimization methodologies, and comprehensive sensitivity analysis. Flanges, integral to mechanical systems, demand precise design. Reliability design theory emphasizes consistent operation under diverse conditions. A novel approach to the flange-specific reliability sensitivity analysis method is central to this research. It promises to revolutionize flange design. By understanding the first two moments of core random parameters, we expedite reliability-driven optimization designs for mechanical connections. The programs enhance our approach, streamlining the pursuit of reliability-driven designs and providing rapid, accurate sensitivity analysis data for mechanical connections. This data offers critical insights into parameter influence, enabling targeted design adjustments. In summary, this research represents a significant advancement in mechanical engineering. By integrating reliability design theory with cutting-edge methodologies, it promises to enhance the quality and reliability of mechanical connections, meeting the rigorous demands of contemporary engineering applications. This holistic approach will play a pivotal role in the future of mechanical component design.

Research on optimal design method of dynamic stress parameters of helical gear based on orthogonal experimental design

Aiming at the reliability optimization design problem of two-grade helical cylindrical gear transmission system, the UG parameterized model based on expression and ADAMS dynamic simulation model under real working conditions are integrated in ISIGHT platform, and the distribution law of peak contact force on the tooth surface of helical gear pair in the transmission system at different meshing times is obtained. Considering the uncertainty of material properties, load conditions and geometric parameters, the sample set was generated according to the orthogonal experimental design method, and the Kriging approximation model was established. On this basis, the contact strength reliability of helical gear was analyzed. The mathematical model of meshing efficiency reliability optimization of helical gear pair was established and solved by Hooke-Jeeves Direct Search method. The results show that the meshing efficiency and reliability of the optimized helical gear pair are improved, and the peak value of the contact force is obviously reduced.

The Role of Oxygen Vacancy and Hydrogen on the PBTI Reliability of ALD IGZO Transistors and Process Optimization

The Role of Oxygen Vacancy and Hydrogen on the PBTI Reliability of ALD IGZO Transistors and Process Optimization

A prediction model for drilling temperature of CFRP/Ti stacks and green cooling strategy considering chip ejection process

Drilling temperature has always been a hot issue in the drilling process of CFRP/Ti stacks. Accurately predicting drilling temperature is necessary for reliable optimization of drilling parameters. However, since the drilling process involves two materials and may affect each other, the prediction of temperature during drilling is still a challenge. In this paper, a novel drilling temperature model is established to predict the temperature distribution in CFRP workpiece during the drilling process of CFRP/Ti stacks, considering the minor cutting edge and titanium alloy chip ejection process. Specifically, according to different drilling phases, in addition to considering the heat source of the cutting zone of different materials, the heat source generated by the interaction of minor cutting edge and rebound hole wall of CFRP and the heat source formed by the thermal-mechanical effect of Ti chip ejection process are also considered. The results show that the prediction errors of peak temperature in CFRP entrance and stack interface region during drilling are within 15.8 % and 10.9 %, respectively, which indicates the validity of the model. Finally, the green cooling strategy (High and low frequency compound vibration-assisted drilling) is adopted to significantly reduce the peak temperature in the drilling process by controlling the chip morphology and ejection process, which further proves that the thermo-mechanical action of Ti chips on CFRP cannot be ignored, and provides reference for temperature modeling and low-damage processing of CFRP/metal stacks.

Application of the FMECA Method for Optimizing the Reliability of the 1600T Press

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[Formula: see text] 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[Formula: see text] company can be addressed by a systematic approach to industrial maintenance. By adopting a holistic approach to industrial maintenance, Algal[Formula: see text] 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.

Denaturing mass photometry for rapid optimization of chemical protein-protein cross-linking reactions

Chemical cross-linking reactions (XL) are an important strategy for studying protein-protein interactions (PPIs), including low abundant sub-complexes, in structural biology. However, choosing XL reagents and conditions is laborious and mostly limited to analysis of protein assemblies that can be resolved using SDS-PAGE. To overcome these limitations, we develop here a denaturing mass photometry (dMP) method for fast, reliable and user-friendly optimization and monitoring of chemical XL reactions. The dMP is a robust 2-step protocol that ensures 95% of irreversible denaturation within only 5 min. We show that dMP provides accurate mass identification across a broad mass range (30 kDa–5 MDa) along with direct label-free relative quantification of all coexisting XL species (sub-complexes and aggregates). We compare dMP with SDS-PAGE and observe that, unlike the benchmark, dMP is time-efficient (3 min/triplicate), requires significantly less material (20–100×) and affords single molecule sensitivity. To illustrate its utility for routine structural biology applications, we show that dMP affords screening of 20 XL conditions in 1 h, accurately identifying and quantifying all coexisting species. Taken together, we anticipate that dMP will have an impact on ability to structurally characterize more PPIs and macromolecular assemblies, expected final complexes but also sub-complexes that form en route.

APPLICATION OF ARTIFICIAL INTELLIGENCE FOR SOLUTION OF ENGINEERING PROBLEMS. ADVANTAGES AND CHALLENGES

Artificial Intelligence (AI) is becoming an integral part of modern engineering, promising to transform the ways of design, production, and system management. Its application ranges from process automation to optimization of production cycles, enhancing efficiency and reliability. However, despite the advantages, there are challenges such as AI integration into the design phase, safety requirements, and data confidentiality. Especially in the EPC industry, where each project has unique requirements, and high safety standards complicate AI implementation. Additionally, the need for qualified professionals and effective data collection mechanisms create further obstacles. Successful AI implementation requires the integration of company experience, a strategic approach, and support from senior management.

Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression.

During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios.

Radiographic findings in patients suspected of subacromial impingement syndrome: prevalence and reliability

ObjectiveAims were to (i) report prevalence and (ii) evaluate reliability of the radiographic findings in examinations of patients suspected of subacromial impingement syndrome (SIS), performed before a patient’s first consultation at orthopaedic department.Materials and methodsThis cross-sectional study examined radiographs from 850 patients, age 18 to 63 years, referred to orthopaedic clinic on suspicion of SIS. Prevalence (%) of radiographic findings were registered. Inter- and intrarater reliability was analysed using expected and observed agreement (%), kappa coefficients, Bland–Altman plots, or intraclass coefficients.ResultsA total of 850 patients with a mean age of 48.2 years (SD = 8.8) were included. Prevalence of the radiographic findings was as follows: calcification 24.4%, Bigliani type III (hooked) acromion 15.8%, lateral/medial acromial spurs 11.1%/6.6%, acromioclavicular osteoarthritis 12.0%, and Bankart/Hill-Sachs lesions 7.1%. Inter- and intrarater Kappa values for most radiographic findings ranged between 0.40 and 0.89; highest values for the presence of calcification (0.85 and 0.89) and acromion type (0.63 and 0.66). The inter- and intrarater intraclass coefficients ranged between 0.41 and 0.83; highest values for acromial tilt (0.79 and 0.83) and calcification area (0.69 and 0.81).ConclusionCalcification, Bigliani type III (hooked) acromion, and acromioclavicular osteoarthritis were prevalent findings among patients seen in orthopaedic departments on suspicion of SIS. Spurs and Bankart/Hill-Sachs lesions were less common. Optimal reliabilities were found for the presence of calcification, calcification area, and acromial tilt. Calcification qualities, acromion type, lateral spur, and acromioclavicular osteoarthritis showed suboptimal reliabilities. Newer architectural measures (acromion index and lateral acromial angle) performed well with respect to reliability.

Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning

Efficient allocation of resources is crucial for optimizing wireless networks that face constraints in bandwidth, power, and spectrum. This paper proposes a decentralized reinforcement learning (RL) model that departs from traditional centralized paradigms to revolutionize resource optimization. The proposed model empowers individual wireless devices with autonomous decision-making capabilities, enhancing adaptability and scalability by leveraging Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The innovative integration of memory mechanisms facilitates learning from past experiences, addressing the dynamic nature of wireless environments. This decentralized RL model offers practical implications for improved efficiency, adaptability, and reliability in wireless network resource optimization. By transforming individual devices into collaborative decision-makers, our proposed model contributes to a resilient and responsive wireless communication infrastructure. The specific contributions of this paper include the pioneering use of DQN and PPO algorithms within a multi-agent system, offering a groundbreaking solution for dynamic resource optimization in wireless networks.

IoT Based ICU Healthcare: Optimizing Patient Monitoring and Treatment with Advanced Algorithms

In the realm of IoT-based Intensive Care Unit (ICU) healthcare, the quest for precision and reliability in patient monitoring and treatment optimization is paramount. This study delves into the realm of advanced algorithms, particularly focusing on the Pelican Optimization Algorithm Long Short-Term Memory (POA-LSTM), known for its remarkable accuracy rates exceeding 95%. The POA-LSTM algorithm, fine-tuned through the Pelican Optimization Algorithm, emerges as a beacon of accuracy in ICU healthcare. By optimizing hyperparameters and leveraging the Pelican Optimization Algorithm's optimization prowess, POA-LSTM surpasses industry standards, offering unparalleled precision and recall rates. Its ability to make informed predictions and provide real-time insights significantly enhances the quality of patient care and clinical decision-making in ICU settings. Additionally, the study explores Context-Oriented Attention LSTM (COA-LSTM) and Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) algorithms, each contributing unique strengths to the landscape of IoT-based ICU healthcare. COA-LSTM's attention mechanism and PSO-LSTM's hyperparameter optimization further enrich the capabilities of predictive modeling and anomaly detection in critical care scenarios. Through the integration of these advanced algorithms, healthcare providers can harness the power of data-driven insights to revolutionize ICU healthcare, ensuring optimal patient outcomes and advancing the frontier of medical care in the digital age.

Optimisation of the Distribution System Reliability with Shielding and Grounding Design Under Various Soil Resistivities

Lightning strikes can cause equipment damage and power outages, so the distribution system's reliability in withstanding lightning strikes is crucial. This research paper presents a model that aims to optimise the configuration of a lightning protection system (LPS) in the power distribution system and minimise the System Average Interruption Frequency Index (SAIFI), a measure of reliability, and the associated cost investment. The proposed lightning electromagnetic transient model considers LPS factors such as feeder shielding, grounding design, and soil types, which affect critical current, flashover rates, SAIFI, and cost. A metaheuristic algorithm, PSOGSA, is used to obtain the optimal solution. The paper's main contribution is exploring grounding schemes and soil resistivity's impact on SAIFI. Using 4 grounding rods arranged in a straight line under the soil with 10 Ωm resistivity reduces grounding resistance and decreases SAIFI from 3.783 int./yr (no LPS) to 0.146 int./yr. Unshielded LPS has no significant effect on critical current for soil resistivity. Four test cases with different cost investments are considered, and numerical simulations are conducted. Shielded LPSs are more sensitive to grounding topologies and soil resistivities, wherein higher investment, with 10 Ωm soil resistivity, SAIFI decreases the most by 73.34%. In contrast, SAIFIs for 1 kΩm and 10 kΩm soil resistivities show minor decreases compared to SAIFIs with no LPS. The study emphasises the importance of considering soil resistivity and investment cost when selecting the optimal LPS configuration for distribution systems, as well as the significance of LPS selection in reducing interruptions to customers.

Reliability analysis and optimization design of magnetic fluid dynamic seal shell structure under thermal/mechanical load

In order to study the reliability of Magnetic fluid dynamic seal shell structure under thermal/mechanical load, the improving cooling structure, calculating fuzzy fatigue reliability, and collaborative optimization design of fatigue reliability and lightweight are studied in this paper. The temperature model of the shell structure under four temperature conditions is established to analyze the influence of the thermal deformation caused by excessive local temperature. The cooling structure is improved, and the maximum temperature under the maximum temperature condition is reduced by 40.154 °C. For the sake of calculating the reliability of the new shell structure, a strength analysis model under thermal/mechanical load is built, and the fuzzy fatigue reliability function is obtained based on Latin hypercube sampling and the second-order response surface method. Four kinds of reliability are acquired by coding the functional functions and four kinds of membership functions with MATLAB, but all of them are lower than 90 % of the industrial requirements. To improve the reliability of the new shell structure and carry out the anti-fatigue lightweight design at the same time, an approximate model was constructed based on the interval analysis method and second-order response surface method for analysis. The accuracy of the approximate model was verified by re-sampling and finite element analysis. Based on the non-dominated sorting genetic algorithm, the optimal solution is determined. The optimization results show that the stress target value is reduced by 19.3 %, the weight target value is reduced by 3 %, and the fuzzy fatigue reliability reaches 94.65 %, 94.58 %, 93.89 %, and 96.31 % respectively. A new cooling structure of the Magnetic fluid dynamic seal is obtained and the maximum temperature is reduced. Moreover, the reliability of the new shell structure under thermal/mechanical load coupling is improved.

Optimization of a Ge2Sb2Te5-Based Electrically Tunable Phase-Change Thermal Emitter for Dynamic Thermal Camouflage.

Controlling infrared thermal radiations can significantly improve the environmental adaptability of targets and has attracted increasing attention in the field of thermal camouflage. Thermal emitters based on Ge2Sb2Te5 (GST) can flexibly change their radiation energy by controlling the reversible phase transition of GST, which possesses fast switching speed and low power consumption. However, the feasibility of the dynamic regulation of GST emitters lacks experimental and simulation verification. In this paper, we propose an electrically tunable thermal emitter consisting of a metal-insulator-metal plasmonic metasurface based on GST. Both optical and thermal simulations are conducted to optimize the structural parameters of the GST emitter. The results indicate that this emitter possesses large emissivity tunability, wide incident angle, polarization insensitivity, phase-transition feasibility, and dynamic thermal camouflage capability. Therefore, this work proposes a reliable optimization method to design viable GST-based thermal emitters. Moreover, it provides theoretical support for the practical application of phase-change materials in dynamic infrared thermal camouflage technology.

A Shorter Version of the Revised Francis Psychological Type and Emotional Temperament Scales (FPTETS-R)

Abstract: The Francis Psychological Type and Emotional Temperament Scales (FPTETS) operationalize the psychological-type model of personality alongside emotional temperament. The scales have been widely used in research as continuous variables that explain a wide range of religious beliefs and attitudes. The full instrument consists of five 10-item scales so a shorter version would be useful in longer surveys where completion time needs to be minimized. This study uses data from 700 Church of England clergy who completed the revised version of the FPTETS to reduce the 10-item scales to 6-item scales. Ant colony optimization was found to be a better way of selecting the final items than reliability optimization alone because it balanced individual scale reliabilities with maintaining the factor structure of the overall instrument. The selected scales were validated using data from 1,194 lay people from the Church of England, and two samples of 884 clergy and 2,765 lay people from the Episcopal Church (USA). The short scales are commended for use where the need is for continuous scale scores rather than producing psychological typologies.