Reliability Optimization Research Articles

Systematic Reliability Optimization (ASRO)

Reliability optimization is a critical aspect of modern engineering systems and products, particularly with the growing complexity and interconnectedness of systems. This paper delves into the significance of reliability optimization and the techniques employed to achieve it. It highlights the benefits of optimizing reliability, including reduced costs, enhanced customer retention, and a competitive advantage. The paper discusses the challenges of balancing performance, cost, and reliability, especially in real-world systems with intricate nonlinear interactions between subcomponents. It introduces various reliability optimization techniques, including redundancy analysis, physics-based models, accelerated testing, and data-driven methods. The paper emphasizes the potential of advanced sensing and AI-based methods for reliability optimization. It highlights the importance of AI in optimizing the design and management of complex cyber-physical systems (CPS), where failures can have severe economic and safety consequences. The paper also discusses the empirical review of over 50 studies since 2016, which provides insights into the effectiveness of various optimization approaches across industry verticals. In conclusion, reliability optimization is crucial for the development and operation of modern engineering systems and products. Advanced sensing and AI-based methods offer promising solutions for optimizing reliability in complex systems, particularly CPS. By systematically optimizing reliability, companies can reap significant benefits and ensure the successful operation of their products and services.

Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM

The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations.

Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability

Image perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test–retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test–retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test–retest model. Similar optimal reliability can be achieved with testing AUC = 0.7–0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test–retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.

Workflow Scheduling Scheme for Optimized Reliability and End-to-End Delay Control in Cloud Computing Using AI-Based Modeling

In the context of cloud systems, the effectiveness of placing modules for optimal reliability and end-to-end delay (EED) is directly linked to the success of scheduling distributed scientific workflows. However, the measures used to evaluate these aspects (reliability and EED) are in conflict with each other, making it impossible to optimize both simultaneously. Thus, we introduce a scheduling algorithm for distributed scientific workflows that focuses on enhancing reliability while maintaining specific EED limits. This is particularly important given the inevitable failures of processing servers and communication links. To achieve our objective, we first develop an artificial intelligence-based model that merges an improved version of the wild horse optimization technique with a levy flight approach. This hybrid approach enhances the ability to explore new possibilities effectively. Additionally, we establish a viable strategy for sharing mapping decisions and stored information among processing servers, promoting scalability and robustness—essential qualities for large-scale distributed systems. This strategy not only boosts local search capabilities but also prevents premature convergence of the algorithm. The primary goal of this study is to pinpoint resource placements that strike a balance between global exploration and local exploitation. This entails effectively harnessing the search space and minimizing the inclination toward resources with a high likelihood of failures. Through experimentation in various system configurations, our proposed method consistently outperformed competing workflow scheduling algorithms. It achieved notably higher levels of reliability while adhering to the same EED constraints.

Factoring Interacting Stress Mechanisms in Design for Reliability of Extreme Environment Power Modules

As power densification demands are placing electronic packages under greater reliability risk, the consequence of complementary or interacting stresses in producing failure are becoming increasingly significant. As such, it is important that reliability methods and package designs consider how multiple-stress interactions may impact product life. Here, the coordination between a novel accelerated testing method and electronic design automation efforts has demonstrated a successful optimization approach for a wire-bonded 2D module layout combining failure mechanisms of electromigration and mechanical stressing. Utilizing custom, physics-of-failure approaches in accelerated testing, interactions can be observed in failure acceleration, which then can be incorporated into design for reliability (DfR) optimization tools. The PowerSynth 2 platform has been utilized as a design for reliability tool to perform a rapid relative reliability evaluation incorporating multi-stress scenarios. This work demonstrates the value added to reliability evaluation techniques when accounting for interacting failure mechanisms and suggests that next generation power devices consider these effects in lifetime estimation.

Optimization of the aero-engine thermal management system with intermediate cycle based on heat current method

Reliability and efficiency of the aero-engine thermal management system (ATMS) is of great importance to ensure the required working condition. Conventional methods on modeling ATMS are complex and difficult to solve for optimizing the whole system under multiple conditions. This study combines the heat current method and artificial neural network (ANN) to develop the optimization models with the objectives of maximum heat transfer rate and minimum thermal conductivity respectively. The ATMS experimental platform with intermediate cycle using water as the simulated medium is constructed to verify the optimization reliability. After optimization, the maximum heat transfer rate of ATMS is increased from 7740 W to 8402 W by 8.6%. The minimum thermal conductivity decreases from 1628 W/K to 1032 W/K by 36.6% comparing with the initial working condition. While considering the conditions of artificial control on the fuel oil side, the optimal thermal conductivity decreases from 1628 W/K to 1101 W/K by 32.3%. The optimal findings provide powerful guidance for improving the heat dissipation and reducing weight of the aircraft.

Reliability Optimization of Hybrid Systems Driven by Constraint Importance Measure Considering Different Cost Functions

The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between constraint conditions and objective functions in the optimization problem. To better consider the contribution of the CIM, this paper considers three different cost function forms, including power type, trigonometric type, and exponential type. Combining the global search ability of the arithmetic optimization algorithm (AOA) with the local search ability of the CIM, a CIM-based arithmetic optimization algorithm (CIAOA) is developed to analyze the contribution of the CIM. Through the numerical experiments, the optimal system reliability and convergence generation of the CIAOA and AOA under different cost function forms are regarded as the indexes to analyze algorithm performance. The experimental results show that the average system reliability improvement percentages under power type, trigonometric type, and exponential cost constraint are 8.07%, 0.14%, and 0.53%, respectively, while the average convergence improvement percentages under three cost forms are 37.30%, 0.08%, and 1.66%, respectively. Therefore, the CIAOA performs the best under power cost constraints. Finally, a numerical example of a hybrid power vehicle system is introduced to analyze the contribution of the CIM under different cost functions by considering the reliability improvement rate in the optimal solution and the ranking of the CIM. The higher prioritization components in the two rankings are similar, which shows that the component with higher a CIM is selected to improve its reliability.

Flexibility index for a distributed energy system design optimization

Flexibility plays a critical role in the design of distributed energy systems (DESs) as it encompasses various aspects related to demand, storage, and supply. To optimize the capacity configuration of DESs effectively, a novel flexibility index (FI) is proposed in this study. The FI is constructed based on the fuzzy best-worst method with considerations for economy, autonomy, energy efficiency, and environmental friendliness, aligning with the characteristics of the DES. Considering FI, particle swarm optimization is employed to determine the optimal design scheme for the DES. A case study involving a swimming pool in Changsha City is conducted to demonstrate the reliability of the proposed optimization scheme. Furthermore, a multi-objective optimization model based on non-dominated sorting genetic algorithm-II and technique of ordering preferences for ideal solution similarity algorithms is developed with different objective functions. The results show that the system optimized considering flexibility maintains better performance, with system flexibility, renewable energy penetration, and off-grid degree indices of 0.824, 0.780, and 0.757 respectively. In addition, the optimization of system configuration considering flexibility can dynamically respond to diverse energy demands, maintaining lower operation and maintenance costs ($11223.75 per year), and lower CO2 emissions (91800kgCO2 per year). The quantified FI presented in this study provides a user-friendly and reliable optimization index for the design of DESs.

Reliability control and calculation for agri-equipments based on an intelligent algorithm

In the research, an agricultural machinery reliability analysis method based on fusion algorithm is proposed, a optimal radial basis function neural network and M-C statistical test method are mixed to obtain an agricultural machinery reliability. This mixed model is used to reliability design and calculation of a cotton picker, the simulation model of reliability control and calculation for a cotton picker based on the mixed algorithm is set up, and reliability of the level spindle of a cotton picker is computed through the mixed method, and the effect of important factors on the cotton picker is predicted. The level spindle is critical force-bearing parts of a cotton picker and breakdown occurs frequently, their reliability control and optimization are key problems that need to be solved urgently, this study builds an innovative approach for the reliability optimization and design of agricultural equipments.

Reliability analysis and optimization of the hammer rotor of forage crusher under multiple failure modes

Reliability analysis and optimization of the hammer rotor of forage crusher under multiple failure modes

Factoring Interacting Stress Mechanisms in Design for Reliability of Extreme Environment Power Modules

As power densification demands are placing electronic packages under greater reliability risk, the consequences of complementary or interacting stresses in producing failure are becoming increasingly significant. As such, it is important that reliability methods and package designs consider how multiplestress interactions may impact product life. Here, the coordination between a novel accelerated testing method and electronic design automation efforts has demonstrated a successful optimization approach for a wire-bonded 2D module layout combining failure mechanisms of electromigration and mechanical stressing. Utilizing custom, physics-of-failure approaches in accelerated testing, interactions can be observed in failure acceleration, which then can be incorporated into design for reliability (DfR) optimization tools. The PowerSynth 2 platform has been utilized as a DfR tool to perform a rapid reliability evaluation incorporating multistress scenarios. This work demonstrates the value added to reliability evaluation techniques when accounting for interacting failure mechanisms and suggests that next-generation power devices consider these effects in lifetime estimation.

Reliable Data Transmission Scheduling for UAV-Assisted Air-to-Ground Communications

Reliability is significant for unmanned aerial vehicle (UAV)-assisted air-to-ground (A2G) communications. In this letter, we address the reliability optimization problem by properly scheduling data transmissions under Non-Line-of-Sight (NLoS) channel fading. We capture the channel fading effect by the Rayleigh distribution and then formulate the A2G communication reliability from a probabilistic perspective. We propose a constrained optimization model that jointly takes into account the impacts of the aerial and ground nodes' mobility, the stochastic fading characteristics of the A2G channel, and the transmission constraints. From the model, we derive a closed-form reliability-optimal solution for data transmission scheduling and theoretically characterize the optimal reliability that is achievable. Simulation results verify our theoretical results and show the superior performance of the proposed scheduling solution over benchmark methods.

An Advanced Optimal Algorithm for the Reliability Optimization Problem of a Series System with Multiple-Choice using Cost-to-Reliability Approach

An Advanced Optimal Algorithm for the Reliability Optimization Problem of a Series System with Multiple-Choice using Cost-to-Reliability Approach

Theoretical and Experimental Analysis of Osmotically Assisted Reverse Osmosis for Minimum Liquid Discharge.

Osmotically assisted reverse osmosis (OARO) is an innovative process that shows promising potential in the treatment of brine produced by conventional reverse osmosis (RO) systems. This study presents a theoretical and experimental analysis of the OARO process, focusing on its application to achieve minimum liquid discharge (MLD). This theoretical analysis includes the development of a mathematical model to describe the transport phenomena occurring during OARO. By considering mass balance equations coupled with transport equations, the theoretical model allows for the simulation of a full-scale system consisting of a single-stage RO and a four-stage OARO. Experimental investigations are also conducted to validate the theoretical model and to evaluate the performance of the OARO process. A laboratory-scale OARO system is designed and operated using a synthetic RO brine. Various operating conditions, including applied pressure, feed concentration, and draw concentration, are varied to investigate their effects on process performance. The experimental results demonstrate the feasibility of OARO as an MLD solution and also validate the predictions of the theoretical model, confirming its reliability for process optimization and design. The results of the theoretical analysis show that OARO has the potential to significantly improve water recovery compared to conventional RO. Based on the simulation, the optimal operating conditions are explored, leading to a significant reduction (up to 89%) in the volume of brine discharge.

PID Control Algorithm Based on Particle Swarm Optimization for Quadrotor UAV with Tip Defect

For the four-rotor UAV (Unmanned Aerial Vehicle), blade is one of the most important actuator, the four-rotor UAV is prone to blade tip defect during use, which will directly affect the reasoning size of the four-rotor UAV, resulting in the flight quality or performance decline of the four-rotor UAV, ordinary PID control in the case of blade tip defect, it can still be optimized by other algorithms. In this paper, particle swarm optimization will be used to optimize PID parameters in the case of tip defect of quadrotor UAV, and simulation experiments will be conducted in MATLAB Simulink to verify the reliability of particle swarm optimization by comparing and optimizing data curves such as forward and backward roll Angle and yaw Angle.

Reliability optimization in IRS-assisted UAV networks

Unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) are regarded as critical technologies which need to be developed for the sixth-generation mobile network (6G). In this paper, we study the reliability of the IRS-assisted Unmanned aerial vehicles (UAV) communication system with limited UAV energy in complex urban scenarios. We present the problem by joint optimization of the IRS's scheduling, the UAV's trajectory, the IRS's phase shift, and the UAV's transmit power for maximizing the reliability of the transmission link under the constraints of UAV energy and channel error rate. The concavity of the problem is difficult to judge, and the problem is hard to find a substitution function to convert it into a tractable form. Therefore, we design a chaotic adaptative hybrid whale optimization algorithm (CAHWOA) to address the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the variational perturbation binary whale optimization algorithm (VPBWOA). CAWOA and VPBWOA are used for solving the continuous variable subproblem and the binary variable subproblem, respectively. The numerical results show that the joint optimization of IRS and UAV improves the transmission link reliability by nearly 30% compared with the two baseline schemes. CAHWOA can improve the convergence rate by about 18% compared with the baseline algorithms. Meanwhile, it can enhance the optimization-seeking accuracy by about 0.04.

Reliability modeling and optimization of K/N phased mission system with backup missions and global redundancy strategy

AbstractThe mission success probability (MSP) is a critical indicator for phased mission systems (PMSs). In the modern aerospace industry, redundancy techniques, including component/phase redundancy, are commonly seen to increase the MSP of the whole system. These component/phase redundancies make the reliability analysis more complex. Meanwhile, one or more components are required for normal working for different subsystems, called the K/N structure. In this article, a Markov‐process method is proposed for PMS with K/N subsystems and different redundancy strategies. Then, a universal system optimization model is proposed to optimize system structure and redundancy strategies for all subsystems at the same time. Then, an improved genetic algorithm (GA) is used to resolve the optimization problem. At last, a propulsion system is used as an engineering case, showing the proposed binary decision diagram‐based method.

Optimum number of cascade cells for multilevel inverter considering redundancy strategies based on cost and reliability optimization

AbstractThis paper proposes an economical reliability‐oriented solution to determine the optimal number of cascade cells and redundancies for STATCOM using a Non‐Dominated Sorting Genetic (NSGA‐II) algorithm based on different redundancy configuration schemes. A multi‐objective optimization framework is presented, to minimize the total cost and maximize its reliability index by considering power quality issues. In the presented model, a new cost modelling is introduced considering the cost of inductor loss. The multilevel cascade topology with staircase modulation strategy is also taken into account. The effectiveness of the proposed optimization framework is validated by the NSGA‐II algorithm compared to the results of goal attainment and goal programming as decomposition single‐objective optimization methods. The results verify that the proposed multi‐objective optimization framework could be useful in the optimal selection of high‐voltage STATCOM levels in the presence of redundancies while ensuring the required level of reliability at the minimum cost. Also, the presented NSGA‐II optimization algorithm is applicable to maintain both objective functions at an acceptable value.

A quantitative analysis of MIMO channel performance in new generation networks: Prospects and limitations

The objective of this article is to analyze the behavior of channels in mobile radio transmission chains and propose a solution for reducing propagation attenuation and ensuring Quality of Service (QoS) in telecommunications. As network coverage and throughput demands continue to grow, a thorough characterization of channels is essential. The primary contribution of this work is to investigate the different phases of the digital transmission chain jointly to achieve optimal reliability and QoS. Spatial and temporal representations must be considered at the same level when characterizing and modeling MIMO system channels. Accurate and realistic modeling can be achieved through transmission channel characterization in the spatial domain, which is critical in a MIMO context to improve throughput and performance compared to conventional systems. The article explores the behavior of information carriers in the propagation environment and the impact of fading, diversity, multiplexing, and multipath phenomena.

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.