Reliability Of Engineering Systems Research Articles

Graph Neural Network Approach to System-Level Health Index and Remaining Useful Life Estimation

Current methods for predicting health index and remaining useful life (RUL) in complex systems struggle to account for performance dependencies between components, leading to inaccurate system-level estimates. This research proposes a novel approach utilizing graph neural networks (GNNs) to improve system-level health index and RUL estimation. GNNs excel at capturing complex interdependencies within a system, making them ideal for this task. The proposed methodology is designed for systems with synchronously sampled process data. To illustrate the application of the proposed approach, we will use the Condensate Extraction Subsystem (CES) of a nuclear power plant (NPP) as a case study. Sensor data like temperature, pressure, and flow rates will be used to train GNNs to predict the overall health and RUL of the CES over time. To evaluate the effectiveness of GNNs, a custom NPP simulator will be used to model the CES under various realistic fault modes across a variety of components. The GNN's performance will be verified and its robustness will be tested under diverse scenarios. This research aims to demonstrate the effectiveness and resilience of GNNs for system-level prognostics. By providing valuable insights for maintenance decision-making, this approach can enhance operational reliability and safety in complex engineering systems. The proposed framework has the potential to be applied across various industries, leading to advancements in predictive maintenance practices.

Band Matrices, Markov Chains, and the Reliability of Engineering Systems

Band Matrices, Markov Chains, and the Reliability of Engineering Systems

Time-dependent reliability analysis of aerospace electromagnetic relay considering hybrid uncertainties quantification of probabilistic and interval variables

Reliability is a crucial metric in aerospace engineering. The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems. Current methods for analyzing the reliability of aerospace electromagnetic relays have limitations, such as neglecting the combined effects of multiple uncertain factors, degradation of key component properties, and the influence of fluctuations in aerospace environments. Additionally, these methods often assume a single-type uncertainty in the manufacturing process, leading to significant deviations between the analysis results and actual measurement results. To address these issues, this study proposes an efficient time-dependent reliability analysis method based on the HL-RF algorithm, considering a hybrid of probabilistic and interval uncertainty that accounts for degradation and environmental conditions. The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods, demonstrating significant advantages. The proposed method has been applied to the time-dependent reliability analysis of actual aerospace electromagnetic relay products under different environmental conditions. The analysis results exhibit an error margin within 5.12% compared to actual measurement results. Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantification, this method reduces the analysis error by 52% and 67% respectively. When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification, the error reduction is 23%. These demonstrate the superiority of the proposed method and validates its effectiveness. The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.

Task-orientated probabilistic damage model with interdependent degradation behaviors for RUL prediction of traction converter systems

Remaining useful life (RUL) prediction is crucial for the safety and reliability of engineering systems with stringent requirements, like high-speed trains. Previous studies on fixed or phased conditions ignore the effects of time-varying operations on survivability. Additionally, the stochastic dependence among units is often neglected. To address the above problems, a task-oriented probabilistic damage model with interdependent degradation behaviors (TPDM-IDB) and an RUL prediction method based on TPDM-IDB are proposed. The model integrates three relations: the fundamental relation from load to damage, the task-oriented relation from task to load, and the interdependence relation. First, the degradation process and variability of the failure unit are tracked based on failure physics, mapping load to damage. Next, a task-oriented thermal loading translation is proposed to dynamically update load under time-varying operations. Then, to address the underestimation of life caused by interdependent degradation behaviors, the degradation factor is adjusted by thermal load redistribution. Finally, considering the failure stochasticity, a decision simulation method based on structural reliability is employed to predict the RUL. The effectiveness of the proposed method is validated through simulation examples and actual case studies. Notably, the method uses system tasks as inputs, which is more suitable for practical engineering applications.

A Bayesian design method for monopropellant engine system reliability qualification test plan

During the development of systems, traditional System Reliability Qualification Testing (SRQT) is typically utilized to assess whether they meet predefined reliability standards. However, this approach often demands substantial sample sizes and prolonged test durations, rendering it impractical for costly, highly reliable systems with limited sample sizes. Additionally, the extended test duration may not align with practical time-to-market pressures or budget constraints. To overcome these challenges, the study integrates subsystem data into the monopropellant engine system RQT plan's design. By leveraging Monte-Carlo simulation, subsystem data is modeled to create a system parameter distribution, enabling the formulation of SRQT plans based on posterior risks. An example of a monopropellant liquid rocket engine system is provided to demonstrate the advantages and applications of the proposed methodology.

The LPST-Net: A new deep interval health monitoring and prediction framework for bearing-rotor systems under complex operating conditions

Accurate and science-based prediction of bearing performance degradation has been a principal concern and a critical challenge issue in the sector of Prognostics and Health Management in the industry as the solution to promote the engineering system's reliability, availability, and maintainability. Deep Learning (DL) methods are currently a hotspot in Prognostics and Health Management. Nonetheless, with complex operating conditions, existing forecast models still inevitably suffer from three fatal flaws. Firstly, dynamic modern industrial systems and harsh operational environments make the degradation data of the bearing-rotor system highly stochastic and nonlinear. Secondly, in practice, the bearing-rotor system failure process will be governed by complex failure dynamics and degradation mechanisms, resulting in degradation data characterized by intense temporal order. Thirdly, deep learning models use a multi-layer or cellular design containing numerous weights and biases, generating more computational overhead. To fill this research gap, a new deep interval health monitoring and prediction framework named Lightweight Probabilistic Spatiotemporal (LPST-Net) is proposed, which is integrated with the concepts of lightweight and interval prediction and is capable of state monitoring and Remaining Useful Life (RUL) prediction for bearing-rotor systems under complex operating conditions. Mainly inspired by the improvement of the Gate Recurrent Unit (GRU), this paper designs a time series variable prediction algorithm and derives a new formulation named Weight Diminish Recurrent Unit (WDRU). It dramatically reduces the training parameters of the proposed LPST-Net framework and improves the convergence speed while ensuring prediction accuracy. The degradation data are obtained under 2 actual and complex operating conditions of bearing-rotor system unbalance and high temperature. The three metrics show that the proposed LPST-Net framework can achieve high-precision point prediction, suitable prediction intervals, and reliable probabilistic prediction results. It is also verified that the proposed LPST-Net framework has superior performance and more practical application value compared with seven mainstream methods, such as (b)Squeeze-WDRU-GPR-Net.

Evaluation of reliability of complex engineering systems using risk acceptance criteria

The reliability evaluation of gas pipeline systems is one of the most important tasks, since the safety of gas supplies determines the reliable operation of the whole system.Purpose: This paper presents the evaluation methodology based on the risk acceptance criteria established in accordance with the current rules and standards.Methodology: A three-component methodology is used to evaluate the reliability level of gas pipeline systems. It is implemented by creating a model of failure consequences, which accounts for environmental conditions and pipeline properties. Acceptable probabilities of the pipeline failure are determined using the risk theory. Based on these data, the model is designed to assess the gas transport reliability in the pipeline system with regard to failure probabilities and hydraulic characteristics.Research findings: The reliability level of the system is determined using the acceptable failure probability. The obtained reliability is compared with that of the real pipeline system. The best procedure is proposed to maintain and improve the system reliability.

TALICS3: Tape library cloud storage system simulator

High performance computing data is surging fast into the exabyte-scale world, where tape libraries are the main platform for long-term durable data storage besides high-cost DNA. Tape libraries are extremely hard to model, but accurate modeling is critical for system administrators to obtain valid performance estimates for their designs. This research introduces a discrete–event tape simulation platform that realistically models tape library behavior in a networked cloud environment, by incorporating real-world phenomena and effects. The platform addresses several challenges, including precise estimation of data access latency, rates of robot exchange, data collocation, deduplication/compression ratio, and attainment of durability goals through replication or erasure coding. Using the proposed simulator, one can compare the single enterprise configuration with multiple commodity library configurations, making it a useful tool for system administrators and reliability engineers. This makes the simulator a valuable tool for system administrators and reliability engineers, enabling them to acquire practical and dependable performance estimates for their enduring, cost-efficient cold data storage architecture designs.

ARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE LEARNING ALGORITHMS

This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively. Concluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements.
 Keywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration.

Stochastic analysis of structural reliability of complex engineering systems

Purpose: The assessment of risks and prediction of reliability of complex engineering systems, in particular onshore gas pipelines subjected to external corrosion. Two methods are proposed using the structural reliability analysis.Methodology: Two strategies are considered for inspection and maintenance service of hazardous production facilities.Research findings: The model of structural reliability is proposed to estimate the metal rupture from external corrosion of the pipe section. Models are presented for its mechanical failure with respect to stochastic processes of loads and resistance.Value: The inhomogeneous Poisson point process is used to simulate the formation of new defects, and the Poisson distribution is used to simulate their growth. The first method focuses on the analysis of external corrosion of gas pipelines with metal loss and predicts rupture at a reference pipe segment, constructed with respect to average pipe rupture characteristics from the PHMSA database. The second model predicts reliability for untreated sections subject to external corrosion with metal loss.

Uncertainty quantification based on residual Tsallis entropy of order statistics

<abstract><p>In this study, we focused on investigating the properties of residual Tsallis entropy for order statistics. The reliability of engineering systems is highly influenced by order statistics, for example, when modeling the lifetime of a series system and the lifetime of a parallel system. The residual Tsallis entropy of the ith order statistic from a continuous distribution function and its deviation from the residual Tsallis entropy of the ith order statistics from a uniform distribution were investigated. In the mathematical framework, a method was provided to represent the residual Tsallis entropy of the ith order statistic in the continuous case with respect to the case where the distribution was uniform. This approach can provide insight into the behavior and properties of the residual Tsallis entropy for order statistics. We also investigated the monotonicity of the new uncertainty measure under different conditions. An investigation of these properties leads to a deeper understanding of the relationship between the position of the order statistics and the resulting Tsallis entropy. Finally, we presented the computational results and proposed estimators for estimating the residual Tsallis entropy of an exponential distribution. For this purpose, we derived a maximum likelihood estimator.</p></abstract>

Life cycle management of the ventilation system of a construction object

Introduction. In the article, the author considers a mechanical ventilation system as a complex technical system that can be effectively controlled based on a model of its life cycle. Like any engineering system (or product, product, project), a ventilation system has its own life cycle, consisting of a set of successive stages. Each stage is characterized by its own types of work and final results that require management decisions.Materials and Methods. The paper uses the method of analyzing the life cycle of complex technical systems, methods of collecting and processing statistical data, as well as methods of systematic and comparative analysis, generalization of scientific and practical results.Results. In the course of the conducted research, the author applied the "life cycle management" approach to ventilation systems and achieved the goal of developing a model of their life cycle, which includes all stages of system development from conception to disposal. Possible ways of managing the life cycle of ventilation systems from the perspective of continuous interconnection of processes are determined.Discussion and Conclusion. Effective life cycle management of a mechanical ventilation system can be implemented through the development of a software product capable of modeling processes and system elements even at the first stages of the life cycle. The software product should eliminate the existing problem of inconsistencies at different stages of work, store information about the object and provide access to it for each participant in the process. Solving the problem of creating a software product will ensure effective management of the entire life cycle of the ventilation system, reduce labor costs and inconsistencies and ensure compliance with modern requirements for operational reliability and energy efficiency of a vital engineering system.

Entropy generation analysis and optimization of cooling systems in industrial and engineering operations

AbstractEfficient cooling is crucial for maintaining the reliability and performance of industrial and engineering systems that generate excess heat. Entropy generation analysis, established on the second law of thermodynamics, plays a vital role in identifying inefficiencies within these systems and improving their overall efficiency. This study focuses on a theoretical investigation of the entropy generation, considering the cumulative impact of surface slipperiness, Brownian motion, applied magnetic field, thermophoresis on the nanofluid flow toward a convective heated stretching surface. The governing model partial differential equations undergo a transformation through similarity transformation, resulting in a nonlinear ordinary differential equation. This equation is subsequently solved numerically by employing the Runge–Kutta–Fehlberg integration scheme in conjunction with the shooting method. The obtained results reveal the influence of various parameters on the temperature, velocity, Nusselt number, skin friction, Sherwood number, Bejan number, nanoparticle concentration, and entropy generation. Upon analysis, it was notable that the introduction of a magnetic field, higher Biot numbers, Eckert numbers, and elevated Brownian motion led to an increase in the entropy generation within the system. Conversely, the presence of thermophoresis and reduced surface slipperiness resulted in a decrease in entropy. These results are presented through graphical representations, tables, and quantitative discussions, providing valuable insights for optimizing the cooling and performance of industrial and engineering systems.

Modified control variates method based on second-order saddle-point approximation for practical reliability analysis

Abstract. A novel method is presented for efficiently analyzing the reliability of engineering components and systems with highly nonlinear complex limit state functions. The proposed method begins by transforming the integral of the limit state function into an integral of a highly correlated limit state function using the control variates method. The second-order reliability method is then employed within the control variates framework to approximate the highly correlated limit state function as a quadratic polynomial. Subsequently, the probability of failure is obtained through the estimation of the saddle-point approximation and a small number of samples generated by Latin hypercube sampling. To demonstrate the effectiveness of the proposed method, four examples involving mathematical functions and mechanical problems are solved. The results are compared with those obtained using the second-order reliability method (SORM), control variates based on Monte Carlo simulation (CVMCS) with second-order saddle-point approximation (SOSPA), importance sampling (IS) and Monte Carlo simulation (MCS). The findings demonstrate that, while maintaining high-precision reliability results, the proposed method significantly reduces the number of evaluations of the limit state function through a small number of initial samples. The method is capable of efficiently and accurately solving complex practical engineering reliability problems.

Універсальний сплайн-пертурбаційний розподіл

This paper considers the problem of probability distribution construction for a random variable from known numerical characteristics. The problem is of importance in determining the parametric reliability of engineering systems when the numerical characteristics (in particular, the bias and the kurtosis) of an output parameter (state variable) are determined by analytical methods and its distribution must be recovered. This may be done using a four-parameter universal distribution, which allows one to cover certain ranges (preferably, as wide as possible) of the bias and kurtosis coefficients using a single analytical form. The most familiar universal distribution is Gram-Charlier’s, which is a deformation of the normal distribution obtained using a Chebyshev-Hermite orthogonal polynomial expansion. However, in the general case, Gram-Charlier’s distribution function is not a steadily increasing one. For some combinations of the bias and kurtosis coefficients, the density curve may exhibit negative values and multiple modes. Because of this, a search for other universal distributions to cover wider ranges of the bias and kurtosis coefficients is of current importance. The paper analyzes a method of universal probability distribution construction by multiplying the normal density by a perturbing polynomial in the form of a spline (referred to as the spline-perturbed distribution). The idea of a distribution of this type was proposed earlier to account for a nonzero bias coefficient. The spline is constructed based on Hermite’s interpolating polynomials of the third degree with two knots, which have a minimum of parameters and possess a locality property The basic distribution is constructed for a four-knot spline. The paper further develops and generalizes the spline-perturbed distribution to nonzero bias and kurtosis coefficients. Two cases are considered. The first case is a composition of two splines that have four and five knots, respectively. The former and the latter allow one to account for the bias and the kurtosis, respectively. Integral equations are obtained to find the values at the knots of both splines and construct the distribution. The second case is more general and uses one five-knot Hermite spline. The paper shows a way to construct a generalized spline-perturbed distribution without any negative density values or any multiple modes. The knot points are chosen using an enumerative technique. Conditions for the absence of negative density values and multiple nodes are identified.

Temporal noisy-adder of bayesian network for scalable consecutive-k-out-of-n:F system reliability analysis

The Bayesian network(BN) is a powerful tool for modeling and analyzing reliability, safety, and risk in various engineering systems. Although there have been studies that incorporate it into the modeling of Consecutive-k-out-of-n: F systems(C(k,n:F)), these applications are limited to naive mode and cannot tackle scalable systems (e.g., n>2000,k>40). By introducing temporal Noisy-adder structure, named NaCkoon model, the number of nodes in equivalent C(k,n:F) BN model is significantly reduced, and the Conditional Probability Table (CPT) of temporary nodes can be controlled to linear complexity. The correctness of the proposed algorithm is validated by the comparison of literature case. Through numerical experiments, the novel algorithms proposed in this paper were compared with existing ones, including BN construction and inference computation time, was found to be the most optimal solution. Finally, the computable ranges of novel and existing algorithms are obtained through the capability computation. The test results show that NaCkoon model proposed in this paper has great advantages over existing algorithms and proposed NaA+NaO model based BN.

Optimization Of The Use Of ATC Automation Approach Control Surveillance At Perum LPPNPI MATSC Branch

ATC (Air Traffic Control) automation system is a complex system, which helps maintain air traffic order, guarantee flight intervals, and prevent aircraft collisions. This is important to ensure the safety of air traffic. Failure effect evaluation is an important part of ATC automation system reliability engineering. However, changes like these also present challenges and ethical considerations that need to be considered to ensure that aviation safety remains a top priority. In optimizing the use of ATC Automation, research was carried out using qualitative analysis, which means that in this study, data, interviews and documents related to the problem were used. The data used is the result of interviews submitted to the ATC. In conducting this research, the study was conducted in May 2023 using descriptive analysis. The intended purpose of this study is to find out that the use of ATC Automation has been running optimally and has also reduced workload. Accurate in dealing with complex traffic situations. This research is also expected to be used as further research in the use of ATC Automation.

Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses

The rhythmic and stable operation of trolleybuses and autonomous trolleybuses or urban electric buses, depends to a large extent on the reliability of the equipment installed on the trolleybus. The actual operational reliability of trolleybus electrical equipment (EE) depends on its technical condition. Under the influence of external factors and specific operating modes, the technical condition of the equipment is continuously deteriorating, reliability indicators are decreasing, and the number of failures is increasing. Using the mathematical theory of reliability, probability theory and mathematical statistics, numerical methods of solving nonlinear and transcendental equations, this article defines the conditions of diagnostics depending on the intensity of failures and the given probability of failure-free operation of the equipment. Additionally, the inverse problem of determining the current reliability of electrical engineering systems depends on the terms of diagnostics and the intensity of failures being solved. As a result of the processing of statistical information on failures it is established that for the electrical equipment of a trolleybus, after a number of repair measures, the maximum density of failures occurs at a lower mileage, and the probability of failure-free operation can vary depending on the degree of wear of the equipment, i.e., on the number of previous failures. It is theoretically substantiated and experimentally confirmed that the reliability of trolleybus electrical equipment changes according to the exponential law of distribution of a random variable. It has been established that the real averaged diagnostic terms regulated by instructions are not optimal in most cases and differ several times from those defined in this paper. The dependence of switching equipment run-in on time has been clarified, which served as a prerequisite for specifying the inter-repair period for various types of trolleybus electrical equipment. A method of adjustment of the inter-repair time for the electrical equipment of trolleybuses is proposed.

Efficient Solvers for Nonlinear Partial Differential Equations in Computational Fluid Dynamics

Improving the computational efficiency of solving nonlinear partial differential equations (PDEs) is a key area of focus for this research, which has the ability to significantly impact computational fluid dynamics (CFD) simulations. The complexities of fluid flow phenomena can be difficult for traditional methods to handle, leading to simulations that are resource-intensive and computationally slow. The urgent requirement for improved computational tools capable of quickly and precisely simulating complex fluid dynamics scenarios is the driving force behind the significance of this research. New approaches are needed to solve nonlinear partial differential equations (PDEs) in computational fluid dynamics (CFD), which are characterized by turbulent flows, fluid-structure interactions, and high computing resource intensity. Adaptive mesh optimization and high-performance computing architectures are combined in the AMO-HPI technique, which provides a strategic solution. Adaptive Mesh Optimization based on High-Performance Integration (AMO-HPI) is suggested in this research as a way to improve the accuracy of solutions by focusing computational resources on regions of interest through adaptive mesh refinement and coarsening. Making effective use of shared-memory parallelism and message-passing interfaces (MPI) to divide up computational tasks. Improvements in engineering system performance, reliability, and safety can be achieved by the effective simulation of fluid dynamics processes, which opens up new design areas that were previously unreachable. When compared to other methods, AMO-HPI performs better in simulations due to its optimal resource utilization and decreased processing time. In addition to improving CFD methodology, this research could have far-reaching effects on engineering design processes in several industries, which would be great for innovation and sustainable development.

Preface

Striving to achieve carbon peaking by 2030 and carbon neutrality by 2060 is a major strategic development decision for China at present. Among them, building a new power system with new energy as the main body is an important step to promote the clean and low-carbon energy transition and build a better China. To achieve the double carbon goal, energy, power and grid are all indispensable and important forces.Following this concept, the 2023 3rd International Conference on Power Grid Systems and Green Energy (PGSGE 2023) was held during January 6th to 8th, 2023 in Xiamen, China (virtual event). The conference aimed to encourage information exchange from the frontiers of energy grid research, connect the most advanced academic resources in China and the world, transform research results into industrial solutions, bring together talents, technologies and capital, and promote development.This volume contains the papers presented at PGSGE 2023. The papers cover various topics including Power Engineering, High Voltage Systems, Power System Reliability, Energy and Power Engineering, Energy Efficiency and Management, Heat and Mass Transfer Theory, etc. have been through rigorously reviewed by experts and meet the requirements of publication.The online conference attracted about 50 individuals, and four sophisticated professors performed their keynote speeches and shared with us their research experience and professional insights during the conference. The topics of keynote speech included: a): Advanced Predictive Control for Electric Energy Conversion Systems; b) Theory and Application of Wireless Power Transfer; c): Smart Energy Systems; d) Lithium-Ion Battery Simulation Models for Storage Systems in Renewable Power Generation. All the keynote speakers made brilliant speeches combing their own research domains and experiences, and interacted with the audience for the questions raised and better academic communication.On behalf of the Conference Organizing Committee, we would like to extend our sincere acknowledgement to all the keynote speakers, peer reviewers, and all the participants. Particularly, our heartfelt thanks and appreciation go to the Editors and Managers, Journal of Physics: Conference Series for their help and kind cooperation during the preparation of the proceedings. Hopefully, all participants and other interested readers can benefit significantly from the proceedings and find it rewarding.The Committee of PGSGE 2023List of Committee Member is available in this Pdf.