Reliability Of Complex Systems Research Articles

Deep learning-based proactive fault detection method for enhanced quadrotor safety

The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencoder and BiLSTM classification components, effectively reduces feature dimensions, thereby enhancing fault detection accuracy. The autoencoder’s latent layer identifies prominent features across various dimensions, while BiLSTM classification conducts bidirectional analysis using these features from both healthy and faulty states, facilitating early fault detection. Experimental results demonstrate the model’s efficacy, achieving an accuracy of 79.48% in predicting incipient faults 30 seconds before a serious malfunction occurs. This underscores the significant potential of the proposed framework in enhancing operational safety and reliability in complex systems. Moreover, the study emphasizes the importance of leveraging comprehensive data and advanced analysis techniques for early fault detection.

A dynamic system reliability analysis model on safety instrumented systems

This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.

Reliability analysis of subsea manifold system using FMECA and FFTA

Subsea manifold system is a complex system that occupies a pivotal role in contemporary ocean engineering and has a significant impact on the safety of marine resource exploitation. Reliability technology plays a significant role in ensuring the safe operation of the subsea manifold system. To perform a comprehensive analysis of the reliability of complex systems, a combination method of FMECA-FFTA (Failure Modes, Effects and Criticality Analysis - Fuzzy Fault Tree Analysis) is introduced in this study. Firstly, FMECA is used to accomplish a qualitative analysis of system reliability considering multifactorial failure modes, which included analyzing potential failure modes, causes of system failure, and evaluating the degree of hazard to the system through a risk matrix diagram. Then, FFTA is applied to build a fault tree model to divide the system into “system-subsystem-component” and determine the minimal cut sets for quantitative analysis of system reliability. In addition, fuzzy set theory is incorporated to improve the accuracy of handling uncertainty in quantitative reliability analysis. Finally, a qualitative and quantitative reliability analysis is conducted by using FMECA-FFTA method for subsea manifold system. The failure modes of the subsea manifold system are clearly identified, including high-risk modes such as external leakage, medium-high-risk modes such as fail to close/lock on demand, and medium-risk modes such as leakage of critical location, plugged, and effective measures should be taken to focus on preventive protection and regular testing for the high risk, medium-high risk and medium risk modes.

Dynamic human reliability analysis method for nuclear power plant main control room based on SPAR-H and SD

Human reliability analysis (HRA) plays a vital role in probabilistic risk assessment (PRA) by quantifying the reliability and safety of complex technical systems through the estimation of human error probabilities (HEPs). Standardized plant analysis risk human (SPAR-H) reliability analysis is widely used for HRA; it adjusts the nominal HEP by assigning different multipliers to the performance shaping factors (PSFs). However, SPAR-H cannot capture the time-varying nature of HEPs; therefore, it cannot realistically respond to the accumulation and fluctuation of human risk factors across the mission. Hence, this study proposed a systematic method of dynamic HRA. It includes decision-making trial and evaluation laboratory-interpretative structural modeling (DEMATEL-ISM) to qualitatively and quantitatively analyze the hierarchical causality among the PSFs and their influence on each other based on their relative relationship in SPAR-H and construct hierarchical causality diagrams among the PSFs. Additionally, it uses a system dynamics method in conjunction with the analysis of time-dependent PSFs based on the hierarchical causality diagrams of PSFs to respond to the time-varying nature of HEPs. The simulation and rationale check results of the cases show that the proposed dynamic HRA method can realize a more realistic and dynamic estimation of HEPs.

Operational reliability assessment of complex mechanical systems with multiple failure modes: An adaptive decomposition-synchronous-coordination approach

To effectively perform Complex Mechanical Systems Operational Reliability Assessment (CSORA) with multiple failures, the Adaptive Decomposition-Synchronous-Coordination Approach (ADSCA) is proposed by integrating Decomposition-Synchronous-Coordination strategy, adaptive modeling technology, surrogate model and multi-objective reliability assessment theory. In which, the main problem is decomposed into related sub-problems using a Decomposition-Synchronization-Coordination strategy, with sub-models coordinated to achieve synchronous mapping between multiple variables, thereby enhancing efficiency in large multi-subsystem problems. The adaptive modeling technique is adopted to construct a series of interrelated sub models based on the surrogate model as the basis function, improving performance. A multi-objective reliability assessment theory is adopted to achieve CSORA under multiple fault modes, where multiple weak links are integrated to comprehensively evaluate the reliability of complex systems. The significance of the ADSCA lies in its ability to manage complexity, enhance scalability, and improve the accuracy and efficiency of reliability assessments in complex mechanical systems. To demonstrate the effectiveness of the proposed approach, two illustrative examples are used. This includes approximation and probability analysis of nonlinear functions that exhibit multiple responses, as well as reliability analysis of temperature in civil aircraft braking system. The study can provide theoretical reference for the reliability evaluation of multi-failure operation in complex mechanical systems.

Problems of the human factor in transport systems

Transport accident statistics and practical safety results indicate that technological solutions cannot ensure the viability (safety, sustainability, reliability and efficiency) of complex systems and structures without addressing human factors. This problem is especially acute for transport systems as highly complex technological and social structures aimed at ensuring the efficiency and safety of an entire sphere of human life. In transportation systems and technologies, the human factor plays a critical role. The successful operation of transport systems requires consideration of the human factor at all levels – starting from the development, design and planning of systems and technologies, training and education to the involvement of society in decision-making processes. Therefore, it is essential to develop the concept of human factor management and analyze the main problems in transport systems associated with the human factor to improve the safety, reliability and efficiency of their functioning. The article is devoted to the analysis of the human factor problem in transport systems and the search for solutions to manage it. The purpose of the article is to analyze the main problems in transport systems associated with the human factor and to develop the concept of human factor management for further search for solutions to improve the safety and reliability of transport systems. A set of instruments was proposed in the study to achieve the goal and solve the set tasks. Problems connected to the human factor in transport systems were analyzed. The main directions for solving problems with the influence of the human factor on the safety of transport systems were systematized. The main principles, models and ways of developing the concept of managing the human factor problem in complex systems, including transport, were outlined. A systematization of the risk management strategy was proposed. A matrix for the relationship between risk management strategies and approaches to managing the safety of complex systems was proposed. A new look at the principle of adaptive ergonomics as an implementation of the tolerance strategy was proposed, and applying the principle of adaptive ergonomics to transport systems was considered. The main motivation of the work is to attract the attention of the scientific and educational community to the problem of human factor and the need to use the concept of human factor management in the educational process to improve the general safety culture in society.

Exponential stability analysis of delayed partial differential equation systems: Applying the Lyapunov method and delay-dependent techniques

This paper presents an investigation into the stability and control aspects of delayed partial differential equation (PDE) systems utilizing the Lyapunov method. PDEs serve as powerful mathematical tools for modeling diverse and intricate systems such as heat transfer processes, chemical reactors, flexible arms, and population dynamics. However, the presence of delays within the feedback loop of such systems can introduce significant challenges, as even minor delays can potentially trigger system instability. To address this issue, the Lyapunov method, renowned for its efficacy in stability analysis, is employed to assess the exponential stability of a specific cohort of delayed PDE systems. By adopting Dirichlet boundary conditions and incorporating delay-dependent techniques such as the Galerkin method and Halanay inequality, the inherent stability properties of these systems are rigorously examined. Notably, the utilization of Dirichlet boundary conditions in this study allows for simplified analysis, and it is worth mentioning that the stability analysis outcomes under Neumann conditions and combined boundary conditions align with those of the Dirichlet boundary conditions discussed herein. Furthermore, this research endeavor delves into the implications of the obtained results in terms of control considerations and convergence rates. The integration of the Galerkin method aids in approximating the behavior of dominant modes within the system, thereby enabling a more comprehensive understanding of stability and control. The exploration of convergence rates provides valuable insights into the speed at which stability is achieved in practice, thus enhancing the practical applicability of the findings. The outcomes of this study contribute significantly to the broader comprehension and effective control of delayed PDE systems. The elucidation of stability behaviors not only provides a comprehensive understanding of the impact of delays but also offers practical insights for the design and implementation of control strategies in various domains. Ultimately, this research strives to enhance the stability and reliability of complex systems represented by PDEs, thereby facilitating their effective utilization across numerous scientific and engineering applications.

Spectral-interferometry-based diff-iteration for high-precision micro-dispersion measurement

Precise measurement of micro-dispersion for optical devices (optical fiber, lenses, etc.) holds paramount significance across domains such as optical fiber communication and dispersion interference ranging. However, due to its complex system, complicated process, and low reliability, the traditional dispersion measurement methods (interference, phase shift, or time delay methods) are not suitable for the accurate measurement of micro-dispersion in a wide spectral range. Here, we propose a spectral-interferometry-based diff-iteration (SiDi) method for achieving accurate wide-band micro-dispersion measurements. Using an optical frequency comb, based on the phase demodulation of the dispersion interference spectrum, we employ the carefully designed SiDi method to solve the dispersion curve at any position and any order. Our approach is proficient in precisely measuring micro-dispersion across a broadband spectrum, without the need for cumbersome wavelength scanning processes or reliance on complex high-repetition-rate combs, while enabling adjustable resolution. The efficacy of the proposed method is validated through simulations and experiments. We employed a chip-scaled soliton microcomb (SMC) to compute the dispersion curves of a 14 m single-mode fiber (SMF) and a 0.05 m glass. Compared to a laser interferometer or the theoretical value given by manufacturers, the average relative error of refractive index measurement for single-mode fiber (SMF) reaches 2.8×10−6 and for glass reaches 3.8×10−6. The approach ensures high precision, while maintaining a simple system structure, with realizing adjustable resolution, thereby propelling the practical implementation of precise measurement and control-dispersion.

DISCRETE-CONTINUOUS PROCESSES AS THE MATHEMATICAL BASIS OF THE RELIABILITY OF THE BEHAVIOR OF SUBSYSTEMS OF THE "DRIVER-VEHICLE-ROAD-ENVIRONMENT" SYSTEM

The work is devoted to the study of the reliability of the behavior of subsystems of the driver-car-road-environment system. According to the definition of a number of researchers, traffic safety is a complex and system of rules, measures and means that ensure safe traffic conditions, which are aimed at protecting and preserving the life and health of active and passive road users, as well as protecting and preserving the environment and property. Therefore, the question of studying all aspects of the operation of the "Driver-car-road-environment" system becomes acute. The general approach to the formation of reliability models of the B-A-D-S system consists in the fact that this model is described in a formalized form from the standpoint of the reliability of the interaction of subsystems in the process of functioning and reflects the degree of influence of the reliability of individual subsystems on the reliability of the system as a whole. Usually, reliability indicators of system elements (failure intensity) are determined on the basis of the laws of distribution of time of failure-free operation, and the concept of "failure" is clearly defined for each subsystem. Reliability models formed in terms of element failures are the main type of system reliability models. It follows that the set of combinations of possible states of elements determines the set of possible states of the system in general, which is called the space of system states (control theory). When writing the work, the research of domestic and foreign authors on the issues of the basics of reliability and risk assessment was studied. The purpose of the publication is to study approaches to ensuring the reliability of complex systems. It was determined that for all mechanical elements (car parts, road elements) the most suitable failure model is the DM-distribution, while the estimation of the coefficient of variation can be in the range from 0.3 to 0.7

A reliability analysis method for fuzzy multi-state system with common cause failure based on improved the weakest T-norm

Recently, the reliability analysis of complex engineering systems has been confronted with a range of challenges, including fuzziness, multiple states, and common cause failures. In particular, factors such as changes in the working environment and human error contribute to the fuzzy probability value for each state in the system, which brings great challenges to its reliability analysis. In this paper, a new reliability analysis method for fuzzy multi-state system with common cause failure is proposed, which developed the fault tree analysis method and fuzzy set theory. First, a model integrating common cause failure with fuzzy T-S fault tree analysis model has been illustrated, and the equivalent model is given. Meanwhile, a novel fuzzy arithmetic method based on the weakest n-dimension T-norm is designed to solve the proposed model. Finally, the aircraft power system is analyzed as an example to verify the effectiveness of the proposed method. The result shows that the proposed method effectively enhances the capacity of failure tree analysis in assessing the reliability of complex systems characterized by multiple states, fuzziness, and common cause failures.

Inferring failure coupling strength in complex networks through generative models

ABSTRACT The failure propagation process has always been a research hotspot in the field of complex system reliability. Most of the research studies focus on the inference of network structure, ignoring one of the most critical parameters for network reliability, failure coupling strength. Here, we develop a generative model describing the failure process to infer the failure coupling strength. Using this model, we generate the node failure sequences under different coupling strength and analyze the spatial properties of failure propagation. We find the failed nodes tend to appear near the previously failed nodes with increasing coupling strength. Based on our generative model, we propose a Bayesian inference method to infer the failure coupling strength from observed system instantaneous failure state data. We find the inferred values are close to the true values for small failure coupling strength. As failure coupling strength increases, error comes from the limits of failure propagation spatial data. We apply this Bayesian inference method to actual biological networks and infer the network failure coupling strength. Our proposed Bayesian inference method helps analyze the hidden failure mechanism from actual scenarios.

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Introduction. The assessment of the reliability and cost of complex systems, such as Complex Bridge Systems (CBS) and Life Support Systems in Space Capsules (LSSSC), is fascinating. To achieve the ideal system design through diverse constraints and increase overall system reliability, researchers have extensively explored system reliability and cost optimization problems. Hence, the significant advancement in metaheuristic methods is the primary source of further system reliability and cost optimization process refinement.
 Aim and tasks. This research attempts to enhance the reliability and cost of complex systems named CBC and LSSSC has been presented. 
 Results. The structure is based on few recent metaheuristic techniques, such as Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Gazelle Optimization Algorithm (GOA), Dragonfly Algorithm (DA), and Coati Optimization Algorithm (COA). Comparing the acquired findings to those found in other proposed techniques demonstrates the usefulness of a methodology based on COA. The proposed COA algorithm exhibits enhanced efficiency by offering superior solutions to reliability and cost-optimization problems. In addition, a non-parametric Friedman ranking was performed for validation. The results of this research are based on improving the reliability of the parameters and decreasing complex systems’ costs used by the five metaheuristic methods. Observing the convergence graph, Friedman ranking, statistical results test, and tables determined that COA is the most effective algorithm for a complex system’s cost and reliability parameters compared to other existing approaches, and also provided a faster solution.
 Conclusions. This study proposes unique ways to reduce costs while increasing parameter reliability in complex systems. After analysing the comparative solution, the authors found that when comparing these approaches (GOA, DA, MFO, WOA, and COA), the COA provided the best minimum solution for the cost and reliability of complex systems. Hence, the suggested COA procedure was more successful than that described in this study.

A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory

This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.

AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy

Structural reliability assessment is a popular topic in engineering problems, particularly for the larger and more complex systems with implicit performance functions, also called black-box problems. The reliability assessment for a black-box problem must continuously be computed using simulation models, such as the finite element model, a highly time-consuming process with a high computational cost. The adaptive Kriging has gained considerable attention over the past decade. The Kriging-based reliability assessment method reduces the computational cost to a great extent, on the premise of ensuring the accuracy of reliability assessment. However, many of the currently published system reliability assessment methods, construct adaptive Kriging models by reducing the probability of incorrect prediction of the system state, and do not make full use of the uncertainty of the system state prediction information. To this end, a new Kriging-based method for structural system reliability assessment is proposed in this study. First, the probabilities of incorrect or correct system state predictions were understood from the perspective of information entropy. Second, an active learning strategy is proposed based on information entropy theory. Finally, the advantages of the proposed method are demonstrated and highlighted through several numerical examples. The results show that the proposed method achieves a good balance between the accuracy and computational cost, and the numerical magnitude effect does not affect the computational cost. Moreover, this is an effective method for assessing the reliability of complex systems.

An Integrated Model for Risk Assessment of Urban Road Collapse Based on China Accident Data

With the deepening development and utilization of urban underground space, the risk of urban road collapse is becoming increasingly prominent, which is a serious threat to the safety of life and property. Therefore, the risk assessment of urban road collapse has vital significance for the safety management of cities. The main idea is to predict ongoing accidents by analyzing historical accident cases in depth. This paper explores the combination of Interpretative Structural Modeling (ISM) and Bayesian Networks (BNs) to construct a risk assessment model of road collapse. First, the main risk factors of road collapse and their coupling relationships are identified, which is used to increase the low reliability of complex systems. Then, the risk factors of road collapse are logically divided by ISM to obtain the BN hierarchy. Finally, the BN node probabilities are evaluated by the Expectation–Maximization (EM) algorithm using the collected 92 real road collapse accident cases. The model can be used to quantify the coupling strength and influence degree of each risk factor on the occurrence of road collapse accidents, which in turn can predict the probability of road collapse accidents in a given scenario. This study can provide a theoretical basis for urban safety management and reduce the risk of road collapse and potential loss of life and property, which is important for the sustainable development of societies.

A Historical Survey of Corrective and Preventive Maintenance Models with Imperfect Inspections: Cases of Constant and Non-Constant Probabilities of Decision Making

Maintenance strategies play a crucial role in ensuring the reliability and performance of complex systems. Imperfect inspections, characterized by the probabilities of false positives and false negatives, significantly impact the effectiveness of maintenance decisions. This survey explores maintenance models under imperfect inspections, characterized by constant and non-constant probabilities of false positives and false negatives. This study investigates various maintenance approaches, such as preventive and corrective maintenance, and evaluates their performance, considering the uncertainties introduced by imperfect inspections. By analyzing the existing literature and research findings, this survey provides valuable insights into the challenges and opportunities associated with maintenance decision making in the presence of inspection imperfections. The comparison between maintenance models with constant and non-constant probabilities of false positives and false negatives sheds light on the dynamic nature of these models, enabling a deeper understanding of their real-world applicability and effectiveness. This comprehensive overview is a valuable resource for researchers, practitioners, and decision makers involved in maintenance planning and optimization in diverse industrial sectors.

Selective transmit modeling framework of complex system reliability analysis considering failure correlation

To improve the efficiency and accuracy of complex system reliability analysis considering failure correlation, a selective transmit modeling framework (STMF) is proposed by integrating multi-stage selective modeling (MSM) strategy with multi-failure transmit modeling (MTM) architecture. In MSM, the modeling quality of multi-learner is discussed to establish the optimal functional relationship between multi-variable and multi-response by considering the suitability of modeling samples, and the fitting performance of copula functions is quantified to construct the optimal correlation model between multi-failure through distance measure. For MTM, the correlation sequence measurement structure of multiple modes is established to construct the joint reliability analysis model of complex structure by updating iteratively the margin distribution functions with the strongest correlation. Taking the turbine bladed disk as study object, the validation of the proposed STMF is verified by comparing with other methods. The analytical results of study case can be expressed as that (i) STMF holds the superior performance in modeling accuracy and time-consuming, at 2.6308 × 10-2 and 0.4638 s respectively; (ii) STMF also has the best property in simulation, separately at 2.2123 s in simulation efficiency and at 99.9899 % in simulation precision; (iii) the reliability 0.9876 of study case obtained by STMF is higher than the reliability 0.9855 that depends on the mutual independence assumption. The effort of this work is prominent to promote effectively the quantification of multi-failure correlation and the improvement of reliability analysis performance for complex systems.

On assessing the reliability of real-time systems at the operational stage

Information management systems operating in real time are characterized by increased reliability requirements. The problem of reliability is becoming increasingly important due to the constant complication of the systems being developed, the expansion of the range of tasks assigned to them, is complex and relevant in modern conditions. Reliability assessment methods and models are constantly being developed and refined, since reliability is one of the key quality problems of modern distributed real-time systems. The article discusses methods for ensuring the reliability of complex systems at the operational stage. Approaches for evaluating reliability indicators of complex systems at the operational stage are presented. The issues of ensuring the reliability of complex systems at the stage of operation are considered. The basic basic concepts of reliability are defined. Approaches for evaluating reliability indicators of complex systems at the operational stage are presented. The analysis of models and methods for assessing the reliability of complex systems at the operational stage is carried out.

SS-MASVM: An advanced technique for assessing failure probability of high-dimensional complex systems using the multi-class adaptive support vector machine

The integrity of structures in contemporary engineering practices is heavily influenced by inherent uncertainties, which necessitates the development of reliable structural reliability analysis methods for uncertainty quantification and probabilistic analysis. This paper investigates the integration of surrogate models and subset simulation (SS) to accurately compute the failure probabilities of high-dimensional complex systems. Focus is placed on enhancing the computational efficiency and accuracy through using the multi-class adaptive support vector machine (MASVM). The proposed SS-MASVM method incorporates more efficient learning functions, integrates K-means clustering, and employs SS technology for precise estimation of failure probabilities of high-dimensional complex systems. Numerical and engineering structural examples validate the effectiveness of the proposed method and highlight its potential in practical reliability analysis, and the results disclose that the SS-MASVM is efficient and accurate for assessing the complex system reliability.

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.