System Reliability Assessment Research Articles

Safety and reliability assessment of CO2 refrigeration system for underground applications below -50 °C

A new CO2 refrigeration concept is evaluated to cool the ATLAS and CMS experiments installed close to 100 m underground, at the Large Hadron Collider (LHC) at CERN. The first prototype unit produced to validate the conceptual design and to assess safety and reliability of the system has been commissioned. This paper presents results obtained, discusses the safety and reliability of the system and discourses on the list of recommendations for the future final units to cover the cooling loads of 380 kW for ATLAS and 750 kW for CMS at -53 °C evaporation temperature.

Network reliability evaluation and management science applications in resource allocation for police organizations

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

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.

Nonlinear dynamic finite element analysis of vehicle impacts into road restraint systems

Despite that the safety of cars is continuously increasing, improving the safety of road transportation remains a key issue of modern society. The design of suitable road restraint systems plays an important role. As the tests of barriers are technically demanding and expensive, and road traffic intensity is continuously growing, it is crucial to develop advanced models that would make it possible to explore various scenarios under which road vehicles may accidently impact into the road restraint barrier. The submitted study focuses on the use of nonlinear dynamic finite element analysis (NLDFEA) in the reliability assessment of road restraint systems, with a particular focus on steel barriers subjected to impacts by heavy vehicles. The outcomes of theoretical analyses are compared with the results in a compiled unique database of full-scale road barrier impact test results. It appears that a good match between crash test results and the validated NLDFEA model can be achieved, differences being mostly within a range of 10 %. When modelling the crashes of buses and HGVs, it is particularly important to model correctly shape and geometry of the front and impacting side of the vehicle, tyre sizes, wheelbase and overall truck dimensions, and position of the centre of gravity of the vehicle including trailer and cargo. Recommendations for subsequent research studies and for practical applications are finally provided.

Time-Varying Reliability Analysis of Mechanisms with Strength Degradation Considering Clearances and Local Stiffness Effects

Mechanism systems are important components of dynamic mechanical systems. The time-varying reliability assessment of mechanism systems is of great significance for the safety and fault diagnosis of mechanical products. In this paper, a time-varying reliability evaluation method for mechanisms under the fatigue failure mode is proposed, which takes into account the joint effects of clearance, local structural stiffness, random load and random strength degradation. The proposed mechanism reliability models are established by nonlinear dynamic analysis considering failure factors and establishing random stress history characterization matrices. Additionally, the models are validated by the Monte Carlo simulation method. The results show that the mechanism clearance, local stiffness, and initial strength dispersion have a significant effect on the time-varying reliability of the mechanisms. Furthermore, both local stiffness and initial strength dispersion have a greater influence on the effect of clearance on the time-varying reliability of the mechanisms. The proposed reliability models can quantitatively analyze the dynamic effects of failure factors on the reliability of the mechanisms, which can be used for the stochastic remaining life evaluation and fault diagnosis of the mechanism systems.

Reliability analysis of a grid-connected hybrid renewable energy system using hybrid Monte-Carlo and Newton Raphson methods

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

Application of the Rosenblatt transformation in First-Order System Reliability approximations

The reliability assessment of structural systems presents a significant challenge in structural engineering. A commonly employed approximation is the First-Order System Reliability Method (FOSRM), which estimates system reliability using the FORM component reliabilities and sensitivity factors. An essential step in FORM involves transforming the random vector X into the standard vector U, often using the Rosenblatt transformation (RT). Several studies demonstrated that different conditioning orders in the RT yield different FORM component results. This study investigates how these differences on component level propagate into the FOSRM system level. We conducted several typical engineering case studies with various failure probabilities, system sizes, and dependency structures (Gaussian and Frank Copula). For the Frank Copula, different Rosenblatt conditioning orders systematically yielded different FOSRM results, with most cases showing differences between 10% and 30% in estimated failure probability. For some systems, these differences increased with system size, suggesting that greater variations might be observed for larger systems. Notably, systems with Gaussian Copula functions also proved vulnerable to the Rosenblatt conditioning order when different components were assessed with different conditioning orders. The observed differences were larger than previously reported and should be carefully considered in uniform safety assessments.

Power system reliability assessment technique and modeling approach based on quantum computing theory

Aiming at the reliability assessment of power system, the state sampling process as a critical step requires to generate power system states one by one using classical computer. Due to the quantum superposition, all system states can be generated at once using the quantum computing and then assess the reliability, and thus it will improve the assessment efficiency. However, how to sample system states and assess reliability using the quantum computing theory will be very important. Therefore, this paper proposes a power system reliability assessment technique and its modeling approach based on the quantum computing theory. Firstly, this paper develops a quantum model of power system reliability (including reliability models of electrical device and system) and proposes a new system state sampling method based on the quantum computing theory, which samples all system states at once and improves the sampling efficiency. Secondly, this paper proposes a quantum computing theory based on reliability assessment technique and its modeling approach for the reliability indices: Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS). Thirdly, this paper uses an iterative quantum amplitude estimation (IQAE) algorithm to measure the reliability indices for improving the measurement accuracy. Finally, this paper investigates both designed Case and RBTS, and results shows the correctness and effectiveness of reliability assessment of power system based on the quantum computing.

A Markov method for a mechanical system reliability assessment using discrete degradation data

Abstract A homogenous continuous-time Markov model (HCTMM) has the advantage of high accuracy and is usually used in reliability analysis. However, the transition intensities (rates) between any two states cannot be accurately obtained due to the discrete data obtained from a mechanical system and little expert knowledge. This paper proposes a method in which the transition intensity matrix of an HCTMM can be obtained indirectly using the actual discrete degradation data of a mechanical system. Firstly, the one-step transition probability matrix can be calculated by a hidden Markov model using the discrete degradation data. Secondly, the unit-time transition intensity matrix can be calculated from the one-step transition probability matrix. At last, an HCTMM is established to assess the reliability of a mechanical system based on the unit-time transition intensity matrix. Moreover, considering the maintenance activities cannot restore a mechanical system as a fire-new one, the random transition of a mechanical system after maintenance is expressed by a quasi-renewal process in this paper. Finally, three cases are studied to validate the proposed method.

LOGICAL-PROBABLIC MODELING OF FAILURES OF GEODESIC NETWORKS

The credibility and reliability of the results of geodetic and cadastral works depends mainly on the accuracy of the instruments, the qualification of the observer, his professionalism and the reliability of the points of the geodetic network (GN). As a rule, in most cases, there is a well-founded choice of geodetic tools suitable for accuracy and the involvement of a highly qualified geodetic engineer to perform certain types of geodetic work. However, certain questions may arise regarding the determination of starting points for the creation of a reliable geodetic survey justification. The issue of selecting reliable geodetic points for performing engineering and geodetic works based on the application of reliability assessment methods is an urgent scientific problem today. The purpose of the work is to establish the optimal reliability assessment method based on the analysis of existing ones. Adapt this method to solve the problem of assessing the reliability of geodetic networks as one of the most important parameters characterizing its condition. Assess the reliability of the Dnipro geodetic network by constructing a fault tree. Calculate the dynamics of the intensity of GN failures during the 100-year period of its existence. Calculate the appropriate periodicity of surveys (monitoring) of GN depending on the intensity of failures of elements (geodetic points). Methodology. Analysis of international and national standards for reliability assessment, Standards of Ukraine, where the methods of system reliability assessment are given. Review of methods for assessing GN reliability. The deductive method with the construction of a geodetic network failure tree has been applied. This method is based on a logic-probabilistic model of cause-and-effect relationships of system failures with failures of its elements and other events. Scientific novelty. The optimal method of assessing the reliability of GN has been established. A GN failure tree has been built in the TopEvent FTA software tool. The reliability of the geodetic network has been evaluated, the intensity of failures at various stages of its operation has been calculated. Mathematical expressions of the failure functions of the geodetic network have been described by applying Boolean functions. Practical value. International and national regulatory documents and Standards of Ukraine of reliability assessment have been analyzed. In Standarts the methods of system reliability assessment have been given. The performed assessment of the reliability of the state of the geodetic network of the city of Dnipro helps to choose stable geodetic points for the creation of local geodetic networks and survey justification. Reasonable periodicity of monitoring for the survey of geodetic points has been substantiated. The use of reliable points of geodetic networks will allow obtaining reliable observation data, which will significantly increase the quality of engineering and geodetic works. Results. The functioning of the geodetic network of the city of Dnipro on the constructed sequential tree of failures has been considered. Boolean functions to describe the mathematical expressions of the failure functions of the geodetic network, which includes 5 and 10 elements have been used.

An active learning approach for reliability assessment of passive systems combining polynomial chaos expansion and adaptive sampling

Passive safety system is extensively employed in newly designed reactors to increase their inherent safety. However, the driving force is smaller, and the uncertainty of the thermal–hydraulic (T-H) process may potentially result in the inability of system to perform its intended function, which is often known as functional failure. Therefore, the passive systems reliability has received increasing attention. Unfortunately, the assessment of system failure probability utilizing Monte Carlo simulation (MCS) methods necessitates a significant quantity of repeated calculations employing the thermal–hydraulic code, which may be impractical in terms of computational cost. To enhance the assessment calculation efficiency, an adaptive surrogate model is proposed. The bootstrap method was employed to introduce an error estimate into the polynomial chaos expansion (PCE) model, which solves the obstacle of the PCE model in reliability evaluation applications due to the lack of error estimation. The method constructs an active learning approach through error estimation, strategically identifies and selects the best candidate samples, and iteratively updates the initial experimental design. The effectiveness of the method was verified through three benchmark cases, and it was employed for assessing the passive residual heat removal system (PRHRS) reliability within integral-type pressurized water reactor (IPWR200). The results demonstrated that the proposed method significantly diminishes the frequency of invocations to the T-H code and improves computational efficiency to a large extent. Furthermore, it can serve as a valuable guide for the design, operation, and reliability evaluation of the passive system.

Limit hypersurface state of the art Gaidai multivariate risk evaluation approach for offshore Jacket

Current study presents state of the art approach to risk and reliability assessment for multivariate nonlinear dynamic systems. Specifically, novel hypersurface Gaidai risks evaluation methodology has been presented for evaluating offshore structural risks. Advocated approach being particularly suitable for offshore engineering multidimensional dynamic systems, possessing large number of critical components, that have either been physically observed/measured, or numerically modeled over a representative timelapse. Advocated Gaidai hypersurface structural risks evaluation methodology is applicable for a wide range of engineering and industrial systems. This study demonstrates that given in situ environmental conditions, it is well possible to assess accurately risks of system’s failures, damages or hazards, caused by excessive structural dynamics. Multivariate dynamic systems, possessing nonlinear cross-correlations between critical system’s components often present design challenges, when utilizing classic structural risks evaluation approaches, as those are mostly only univariate or bivariate. Dynamic offshore Jacket hot spot stresses have been utilized as an example in this reliability investigation. Modeling system’s excessive dynamics is challenging because of the non-stationarity of the system and the intricacy of fluid-structural dynamic interactions, arising from in situ wave-induced loads, influencing Jacket’s structural dynamics. Nonlinearities greatly affect structural dynamics, e.g., 2nd, 3rd, higher order effects within fluid-structural interactions. Methodology presented in this work offers a straightforward, effective, yet precise means of assessing the risks of failure or hazard for multivariate, non-stationary, non-linear dynamic offshore systems. For bivariate case, verification note has been added.

Review on reliability assessment of energy storage systems

AbstractAs renewable energy, characterised by its intermittent nature, increasingly penetrates the conventional power grid, the role of energy storage systems (ESS) in maintaining energy balance becomes paramount. This dynamic necessitates a rigorous reliability assessment of ESS to ensure consistent energy availability and system stability. The authors provide a review of the existing research on ESS reliability assessment, encompassing various methods, models, reliability indicators, and offers an analysis of future research trends in ESS reliability. Firstly, the authors summarise the different types of ESS and their characteristics, analysing the trends in ESS reliability research and the unique characteristics of ESS compared to conventional power systems. Secondly, the methods used for the assessment are reviewed, including Markov methods, generalised generating functions, Monte Carlo simulations etc. The shortcomings and characteristics of these methods are discussed. The key reliability indicators, such as Mean Time Between Failures and Mean Time to Repair are emphasised. The applied role of reliability studies is summarised. Finally, the perspective of new research trends in ESS reliability assessment are identified, especially the integration of artificial intelligence and machine learning, and emphasises their potential to further improve the robustness and effectiveness of ESS reliability.

Evaluation method of software reliability index

Abstract Software reliability refers to the ability of software not to cause system failure under specified conditions and within the specified time. With the increasing demand for equipment systems for software systems, the software system has become an important part of determining the success or failure of combat force system missions. The reliability assessment of the software system is becoming more and more important. Aiming at the problem of software reliability evaluation, this paper discusses the choice of reliability evaluation model. The model parameters are determined. On this basis, according to the selected reliability evaluation model, the model parameters are estimated through the collected data. Finally, the reliability evaluation analysis is carried out.

System reliability study of geothermal energy walls in subway stations based on rapid thermal performance prediction model

The energy conservation potential of relative systems in subway stations can be improved via the implementation of energy walls. However, current research mainly focuses on analyzing the factors affecting the energy walls' heat transfer performance, neglecting the reliability and performance assessment for ground source heat pump systems based on energy walls. This paper focuses on a subway station in hot-summer and cold-winter climate zone. Initially, both a resistance-capacity model and a 3D finite element model of energy walls are established. The resistance-capacity model is validated to be integrated with heat pump units to propose the rapid system performance prediction. Subsequently, the system thermal performance with the input of dynamic loads and conventional operational strategies is investigated. The results reveal that due to the heating and cooling load imbalance, the system inevitably experiences thermal imbalances and enters an inefficient operational state by the 6th year. Finally, to address this issue, a hybrid heat pump system with cooling towers is proposed, which demonstrates better thermal performance when used during non-cooling seasons. Another hybrid heat pump system for simultaneous cooling and heating is also proposed. These approaches both delay the onset of the system's inefficient operational state by 1–2 years, thereby improving the thermal performance.

Reliability and risk assessment of digital twin system based on improved failure mode and effects analysis

Reliability and risk assessment of digital twin system based on improved failure mode and effects analysis

Fiducial Approach for the Storage Reliability Assessment of Complex Repairable Systems

Fiducial Approach for the Storage Reliability Assessment of Complex Repairable Systems

A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes.

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the "curse of dimensionality", which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2×2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system's non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method.

Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach

Dynamic reliability assessment has offered a new paradigm for engineered systems to integrate multi-level observations to update the reliability measures of a specific running system. The existent work on dynamic reliability assessment can only leverage precise observations and cannot be straightforwardly implemented in scenarios where observations are imprecise. However, the observations are inevitably imprecise owing to the limited accuracy of inspection techniques and vague judgments of the system state. In engineering scenarios, multi-state systems (MSSs) with a hierarchical structure are commonly existent, and the imprecise observations can be collected across multiple physical levels of the system. In this article, the uncertainty associated with imprecise observations is characterized by the evidence theory, and it can be therefore regarded as an evidential form. A dynamic Bayesian network (DBN) model is utilized to evaluate the reliability of hierarchical MSSs with multi-level evidential observations. Subsequently, the evidence theory is implemented to quantify epistemic uncertainties associated with imprecise observations. These observations, sourced from multiple physical levels, are merged by the DBN model using the Dempster rule of combination (DRC) to update the reliability of a specific running system in a dynamic fashion. The feasibility and correctness of the proposed method have been demonstrated through a numerical case and a real engineering case of a kerosene filling control system (KFCS), and the result indicates that the proposed DBN-based algorithm methods have high applicability and generalizability.

Validation of Sinhala and Tamil translations of the motherisk: Pregnancy unique quantification of emesis (PUQE) scoring system

Aims: Nausea and vomiting in pregnancy (NVP), where there are no other underlying causes, is one of the most common symptoms in the early part of the pregnancy. It is responsible for up to 80% of pregnant population. By using PUQE score system, it is very convenient to categorize pregnant women with nausea and vomiting come to outpatient department (OPD) or general practice into those who can manage in community setting or those who need inpatient treatments. Objectives: Validation of PUQE instrument to assess NVP and hyperemesis gravidarum (HG) women in Sri Lanka. Methods: Descriptive cross-sectional study conducted among 160 pregnant women presented to the Gynecology Unit of Teaching Hospital Anuradhapura (ATH) during three months duration. Translated self-administered PUQE questionnaire was applied to the patients with signs and symptoms with emesis in pregnancy. All study participants were undergoing clinical judgment as a confirmation test. Diagnostic test accuracy methods were used to assess criterion validity of the experimental tool. Data were analyzed by using SPSS statistical software version 25.0. Results: Study population has a mean age of 27.96 (SD 5.31). Majority of study participants were Sinhalese and according to the parity majority were primigravida pregnancies. Majority of study participants were diagnosed as NVP Stage II (N=134:53.8%) by the PUQE scoring system. According to the clinical judgment Stage II of NVP was diagnosed among 127 (79.4%) study participants. Mean score of the PUQE scale was 9.08 (SD=2.35). Reliability assessment of the PUQE scoring system was reported as an excellent level (Cronbach alpha=0.804). Five types of management strategies were detected among study participants. Significantly high percentage of study participants were given first line antiemetics and oral rehydration solutions. Calculated sensitivity of the PUQE scoring system to diagnose Stage I of NVP was 69.56% and specificity was 96.35%. Calculated sensitivity of the PUQE scoring system to diagnose Stage II of NVP was 100% and specificity was 78.78%. All pregnant women who were clinically detected as Stage II NVP patients were detected as Stage II NVP patients by the PUQE scoring system also. None of the false positives and false negatives regarding Stage II of NVP detected by the PUQE scoring system. Conclusion: First line antiemetics and oral rehydration solutions are usually used for treating pregnancy-related nausea and vomiting. Stage II NVP conditions were detected with a higher prevalence when NVP status was diagnosed clinically and with PUQE scoring system. Specificity of PUQE scoring system appeared higher while detecting NVP Stage I and for NVP Stage II, sensitivity of PUQE scoring system appeared high. For Stage III NVP, clinical judgments and PUQE scoring system values were completely compatible. Management strategies used for treating pregnancy-related hyperemesis status should be further studied. Conducting these studies at different study settings may help to achieve more successful results. Pregnancy unique quantification of emesis tool can be used to diagnose NVP status more descriptively. Also, this PUQE tool is a valid tool which can be effectively used for patient management. Application of PUQE scoring system should be published by planning and implementing staff awareness programs for both curative sector and public health sector.