Reliability Analysis Of Systems Research Articles

Closed-Form Capacity Reliability Analysis of Multiuser MIMO System in the Presence of Generalized Multipath Fading.

This research studies the problem of a joint capacity/capacity reliability analysis of the multiuser multi-input multioutput (MIMO) system functioning in the presence of generalized multipath fading. The study presents the derived results of the closed-form analytical statistical description of the ergodic sum-rate capacity, the capacity reliability and the capacity's higher-order statistics in the case of complex Nakagami-m distributed channel transmission coefficients. A numerical verification of the derived expressions was performed, and it demonstrated excellent correspondence with the simulation. The system performance was evaluated with the help of a numerical analysis of the joint first- and second-order statistics description, depending on the channel and system parameters. The results demonstrated several peculiarities, e.g., the existence of a specific extremum of the capacity reliability for small-sized MIMO systems, its opposite behavior (in terms of the varying number of antenna elements) for heavy and light fading, and the existing asymptotic regions of the system and the channel parameters.

Reliability Analysis of Failure-Dependent System Based on Bayesian Network and Fuzzy Inference Model

With the rapid development of information and automation technology, the manufacturing system is evolving towards more complexity and integration. The system components will inevitably suffer from degeneration, and the impact of component-level failure on the system reliability is a valuable issue to be studied, especially when failure dependence exists among the components. Thus, it is vital to construct a system reliability evaluation mechanism that helps to characterize the healthy status of the system and facilitate wise decision making. In this paper, a reliability analysis framework for a failure-dependent system is proposed, in which copula functions with optimized parameters are used for the description of different failure correlations, and a fuzzy inference model is constructed to derive the subsystem reliability based on the component-level failure correlation. Finally, a Bayesian network is applied to infer the system reliability based on the system structure combined with the impact of failure correlation inside. Simulation results of the proposed method show that the inference results of system reliability are reasonable and effective in different cases. Compared with the copula Bayesian network method, the proposed method shows better adaptability to failure-dependent systems to varying degrees. This work can provide theoretical guidance for evaluating the reliability of manufacturing systems of different types.

Fire Safety of Plane Steel Truss according to System Reliability Analysis Combined with FORM Method: The Probabilistic Model and SYSREL Computation

This article focuses on the reliability analysis of the plane steel truss under fire conditions. The safety of the structure was estimated by system reliability analysis combined with First Order Reliability Method (FORM). The authors created the C++ code, which enables us to prepare the advanced probabilistic model for bearing capacity in the selected time of fire duration. Searching cut-sets for system analysis was performed in the C++ code, where stiffness matrix spectral analysis was employed. It was found that a probabilistic model has significant influence on the reliability indices. The research showed that depending on the probabilistic model, the sensitivity of the reliability index to individual variables is different.

System reliability and sensitivity analysis of lateral loaded pile considering soil’s spatial variability

ABSTRACT Probabilistic analysis has been widely used to assess the inherent uncertainty of variables in laterally loaded pile systems, but the calculation is still difficult and time-consuming. The present study presents an efficient probabilistic analysis framework for a laterally loaded pile system. The performance of the system is defined as the lateral deflection at the pile head and maximum bending moment of the pile shaft, corresponding to two failure modes. Within this framework, the spatial variability of the soil and the correlation between failure modes are considered by the random field theory and the First-Order Reliability Method, respectively. Moreover, the Sequential Compounding Method is used as an efficient tool to determine the system reliability indexes. The framework is confirmed by comparing the reliability indexes of failure modes and systems with those of the Monte Carlo Simulation Method. Furthermore, a parametric analysis and system sensitivity analysis are performed. The results show that the auto-correlation distance, allowable lateral displacement at the pile head, and allowable bending moment of the pile shaft have a great influence on reliability indexes of failure modes and system, and the major parameter of soil in affecting pile is the elastic modulus compared with the undrained shear strength.

Optimum maintenance strategy for CRTS II slab track based on time-dependent system reliability

CRTS (China Railway Track System) II slab track is a vertical multi-layer and longitudinal anisotropic strip system, which has been widely constructed in China’s high-speed railway. In this study, an innovative optimum maintenance strategy is proposed for CRTS II slab track, based on the concept of time-dependent system reliability and life-cycle cost analysis. System limit state function of the track structure is constructed with consideration of important serviceability failure modes. The time-dependent system reliability of the track structure is investigated and utilized as the indicator of track performance, which is estimated by an innovative outcrossing method based on the system limit state function considering time-dependent correlation among failure modes. At each maintenance time, all critical failure modes and components are identified and repaired to decrease the system failure probability under the acceptable level. Application of the proposed maintenance strategy is demonstrated by a worked example of a track structural system on bridge. It is found that: (1) appropriate maintenance strategy is important to ensure service reliability of the track system and extend the service life; and (2) the proposed maintenance strategy can be utilized to predict when, where, and what failure mode for maintenance and it can help to minimize the expected life-cycle cost without compromising the service reliability of the track structure.

Developing an advanced reliability analysis framework for marine systems operations and maintenance

Power generation system reliability is of great concern for all ship operators irrespective of sector, as it provides the highest utility and ensures collective safety of operators, passengers, equipment and cargo. A novel approach to system reliability analysis using DFTA, FMECA and BBN applied to 4 DGs has been conducted. The outcomes provide insight on faults and component criticality to vessel maintenance and availability. Building from the understanding of how multiple factors can influenced maintenance in addition to routine or age-related wear and tear of machinery. This research looked into operators’ peculiar challenges regarding environment, operational demands and technology challenges that affects maintenance and system reliability. A framework based on outputs from DFTA minimal cut set, RPN from FMECA were used as inputs for BBN to analyse ship marine DG system availability. A BBN influence diagram was used to build a maintenance strategy DSS. Overall outcome for the maintenance strategy selection DSS indicates relatively high unavailability. Therefore, DGs with low availability were recommended to be on Corrective action and ConMon, while DGs with good availability were recommended to be on PMS.

Reliability Theory and Practice for Unmanned Aerial Vehicles

Due to rapid advancements on the Internet of Things (IoT), unmanned aerial vehicles (UAVs), also known as drones, are transforming numerous military and civil application areas. UAVs aim to enhance the production efficiency, ensure safety, and reduce risk, particularly protecting the human workforce in the case of harsh and dangerous environments. Due to the mission-critical, business-critical, or safety-critical nature of the UAV applications, it is pivotal that UAVs perform reliably to deliver the required service during the intended mission time. Therefore, reliability is one of the essential requirements for designing and operating UAVs. This article presents a critical review of UAV reliability literature in both theoretical and practical research, pinpointing failure causes and reliability challenges of UAV systems, classifying and reflecting on the reliability modeling, analysis, and design methods for UAV systems and key subsystems. Some open research problems and opportunities are also discussed to highlight potential new challenges for designing reliable and resilient UAVs and UAV-assisted IoT systems.

Techno-Economic Green Optimization of Electrical Microgrid Using Swarm Metaheuristics

In electrical power engineering, elements such as reliability analysis, modeling, and optimization for complex systems are of the utmost importance. Although there exist myriad studies regarding reliability optimization with conventional methods, researchers are still seeking to find more efficient and accurate methods to address the issue of the redundancy allocation problem. To that effect, an ideal power energy management approach is put forward for the operation of a hybrid microgrid system with different kinds of productions. In the present study, we suggest three algorithms in order to optimize the series-parallel power energy system: the Firefly (FA), Bat (BA), and Interior Search (ISA) algorithms. Moreover, the reliability estimate of the system is solved with the Ushakov algorithm (UMGF). The components may completely fail, which decreases their performance rate. Furthermore, the optimization results are achieved using objective functions that include the total cost of the system, emission gases (NOX, SO2, and CO2) of the power production from fuel cells, diesel generators, and gas turbines, and take into consideration the dependability indices. Devices used in power subsystems are characterized based on their dependabilities, performances, capital costs, and maintenance costs. Reliability hinges on a functioning system, which naturally entails meeting customer demand; as a result, it is influenced by the accumulated batch curve. This method provides an idea with regards to the economic cost optimization of microgrid systems. Finally, we present the results of numeric simulations.

Reliability analysis of cold-standby systems using a self-stratifying subset simulation method

Cold-standby systems are often featured with small failure probability and widely used as critical safety systems in current engineering fields. The existing analysis methods for cold standby systems have the problems with low-computional efficiency either for small failure probability events or for the components with mixed distribution types. To solve these problems, we propose a self-stratifying subset simulation (4S) method based on MCMC simulation and the performance function of cold-standby system. Numerical case studies are performed to demonstrate the reasonability and feasibility of the proposed 4S method. The case studies indicate that the results obtained by the proposed 4S method agree well with those obtained by the existing methods and the computation efficiency is greatly improved especially for small probability events. Moreover, the proposed 4S method was applicable for the cold-standby system cold-standby system with not only the exponentially or non-exponentially distributed components but components with more mixed distribution types.

Linear Consecutive-k-out-of-n: G System Reliability Analysis

The concerned study pertains to the development of a new stochastic model for the reliability analysis of linear consecutive-k-out-of-n: G system, where k>n2. In the developed model, system may collapse as a result of common cause failure or hardware failure in its units. The system has exponentially distributed failure rates, and in case of breakdown, it is repaired with the copula method. The developed model has been examined through supplementary variable technique (SVT) along with Laplace transform. The current paper has specifically studied consecutive-(n-1)-out-of-n: G system. The performance of such system having ten components is explored and its various reliability measures have been obtained and discussed with the help of graphs. The originality of this work lies in incorporating common cause failure in conjunction with copula repair in the reliability modeling of consecutive systems through the SVT. The study confirms that an increase in failure rates and the number of components of the concerned system decreases mean time to failure (MTTF). The profit of linear consecutive-9-out-of-10: G system is examined with the help of a numerical example.

Efficient model-correction-based reliability analysis of uncertain dynamical systems

The scope of this paper is to apply a model-correction-based strategy for efficient reliability analysis of uncertain dynamical systems based on a low-fidelity (LF) model whose outcomes are corrected in a probabilistic sense to represent the more realistic outcomes of a high-fidelity (HF) model. In the model-correction approach utilized, the LF model is calibrated to the HF model close to the so-called most probable point in standard normal space, which allows a more realistic assessment of the considered complex dynamical system. Since only few expensive limit state function evaluations of the HF model are required, an efficient reliability analysis is enabled. In an application example, the LF model describes an existing single-span railway bridge modelled as simply supported Euler–Bernoulli beam subjected to moving single forces representing the axle loads of a moving train. The HF modelling approach accounts for the bridge–train interaction by modelling the passing train as mass-spring-damper system, however increasing the computational effort of the limit state function evaluations. Failure probabilities evaluated with the model-correction approach are contrasted and discussed with failure probabilities of the sophisticated bridge–train interaction model evaluated with the first-order reliability method (FORM). It is demonstrated that the efficiency of the method depends on the correlation between the LF and the HF model. A comparison of the results of FORM and the model-correction-based approach shows that the latter provides reliable failure probability prediction of the HF model while leading to a significant reduction in computational effort.

Reliability analysis and optimization for a redundant system with dependent failures and variable repair rates

This paper considers the reliability analysis and the bi-objective optimal problem for a redundant system with two dependent failures: common cause failure (CCF) and load-sharing failure, where the system contains N active components, W warm standbys and C cold standbys, a K-mixed redundancy strategy is considered. In the system, there is a repair team providing variable repair rates according to differential situations: when there are standby components available in the system, the repair team assigns a regular repairman to repair the failed components, with the known fact that regular repairman is not as skilled as an expert repairman in doing some complex repair; when all the spares are exhausted, the repair team assigns an expert repairman to attend failed components; when the system breaks down due to a CCF’s occurrence, the repair team provides a very special repair service to restore the system to normal working state. The steady-state distribution of the system is obtained by matrix-analytic method. The sets of equations satisfied by the reliability functions and transient availabilities from arbitrary initial state are respectively presented and solved by Markov renewal theory, Laplace–Stieltjes transform (LST) and Laplace transform (LT) technique. Based on the obtained results, sensitivity analysis is performed by some numerical experiments. From economic view point, the bi-objective optimization problem is constructed and solved by NSGA-II algorithm for the minimal expected total cost and maximal availability.

Uncertainty analysis of extreme mooring loads associated with environmental contours and peak tension distributions

This paper aims to assess the uncertainty on the extreme mooring loads of floating system considering short-term variability. Two environmental contour approaches based on the inverse First and Second Order Reliability Methods are employed to identify critical sea states that may give rise to extreme loads. The uncertainty related to the construction of environmental contours is addressed including significant differences due to marginal distribution fitting, parameter estimation methods and joint models. Three measured datasets are analysed using a known conditional joint distribution and proposed mixed copula model. 3-h time domain numerical simulation for each sea state is conducted and the characteristic extreme responses of mooring lines subjected to design loads are assessed. The uncertainties due to various statistical models including the average conditional exceedance rate method as well as global maxima, peak-over-threshold method combined with Gumbel distribution, Generalized Extreme Value distribution, Generalized Pareto distribution and 3-parameter Weibull distribution are investigated and quantified. It is observed that marginal distributions, joint models and parameters estimation methods have apparent effect on design loads estimation, and the extreme tensions of the semi-submersible platform shows significant difference using various probabilistic models. The results indicate that those epistemic uncertainties should be account for in the reliability analysis or safety factor calibration for mooring systems.

An efficient algorithm for analyzing multimode structure system reliability by a new learning function of most reducing average probability of misjudging system state

• A new MRF learning function for multimode structure system is proposed. • A contribution function is established and explicitly derived. • A reasonable convergence criterion is employed. • The accuracy and efficiency of the proposed method are verified by five examples. For efficiently analyzing reliability of multimode structure system by adaptive Kriging model combined with Monte Carlo simulation, a new most reduction learning function (MRF) is proposed in this paper. Firstly, a contribution function is established to quantify the effect of candidate sample point (CSP) on reducing average probability of misjudging single mode structure state in the differential domain adjacent to CSP. Secondly, the contribution function is explicitly derived after selecting a reasonable size of the differential domain. Thirdly, by the maximum expected contribution function of single mode structure and the logical relationship among modes in multimode structure system, the new learning function named MRF is obtained. The main novelty of this work is proposing a new MRF based strategy for multimode structure system reliability analysis, in which it considers the logical relationship among modes in multimode structure system, probability of misjudging single mode structure state, probability density function and the absolute value of Kriging prediction of single mode structure at CSP. Compared with the up-to-date methods, the proposed strategy for multimode structure system reliability analysis is easier and more efficient to be executed, and its superiority is verified by numerical and engineering examples.

Modelling of heating furnace fire scenarios using fault tree analysis, a bayesian network, and a thermal transfer method for system reliability analysis

Energy production for oil companies requires high-performance equipment. However, after a certain period of use, this equipment could be damaged, causing numerous problems and defects, impeding production, and leading to incidents that may cause material and human losses. Therefore, it is necessary to master the performance parameters of each equipment, which are key indicators of its functionality and can indicate the possibility of reusing it after inspection and diagnosis. However, it is worth noting that the studied system is a vertical cylindrical furnace in Algeria's refinery. This paper describes a new hybrid approach, a methodology that evaluates system reliability by combining two studies. The first is based on the surety of the system's functioning through an analysis with a fault tree to identify the risks, followed by a quantitative analysis using a Bayesian method. The second study follows the thermal calculation of the equipment's performance parameters, namely: the thermal efficiency (η), heat transfer coefficient (h), and heat absorbed quantity (Q), using heat balance equations to quantify the heat absorbed quantity and identify the fluid flow regime inside the tube. The calculation considers three cases: The design case, the failure case, and the after renovation case. The originality of this work appears when the collected findings from the second study are fed into the first study's Bayesian Network (BN) to analyse the system's reliability using the calculated parameters. Then, BN is used to calculate the Probabilistic Importance Factors (PIFs) in order to identify the most impactful system components. Finally, a prediction analysis was conducted. The latter reveals that one of the furnace's major faults is the fumes accumulation inside the combustion chamber. Corrosion, overheating, coke formation inside the tubes, dumper deformation, and burner tip clogging all contribute to this fault.

Analysis of Information System Implementation, Compensation and Reliability of Internal Control to the Financial Fraud Prevention

This research examines the implementation of information system, compensation, and internal control toward the effect on financial fraud prevention. The study conducted in one of the largest chemical distribution company in Indonesia, with the financial staff of the headquarter as the respondent. Data gathered by using total sampling, and examine by using regression analysis. Result shows that the implementation of information system and compensation can prevent financial fraud, but compensation is not. the significant of the result is, compensation works on preventing financial fraud when the information system and internal control applied. To develop stringer impact, future research expected to have wider scope of respondent and using smaller alpha score to see the different of the research and to add the qualitative perspective.

Discrete-Time Bayesian Networks Applied to Flexible Coping Strategies of Nuclear Power Plant Systems

The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risk in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by the Nuclear Energy Institute to manage the challenges of BDBEEs and enhance reactor safety. Due to the uniqueness of the FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. In this study, we investigate the effectiveness and applicability of both Bayesian networks (BNs) and discrete-time Bayesian networks in the reliability analysis of FLEX equipment. The study compares BNs with two other reliability assessment methods: fault tree and Markov chain. These methods are also shown to be capable of mapping into BNs to perform a reliability analysis of FLEX systems. A neutral dependency algorithm is used to simplify the conditional probability tables and reduce the complexity of the BNs. The results indicate that BNs are not only a powerful method for modeling FLEX strategies but are also effective techniques for inclusion of the dynamics of FLEX equipment in probabilistic risk analysis.

Liquid chromatography-quadrupole orbitrap and liquid chromatography-tandem mass spectrometry system for rapid identification and quantitation of thirty nonsteroidal anti-inflammatory drugs and acetaminophen in illegal products.

As the public interest in healthcare increases, illegal dietary supplements, foods, and drugs containing unauthorized pharmaceutical ingredients, including nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen, have been identified. Excessive and unintentional consumption is toxic to the gastrointestinal tract, kidneys, and liver; therefore, these pharmaceuticals must be monitored using analytical methods. A rapid and reliable analysis system involving liquid chromatography-quadrupole orbitrap mass spectrometry (LC-Q-Orbitrap/MS) and liquid chromatography-tandem mass spectrometry (LC/MS/MS) was established and validated to identify and quantify 30 NSAIDs and acetaminophen. In addition, we obtained the MS2 spectrum for each component with the proposed structure of the fragment ions. The analytical method was applied to 505 samples of illicitly distributed dietary supplements, foods, and pharmaceuticals. Non-steroidal analgesics were detected in 126 samples. Carbamazepine (42.9%) and diclofenac (30.2%) were the most detected components in the samples; other pharmaceutical adulterants were also detected in some cases. Additionally, we present the identification of an unknown component, dexamethasone (799 μg/g), using LC-Q-Orbitrap/MS in a sample containing the unknown component with meloxicam (15.4mg/g). The developed analysis system, consisting of qualitative analysis using LC-Q-Orbitrap/MS and quantitative analysis using LC/MS/MS, can rapidly and accurately identify and quantify NSAIDs and acetaminophen while also identifying non-analytical components.

A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis

The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called UA, which takes the correlation between training points and samples into account. The UA function has been developed from the U function but is distinct from it. The UA function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since UA and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications.

System reliability analysis of landslides involving sliding surfaces using intelligent multiple response surfaces

The shear strength reduction method provides an effective tool of numerical analysis for landslides reliability analysis. However, it ignores the failure probability of the secondary failure surfaces and requires huge computational cost. To avoid these common criticisms, an intelligent multiple response surfaces method for system reliability using multiple response-surface method (MRSM) and least-squares support vector machine (LSSVM) is presented to evaluate the stability of complex multistage historic landslides with multiple sliding surfaces. Deterministic analysis of each sliding surface is first performed using the finite element method of sliding surface stress analysis, which is applied to obtain the safety factors of different sliding surfaces from the stress fields generated by finite element simulations. The LSSVM model with excellent nonlinear fitting ability is then employed to construct the multiple response-surface method (MRSM) of the sliding surfaces and a genetic algorithm (GA) is adopted to optimize the LSSVM. This proposed methodology is finally applied to investigate the probability of system failure of the Zhenggang landslide in southwestern China. The results indicate that the proposed approach can reduce the computational cost of finite element analysis in direct Monte Carlo simulation (MCS) by proper training using a limited of samples, and the calculation accuracy meets the engineering requirements of complex multistage historic landslides.