Perform Reliability Analysis Research Articles

Automatic Calculation and Simulation of Time-Varying Failure Rate of Digital Relay Protection Device

Efficiency is an important indicator of risk assessment, but the failure rate changes with time, which makes risk assessment very difficult. Traditional methods often only measure the steady-state and static indicators of relay protection. The traditional method often only measures the steady-state and static indicators of the relay protection, or directly uses the RiskWeibull analysis system to perform reliability analysis on the relevant data. However, this method can only perform quantitative static analysis on the data. Once the system is disturbed, its reliability will be affected. Analysis is not possible. Since the relay protection device has the possibility of malfunction and refusal at the same time, in order to distinguish, the risk is introduced into the reliability evaluation of the relay protection device to achieve the purpose of considering the possibility and consequences of its failure at the same time. Based on the traditional method, this paper proposes a method to calculate the time-varying failure rate of the relay protection device by using the support vector machine model, which improves the influence of hidden risks on the power grid relay protection, and not only realizes the scientific hidden danger evaluation, risk rating. It is of great significance for the development of power grids to evaluate the consequences of failures and to make the transmission lines operate safely and stably.

Precisely modeling offshore jacket structures considering model parameters uncertainty using Bayesian updating

Digital twin (DT) is recognized as a promising tool to perform reliability analysis for offshore platforms. An open issue in developing the DT reliability analysis tool is to establish a high-fidelity highly-precise virtual model for the offshore platform of interest, where the virtual model is able to update its model parameters using actual sensory measurements to eliminate/reduce the effect of parameters uncertainty. To address this challenging issue, this paper proposes a Markov Chain Monte Carlo (MCMC)-based Bayesian updating method to build the requested virtual model for an offshore jacket platform. In this new method, the natural frequencies and mode shapes of the offshore jacket platform, extracted both from the structural dynamic responses of a finite element (FE) model and the sensory measurements in the physical system, are used to construct the likelihood function of the Bayesian inference. Then, the posterior probability distribution function (PDF) of the uncertain parameters is derived under the assumption of uniform prior distribution, which will be used to generate the Markov Chains by implementing the Metropolis-Hastings sampling. Hence, the most probable values of the uncertain parameters can be obtained from the Markov Chains for updating the FE model. A numerical study demonstrates high effectiveness of the proposed model updating method, even with high-level measurement noise and model uncertainty.

RISK ANALYSIS TECHNIQUES OF OIL AND GAS PIPELINE

The safety of pipelines that transport energy has become an important and controversial issue with the general public. The main hazard for safe transportation of substances is a pipeline failure taken as a loss of its tightness and release of the transported medium to the environment. To perform reliability analysis and estimation of accident risk level, Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) are two graphical techniques used to perform a risk analysis, where FTA represents causes and their probabilities of failures, and ETA represents consequences of a failure event. Failure of oil transmission pipelines was analyzed by fault tree analysis in this project, and the probability of internal corrosion, one of the root causes, has been evaluated by applying the physical reliability model. According to failure modes of pipeline, leakage and rupture, a fault tree of the pipeline was constructed. Minimal cut sets of the fault tree have been shown, and the failure probability of a top event and the important analyses of basic events were evaluated by quantitative analysis. In conventional fault tree analysis, probabilities of the basic events were treated as precise values. At last, the probability of different accident scenarios that may result from failed oil pipeline due to pipe rupture has been estimated.

Reliability analysis of multi-parameter monitoring systems for Intensive Care Units

Multi-parameter monitoring systems in Intensive Care Units (ICUs) monitor the clinical condition of critical state patients. These Systems of Systems (SoS) comprise a set of Constituent Systems (CS) to measure parameters such as heart rate, respiratory frequency, and temperature. Due to the critical nature and relevance of ICUs, such SoS shall be as reliable as possible. That is especially true in emergencies, as the COVID-19 outbreak that resulted in the burden of health care systems. We developed a modular and parametric model to perform reliability analysis and to provide insights to assist the management of multi-parameter monitoring systems used in ICUs, also considering maintenance. First, we modeled a multi-parameter monitoring system for ICUs using the CHESS methodology and modeling language. Afterward, we performed a reliability analysis using the CHESS state-based analysis plugin for different scenarios. We identified that the main power supply and the battery are the CS that present the most negative impacts on reliability. In emergencies, reduced time ranges of planned maintenance, when applied during a short period, showed to be promising strategies.

Statistical model for analizing negative variables with application to compression test on concrete

In some areas of knowledge, we can find negative variables (ℝ-), to have a statistical model is crucial to represent the phenomenon and explain it using other variables. This paper proposes a regression model to analyze negative random variables using the reflected Weibull distribution. We developed the RelDists package in the R programming language to implement the proposed model. A Monte Carlo simulation study was conducted to explore the performance of the estimation procedure considering censored and uncensored data and the presence and absence of covariates. From the simulation study, we found that the estimation procedure achieves accurate estimations of the parameters as the sample size increases and the percentage of censoring decreases. In the paper, we present an application of the proposed model using experimental data from a compression test with concrete specimens. In the application, a model was fitted to explain the shrinkage strain using the variable time. The regression model for negative variables and the RelDists package can be used by academic, scientific, and business communities to perform reliability analysis.

Assessment of Spare Parts Requirement by Reliability: A Case Study

Spare parts provision is a complex problem and requires an accurate model to analyze all factors that may affect the required number of spare parts. The number of spare parts required for an item can be effectively estimated based on its reliability. The reliability characteristics of an item are influenced by different factors such as the operational environment, maintenance policy, operator skill, etc. However, in most reliability-based spare parts provision (RSPP) studies, the effect of these influence factors has not been considered. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability analysis by considering risk factors is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. Thus, this paper aims to demonstrate the application of the available reliability models with covariates in the field of spare part predictions using a case study. The proposed approach was evaluated with data from the system of fleet loading of the Jajarm Bauxite mine in Iran. The outputs represent a significant difference in spare parts forecasting and inventory management when considering covariates.

Adaptive structural reliability analysis method based on confidence interval squeezing

Kriging-based adaptive structural reliability analysis methods can replace the structural performance function and perform reliability analysis accurately and efficiently by using appropriate learning functions. In this context, a novel adaptive structural reliability analysis strategy, namely, confidence interval squeezing (CIS) method, is presented in this paper. After obtaining the confidence interval of the failure probability on the basis of the given significance level α, CIS considers squeezing the confidence interval at the highest speed possible to improve the accuracy of estimating the failure probability. CIS strives to improve the estimation accuracy of failure probability rapidly rather than paying too much attention to the state of a single candidate sample, which is the primary difference between the CIS method and other learning functions. In addition, a new global convergence condition is proposed based on the confidence interval of failure probability. Considering the difficulty of direct application of CIS, three variants of the CIS method, namely, sCIS method for sequence additions, pCIS method for parallel additions based on a clustering algorithm, and improved pCIS (ipCIS) method, are proposed. Several examples are used to demonstrate that each CIS method can handle the complex limit state function and the engineering problem of implicit functions efficiently and accurately.

Determination of Buckling Behavior of Web-Stiffened Cold-Formed Steel Built-Up Column under Axial Compression.

The practice of utilizing cold-drawn steel for structural and non-structural elements has expanded nowadays due to it being lighter in weight, economic section, desirable in fabrication, and its preferred post-buckling behavior over hot rolled sections. The cold-drawn steel section back to the back-lipped channel section has a wide application as a structural member. The fasteners are provided at regular intervals for the long-span structure to prevent individual failures. This study is concerned with the inadequacy of research addressing the behavior of built-up columns. The relevant built-up column section is chosen based on the AISI-S100:2007 specification. Thirty-six specimens were designed and tested by varying web, flange, lip dimensions, spacing between the chords, and battened width experimentally subjected to an axial compression. Comparing 36 experimentally buckled specimens with the model generated by Finite Element Method accompanied with ASI-recommended two direct strength methods (DSMs). The DSM comprises the step-by-step procedure incorporated with the elastic, critical, and global distortional interaction. Based on the performed reliability analysis, such as the experimental, analytical, and theoretical studies, the failure load, buckling mode, the economic section, and design rules were proposed. Four suitable sections were selected from the proposal, and the validation study was carried out. From the validation study, experimental values were found to be 1.072 times the FEM values, and DSM values were found to be 0.97 times the FEM values. Based on the significant findings of this study, the proposed design recommendation and the corrected value for DSM are suitable for designing back-to-back stiffened columns.

Systematical Collision Avoidance Reliability Analysis and Characterization of Reliable System Operation for Autonomous Navigation Using the Dynamic Window Approach

Abstract Dynamic window approach (DWA) is one of the most widely used algorithms for local path planning and autonomous navigation. Although many successful examples have been shown under various operation conditions, to the authors' best knowledge, there is a lack of systematic reliability analysis, its further design improvement, and systems operation guidelines for meeting reliability requirement under different operation conditions. Several goals can be defined for a successful path planning and autonomous navigation. Among them, assurance of the collision avoidance and reaching the goal with less time are pivotal requirements, yet such reliability analysis is rarely conducted in a rigorous manner. Furthermore, design improvement and systems operation design based on rigorous reliability analysis can hardly be found in this area. This paper addresses such a research gap for autonomous navigation reliability analysis and further conducts design improvement and characterizes systems operation conditions for meeting the collision avoidance reliability using the DWA. To address the technical challenges associated with limited number of simulations or experiments, reliability analysis is conducted using Bayesian statistics combined with the Monte Carlo simulation (MCS). Design improvement and reliable operation conditions can then be conducted based on the reliability analysis. Results indicate that performance reliability of the DWA is sensitive to its parameter configuration, which can be improved through reliability-based design optimization. With characterized collision avoidance reliability constraints, performance reliability of the DWA can be ensured through adjusting its operation parameters.

Fusion Deconvolution for Reliability Analysis of A Flywheel-Battery Hybrid Energy Storage System

A hybrid flywheel-battery energy storage system is able to smooth the battery charging/discharging; harmful impact can be filtered by the flywheel to reduce battery damage and extend battery life. However, due to extremely high rotating speed of the flywheel, the hybrid storage system is often subject to mechanical failures in the flywheel transmission system. Therefore, it is critical to detect unexpected faults in the flywheel transmission to ensure the normal operation of the hybrid energy storage system. To this end, in this study a new fusion deconvolution is proposed to perform reliability analysis on the hybrid flywheel-battery energy storage system. Firstly, the Deterministic Random Separation (DRS) is used to simplify the sensory signal components to obtain the fault impulse. Then, the multipoint kurtosis (MKurt) of the impulse is used to determine the parameters of the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) model. Lastly, the envelope analysis of the signal filtered by the MOMEDA deconvolution is carried out to obtain the characteristics of the periodic fault pulses. Both numerical and experimental results demonstrate that the proposed fusion deconvolution method produces satisfactory diagnostic performance. Compared with existing popular deconvolution algorithms, the proposed method proposed improves the extraction of the periodic fault pulses.

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system.

In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

Performance reliability analysis of multi-state degraded system with improved Lz transform

Universal Generating Functions and Lz transformations have been widely used in the reliability modeling of multi-state systems. In order to solve the problem of complex calculations due to the dense random combination of multi-state performance parameters in the Lz transformation, a screening function is defined before the Lz transformation, and the screening function is combined with the performance threshold to screening the state performance parameters in advance, and the process is simplified through the screen matrix and the screen block diagram, effectively reduce the combined dimensions and quantity, improve the efficiency of reliability analysis, and combine with specific examples for application verification.

Reliability checking and performance analysis of stake holder of E-SCM using machine learning and petrinets

Electronic commerce or E-commerce (sometimes written as E-commerce) is a business model that lets firms and individuals buy and sell things over the internet.E-commerce provides the sellers with a global reach. They remove the barrier of place. Now sellers and buyers can meet in the virtual world, without the hindrance of location.Supply chain management is coordination of all supply activities of an organization from its suppliers and partners to its customers efficiently and effectively. Electronic supply chain management (E-SCM) is collaborative use of technology to improve the operations of supply chain activities as well as the management of supply chains. The advantages of E-SCM are many, and include timely order-processing, improved inventory tracking and management, improved accuracy in order fulfillment. But which E-SCM is a reliable one is a bothering question of a customer. The customer is the most important and integral part of the supply chain. Neglecting the customer in this chain can cause breakdown of the entire process.The electronic supply chain provides retailers and their suppliers a medium for instant communication. But can a customer rely on a paticular retailer and can the customer expect his product on time. This reliability can be checked by using Bayesian appoach. Then petrinet is used to check the performance of retailers, manufacturers and suppliers and find out when the product will reach the customer through this E-SCM. Using web design tools a webpage is created to give information about supplier. The given information is checked in the background for its authenticity. Later the delivery details will be sent to customer only if information is authentic. This will benefit the customers from fraud sites and help them find a reliable retailer and improves reliability of customer on retailer. Also helps customers in taking decision to place the orders. If order is placed then predicts the number of days taken to deliver that product to customer. Therefore helping the customer to keep track of the order. Hence overall satisfaction of the customer is improved.

Evaluation of the Main Causes of Diesel Engine Injector Failure using Fault Tree Analysis

The maintenance of the diesel engine parts in any electric power station contains many problems that lead to stopping. Several reasons lead to such problems; these reasons should be analyzed and evaluated in order to eliminate their effects. This paper is based on evaluation of the main causes that lead to diesel engine injector failure as a main part of electric power stations, using fault tree analysis (FTA). The FTA is the most broadly utilized strategies in the industrial area to perform reliability analysis of complex designing frameworks. A fault tree is a logical representation of the relationship of basic events that lead to a given unwanted event (i.e., top event). Starting with introducing the FTA and how it could be utilized in analyzing the reasons of main issues that lead to that the injector stops working, in which the probability of occurrence of each issue is calculated. The application of the root cause analysis principle of diesel engine injector failure in diesels Haditha station is chosen as a case under study. According to the probabilities’ evaluation by using the FTA based on of the causes under the top event its value was (0.80). It is concluded from the analysis that the three largest values of the occurrence of a problem with the diesel engine injector are 0.50 for fuel problems, 0.20 for overloads, and 0.18 for nozzle head corrosion. Plant management can define a specific plan with taking into consideration the calculated value of each cause in order to reduce their impacts and to avoid long downtime hours compared to operating hours. Finally, recommandations are suggested to overcome these causes.

An intelligent operational supervision system for operability and reliability analysis of operators manual actions in task implementation

In the paper, we present a framework for comprehensive operational feasibility analysis from both functional and structural safety perspectives using the measures of hazard and reliability. Within the framework, a pattern-matching algorithm is proposed for operation supervision and underlying human error mode identification using procedure-based action library. An intelligent operational supervision system is further developed in integration with operational mission reliability analysis. The capabilities of procedure-guided operation navigation and supervision as well as operability and reliability of operators manual actions are validated by a case study of reactor boron and water makeup system for manual makeup task implementation in QT virtual simulation environment. The validation results show that the intelligent operational supervision system is capable of procedural operation navigation and supervision. The trend impact of proactive operations can be effectively profiled using the measures of reliability in synchronous prediction and supervision.

Cost-effective test set-up for the real-time measurement of the triboelectric energy harvester

Operational parameters, the force and the frequency of contact affect the triboelectric energy harvester output performance. There is a need for the precise control of input parameters and the real-time measurement of the output parameter to quantify the effect of operating parameters. In this paper, an in-house developed cost-effective experimental set-up (approximately $2600) is presented, enabling the variation in contact frequency and measures of the impact force, voltage and current in real-time. The percentage of uncertainty in the measurement of voltage, current and force are 1.63%, 1.29% and 1.83%, respectively. The proposed set-up can be used to perform a reliable performance analysis and comparison of the vertical contact-separation mode triboelectric energy harvesters by real-time measurement of input and output parameters with precise control of input parameters.

Reliability modelling and performance analysis of bio-coal manufacturing system with deviation in demand

Reliability modelling and performance analysis of bio-coal manufacturing system with deviation in demand

Cross-cultural adaptation and validation of the Infant Feeding Style Questionnaire in Brazil.

We cross-culturally adapted and validated the Infant Feeding Style Questionnaire (IFSQ) in Brazil. The cross-cultural adaptation and content validity assessment was conducted in five steps: translation, synthesis of translations, back-translation, evaluation by experts, and pre-test. To evaluate reliability, construct validity, and floor and ceiling effects, interviews were conducted with 465 mother-infant pairs at Primary Health Centers in the Federal District, Brazil. The mothers answered the Brazilian Portuguese version of the IFSQ (IFSQ-Br), which evaluated four feeding styles (laissez-faire, pressuring, restrictive, and responsive) from 9 sub-constructs. The indulgent style was not evaluated due to time limitation. We performed reliability analysis using Cronbach’s alpha coefficient and construct validity was evaluated through Confirmatory Factor Analysis. Higher means were found in the sub-constructs of the responsive and restrictive styles. The IFSQ-Br presented adequate reliability (α = 0.73) with values for the Cronbach’s alpha coefficient of the sub-constructs ranging from 0.42 to 0.75. In the Confirmatory Factor Analysis, the final models presented good fit, with the Comparative Fit Indices (CFI) ranging from 0.86 to 1.0 and the Root-Mean Squared Error of Approximation (RMSEA) between 0.0 and 0.09. The IFSQ-Br was shown to be a valid and reliable questionnaire to evaluate maternal feeding beliefs and behaviors in Brazil. Future studies should evaluate the psychometric properties of the indulgent style and include mother-infant pairs from different cultural contexts in Brazil.

Reliability assessment for the Generalized Slenderness based Resistance Method for circular and elliptical hollow sections

AbstractIn the framework of the RFCS project HOLLOSSTAB, a new design method for determining the resistance of hollow section members has been developed. This method links a generalized slenderness, calculated under the effect of the combined loading, to the resistance of the cross‐section and of the member. This so‐called “Generalized Slenderness‐based Resistance Method” (GSRM) has been shown to provide accurate predictions of experimentally and numerically determined resistances of hollow sections. Nonetheless, the real benefit of this new design method could only be evaluated after a conscientiously performed reliability analysis that is presented in this paper. First, the statistical data of the input variables (cross‐section dimensions, material law) obtained through measurements are presented and compared to the assumptions of Annex E of EN 1993‐1‐1:2020. Then, the reliability analysis is outlined. In particular, attention is given to specific problems resulting from the fact that several basic parameters of GSRM have to be determined numerically (for example the critical loads under combined loading). In addition, the sensitivity of the resulting partial factors to assumptions concerning the statistical distribution of the input variables is studied and discussed.

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network.

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.