Value Of Failure Probability Research Articles

Reliability and criticality analysis of a large-scale solar photovoltaic system using fuzzy-fault tree analysis approach

Over time, solar Photovoltaic (PV) systems experience a decline in performance and reliability due to various environmental factors. Fault Tree Analysis (FTA) can be used to assess the reliability of these systems and identify faults and failure modes that can significantly impact the entire PV system’s performance. However, in practice, obtaining accurate failure probability values for the components of a solar PV system is challenging since systems operate in an ever-changing environment, resulting in a scarcity of data for statistical estimation. This paper proposes a fuzzy theory-based FTA approach to obtain the failure probabilities of the faults more accurately. The Fuzzy-FTA methodology converts experts’ subjective opinions expressed in linguistic terms into a Failure Possibility Score (FPS), which is then converted into a failure probability value. The results of the proposed approach are compared to those obtained through the conventional FTA to assess its effectiveness, applicability, feasibility, and efficiency. Soiling, dust accumulation, inadequate system maintenance, bad system configuration, bird dropping, delamination, improper installation, shading, grounding, and discoloration are the most critical faults and impact on the performance and reliability of solar PV systems.

Third moment method for reliability analysis with uncertain moments characterized as interval variables

Traditional reliability analysis aims to compute the failure probability based on probability distribution functions, which are constructed using the moments of random parameters. In practice, however, appropriate samples may be insufficient to obtain deterministic values of the moments of all random variables and the exact value of failure probability cannot be obtained. To be consistent with the reality, the uncertainties in moments can be measured as interval variables, and then the bounds of failure probability should be evaluated. In this study, an idealized case is considered, where there is at most one imprecise moment associated with any given input random variable. A third moment method is proposed with uncertain moments measured as interval variables, and is named as TMI method. The proposed TMI method is straightforward including only four steps. Firstly, the derivative of performance function to random variables having uncertain moments is calculated, with the random variables set to be their mean values. Secondly, the values of uncertain moments for computing the bounds of failure probability are determined. Then, with inverse normal transformation defined based on the moments, the performance function at the bounds in Gaussian space is directly constructed. Finally, bounds of failure probability can be evaluated by two times of classical reliability analysis corresponding to the constructed performance functions. The application of TMI method is validated by numerical examples, including high-dimensional and strong nonlinear problems.

Investigating the Reliability of an Existing Top Angle and Seat Pad Semi-Rigid Connection System Using Advanced Modelling Techniques

The investigation introduces an advanced model procedure for evaluating the structural reliability of semi-rigid frame connections in existing aged buildings. Specifically, the focus is on the top angle and seat pad (TA–SP) semi-rigid connection, which was not initially considered in the current design standards. The approach employs a plastic hinge model to predict the ultimate strength of the connection and its beam-to-column behaviour. In order to increase computational efficiency, the investigation leverages the nonlinear behaviour of the finite element (FE) model to validate critical parameters. The statistical properties of the existing connection were obtained based on past experimental data, highlighting the weakest elements in the system. The first-order and second-order reliability methods and Monte Carlo simulations were employed to estimate the reliability index. Percentile errors were assessed to understand their impact on higher-order interactions. This new technique of identification quantifies the probability of the system failure interactions. Notably, a 45% lesser error aligns with the target reliability index, while a 114.5% larger error indicates a significant deviation from actual failure probability values. Each methodology introduced adheres to the current standard, and the system reliability analysis provides a vigorous conclusions scheme framework for assessing the existing TA–SP semi-rigid connection.

Acceptance Sampling Plans based on Percentiles of Exponentiated Inverse Kumaraswamy Distribution

Objectives: To prepare the percentile-based acceptance sampling plans for the Exponentiated Inverse Kumaraswamy Distribution (EIKD) at a specific truncation time to inspect the defective lots corresponding to the desired acceptance level. Methods: The failure probability value is estimated using the cumulative probability function F(.) at time ‘t’ which is converted in terms of the scale parameter σ as 100th percentile using quantile function. The minimum size of the sample, Operating Characteristic (OC) and the minimum ratios are calculated for a required levels of consumer’s as well as producer’s risk. Findings: The percentile-based sampling plans are obtained through the minimal size of the sample ‘n’ under a truncated life test with a target acceptance number c in a manner that the proportion of accepting a lot which is not good (consumer’s risk) would not be more than . These values are calculated at The function of probability of acceptance for variations in the quality of a lot (OC function) L(p) of the sample plan are evaluated for the acceptance values of c=1 and c=5. The minimum ratio values are calculated for the acceptability of the lot with producers’ risk of using the sampling plan. Novelty: The modernity of this study is the designing of the acceptance sampling plans to a non-normal data using an asymmetrical distribution that has all three shape parameters. Also, the monitor of the implementation and suitability of statistical quality control and process control aspects using Exponentiated Inverse Kumaraswamy Distribution when compared to other asymmetrical distributions which has at least one scale parameter. Keywords: Sampling plans, Consumer's risk, Operating characteristics function, Truncated life tests, Producer's risk

Probability of Failure for Fixed Offshore Structures Determination: An Analysis of a Braced Monopod and 4-Legged Platforms with Respect to Industry Standards

The structural integrity is susceptible to a combination of statistical and engineering design uncertainties that may remain flexible as long as the structure can successfully manage the encountered load. There are many methods for determining the probability of failure (POF) in the oil and gas sector, but they all have particular constraints. Therefore, this study is a quantitative risk assessment to establish a more reliable method for calculating the POF value. This study chose and evaluated a sample of a braced monopod and 4 legged offshore structures for global non-linear analysis. The most reliable form of distribution was predetermined, and the appropriate integral equation was applied and computed based on the load and strength model conditions. The result of the testing of stress-strength interference for 12 directions and for 8 directions of wave impact in POF indicated that the platform remained intact and reliable compared to the L2 exposure level for the reliability target of POF recommended by ISO 19902, ISO 19901, and PETRONAS. Furthermore, this established that the applied integral equation provided a high degree of confidence in calculating the new POF. This newly created approach will enable the structural engineer to outline action items as part of the organisation’s risk management process.

Series–Parallel System Analysis and Reliability Research of Grid Structures Considering Adaptive Dynamic Bounding Threshold

There are many bars in grid structures, and their topological relationships are complex, so the workload of calculations involved in researching their reliability is large. In this paper, based on the geometric topological relationship within a grid structure, a series–parallel system analysis of the structure is carried out by means of node analysis, constraint grade division, and hierarchical combination analysis, and the series–parallel analysis results for the structure are used to judge the failure of the structure. In order to avoid the problem of missing important components due to unreasonable selection of the bounding threshold when identifying failed components, an adaptive dynamic bounding threshold method with high accuracy is introduced. The failure probability value of the failure path is calculated using the direct numerical integration method, and the reliability of the whole structure is obtained. Finally, the validity of this method is verified using an example of a square pyramid grid structure. The results show that the method is feasible for series–parallel system analysis, structural failure judgment, and reliability analysis of a grid structure.

Reliability Evaluation of EB-PVD Thermal Barrier Coatings in High-Speed Rotation and Gas Thermal Shock

The uncertain service life of thermal barrier coatings (TBCs) imposes constraints on their secure application. In addressing this uncertainty, this study employs the Monte Carlo simulation method for reliability evaluation, quantifying the risk of TBC peeling. For reliability evaluation, the failure mode needs to be studied to determine failure criteria. The failure mode of high-speed rotating TBCs under gas thermal shock was studied by combining fluid dynamics simulations and experiments. Based on the main failure mode, the corresponding failure criterion was established using the energy release rate, and its limit state equation was derived. After considering the dispersion of parameters, the reliability of TBCs was quantitatively evaluated using failure probability and sensitivity analysis methods. The results show that the main mode is the fracture of the ceramic layer itself, exhibiting a distinctive top-down “step-like” thinning and peeling morphology. The centrifugal force emerges as the main driving force for this failure mode. The failure probability value on the top side of the blade is higher, signifying that coating failure is more likely at this location, aligning with the experimental findings. The key parameters influencing the reliability of TBCs are rotation speed, temperature, and the thermal expansion coefficient. This study offers a valuable strategy for the secure and reliable application of TBCs on aeroengine turbine blades.

Remaining service time prediction for corroded shell of a steel tank used for liquid petroleum fuels storage

A numerical example allowing for effective forecasting of the remaining service time for corroded steel shell of a tank used to store liquid petroleum fuels is presented. This time is interpreted here as a period of time counted from the moment of an obligatory tank shell inspection to the moment of the anticipated shell failure understood as the loss of the capacity to safely resist the loads applied to it. Reaching the limit value of failure probability, i.e. the highest probability of failure acceptable to the tank user, is in this approach a determinant of such failure. The detailed considerations pertain to a typical on-the-ground storage tank equipped with a floating roof, located in one of fuel depots in the south of Poland. The forecast has been prepared based on measurements of the random thickness of tank shell weakened by corrosion and measured after 27 years of service time. In the recommended analysis fully probabilistic computational procedures have been applied. This led to a more credible and less conservative service time assessment than the one usually determined via the traditional standard approach. For comparative purposes qualitatively different but formally corresponding safety measures have been applied to describe the obtained results.

Reliability analysis of parted rope injuries during ship mooring operation under neutrosophic environments

At the national and international levels, many efforts have been made over recent decades to ensure the safety of ships at sea. In marine environments, crews face serious concerns during parted rope injuries during ship mooring operation. This problem can be tackled with the help of failure probability analysis which plays a vital role in detecting and eliminating the aforesaid threats in maritime transportation. Therefore, determining the fundamental causes of parted rope injuries during ship mooring operation is essential for maritime safety researchers. When the failure probability values of basic events are exact, conventional fault tree analysis has previously been successfully applied to determine the cause of failure of the top events. However, sometimes the failure information provided by different analysts for mooring operation may be uncertain and inconsistent. To properly address this important issue, the proposed research reflects a novel approach by extending crisp sets, fuzzy sets, and intuitionistic fuzzy sets into neutrosophic sets. The aforesaid approach is used to compute failure probabilities and reliability intervals for truth-membership, indeterminacy-membership and falsity-membership functions, along with a measure of how much each basic failure event contributes to the failure of the top event. The results obtained by incorporating the proposed approach are compared with those obtained using earlier approaches.

Optimal scheduling of BESS for congestion management considering reliability and OTS

The present research explores transmission congestion control in Battery based Energy Storage Systems (BESS) integrated grids using Optimal Transmission Switching (OTS). The oversimplification of underlying assumptions in prior OTS-based congestion management strategies leads to excessive line switching which is undesirable. Hence, a novel reliability constrained alternating current optimal power flow (AC-OPF) based OTS is presented to mitigate the line congestion in the BESS integrated grids. The suggested approach is solved to acquire the OTS solution for 24 h time frame with the primary goal of minimising generating costs subjected to ac power flow equality, BESS, reliability, and transmission congestion constraints. The proposed approach makes use of the integer variable to represent the transmission switching operations and is solved by using a mixed-integer non-linear programming (MINLP) solver. The inclusion of reliability constraints ensures the system reliability by limiting the value of load failure probability to a pre-specified limit. The feasibility of this approach is validated by testing it on a standard IEEE test system for various loading scenarios. The results exhibit that coordinated charging/discharging of BESS supports reducing the number of lines switchings required for congestion management compared to the existing approaches, substantially.

Enhancing Electric Vehicle Reliability and Integration with Renewable Energy: A Multi-Faceted Review

Electric vehicles (EVs) are gaining attention due to their zero carbon emissions, but concerns about their reliability, especially critical components, persist. Previous research has primarily focused on EV drive motor reliability, neglecting the motor controller. To address this gap, this study assesses the reliability of the entire motor system in electric vans, including both drive motor and motor controller components. It predicts failure rates for these components, highlighting vulnerabilities and shortcomings in existing research, which can inform future design and maintenance. In addition, the integration of EVs and renewable energy resources has garnered attention, but concerns about component reliability have arisen. A novel approach called " Innovative Incentive-Driven Fuzzy Fault Tree Analysis " (IIFFTA) is introduced for power systems incorporating EVs and renewable energy, addressing vague and imprecise events and data deficiencies in conventional fault tree analysis. IIFFTA considers different component failure rates and probability values of fault occurrences, offering a more effective risk assessment method. Lastly, plug-in electric vehicles (PEVs) impact distribution systems, and this paper explores distribution feeder reconfiguration (DFR) as a reliability-enhancing strategy for coordinating vehicle-to-grid (V2G) services from PEV fleets within a stochastic framework. The study accounts for uncertainties related to network demand, energy prices, wind power generation, and PEV fleet behavior, employing a self-adaptive evolutionary algorithm (SOS) to address the stochastic optimization problem.

A risk minimization based approach for planning evacuation trip distribution

Most studies on evacuation strategies hitherto focus on routing and the resulting traffic pattern. While doing so, such studies generally consider evacuating people to the nearest shelter location (relief point). In this paper, we illustrate why a strategy of evacuating to the nearest relief point would not be the most risk-averse plan. We present a methodology for planning evacuation trip distribution to potential relief points in a risk-averse manner. The procedure explained would help policy makers to identify relief points where capacity must be improved while preparing for a disaster. We present a risk-minimizing formulation that prescribes evacuation trip distribution and corresponding capacity enhancement scheme (RETDC+) for the relief points. To assess the reduction in risk, we compare this formulation with two other formulations: one where risk-averse trip distribution is performed without enhancing the existing capacities (RETDC), and another where trips are distributed to the nearest relief points (PETDC). We envisage to employ failure probability values based on susceptibility maps that are often prepared for disaster response planning. The methodology is demonstrated for the city network of Gangtok, India which is prone to natural disasters such as landslides and flash floods. We illustrate that the evacuation trip matrix obtained by employing the proposed methodology is 20% more risk-averse for landslide and 27% for flash flood, respectively, than the evacuation trip matrix obtained by a naive distribution to the nearest relief points.

A dynamic evaluation method for slope safety with monitoring information based on a hybrid intelligence algorithm

The variability of soil or rock parameters based on poor information results in an uncertain change in slope failure probability. To solve the negative impact of slope reliability analysis introduced by variability of soil or rock parameters, this article proposes a hybrid intelligence algorithm for analyzing the slope failure probability with Bayes theorem, integrating a one-dimensional convolutional neural network (1D-CNN), and executing Markov chain Monte Carlo (MCMC) simulation with multidepth displacements. We calculate the failure probability through 1D-CNN prediction. The 1D-CNN learns from data generated by numerical models to directly relate input variables to output variables toward obtaining an accurate model of the response surface. The resulting soil or rock parameters are described as probability distributions, which are updated by MCMC simulations based on the multidepth displacement time series. A case study with monitoring displacement along the slope depth is presented to illustrate the effectiveness of our proposed methodology. We find that our method can update the failure probability value and identify the location of potential sliding surfaces with the time series of multidepth displacement. We also find that incorporating changes in Poisson's ratio and elastic modulus seems to have a significant influence on the slope safety evaluation.

Reliability-based design optimization using adaptive Kriging-A single-loop strategy and a double-loop one

With the development of the surrogate-assisted Reliability-Based Design Optimization (RBDO) methods in recent decade, efficiency has been continuously improved by various state-of-the-art learning schemes. However, ensuring sufficient accuracy and feasibility at the optimum is still challenging, especially for real scenarios with complex probabilistic constraints and high fidelity. In order to achieve a good balance between efficiency and accuracy and feasibility at the optimum, both a single-loop and a double-loop adaptive Kriging-based RBDO methods are proposed. By directly approximating the probabilistic constraints using Kriging models in the single-loop method, the original RBDO problem is converted into a trivial deterministic optimization, which can be solved by any type of optimization method. The real values of failure probabilities are estimated by Generalized Subset Simulation (GSS) at the initial construction or during each update of the Kriging models. To further raise efficiency in the double-loop adaptive Kriging-based method, GSS is substituted by an inner-loop Kriging-based failure probability estimation, which includes each local enrichment at the limit states corresponding to the active probabilistic constraints based on an initial global enrichment within the augmented reliability space. Once a certain Kriging model meets the requirement during the refining of all, only the others are kept updating until the accuracy of all are accepted. Four examples are used to demonstrate the performance of the proposed two methods.

Optimization the reliability of emergency safety barriers based on the subjective safety analysis and evidential reasoning theory. Case study

PurposeThis study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.Design/methodology/approachThe emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.FindingsA real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.Research limitations/implicationsThis study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.Originality/valueOur contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

A probabilistic framework of creep life assessment of structural components at elevated temperatures

Current creep life assessment methods of components at elevated temperatures are mainly based on deterministic analysis strategies, which could not achieve the goal of probabilistic evaluation on creep failure. Based on this, a probabilistic framework of creep life assessment for components at elevated temperatures was provided. A method of determining distribution characteristics of material parameters was provided by randomly selecting a group of results at each stress level. Monte Carlo simulation was combined with finite element analysis technology to capture the distribution characteristic of creep rupture life of one typical structural component. Effect of standard derivation of material parameters on creep reliability assessment was discussed. Comparisons between probabilistic and deterministic creep design methods were made. Results indicated that the probabilistic analysis strategy can calculate the specific value of failure probability at various loading conditions, not two values of failure probability (i.e. 100% and 0%) by deterministic analysis strategy. The effect of standard derivation on mean values of effective stress and creep rupture life of the component is dependent on distribution characteristics of material parameters and related variables. A small standard derivation reduces the data scatter of effective stress and creep rupture life of the component.

Application of Probabilistic Models for Multitone Electromagnetic Immunity Analysis

The operational environment of modern electronic systems may include multiple frequency electromagnetic disturbances. However, immunity measurements usually employ single-frequency continuous waveforms (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single-tones</i> ). The performances of two oscillator circuits with different topologies (one simulated and one measured) were used as case studies to investigate immunity to simultaneous single-tone disturbances (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multitones</i> ) using probabilistic Bayesian network models. For the multitone analysis, the noisy-OR model was first used to identify the type of causal interactions between simultaneously occurring single-tones. Probabilistic theories derived from the recursive noisy-OR model, which inherits the independence assumptions of the noisy-OR and any known causal dependence between simultaneously occurring single-tones, were then used to predict the probability of higher order multitone failures. For the two case studies, the probability of three-tone failures was estimated using the single-tone and two-tone failure probability values. An improved adaptive recursive noisy-OR model was also proposed to overcome the practical difficulties of obtaining multitone failure probabilities, from either simulations or measurements.

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.

Reliability Assessment of Heavily Censored Data Based on E-Bayesian Estimation

The classic E-Bayesian estimation methods can only derive point estimation of the reliability parameters. In this paper, an improved E-Bayesian estimation method is proposed to evaluate product reliability under heavily censored data, which can achieve both point and confidence interval estimation for the reliability parameters. Firstly, by analyzing the concavity &amp; convexity and function characteristics of the Weibull distribution, the value of product failure probability is limited to a certain range. Secondly, an improved weighted least squares method is utilized to construct the confidence interval estimation model of reliability parameters. Simulation results show that the proposed approach can significantly improve the calculation speed and estimation accuracy with just very few robustness reductions. Finally, a real-world case study of the sun gear transmission mechanism is used to validate the effectiveness of the presented method.

Causal Approach to Integrate Component Health Data into System Reliability Models

Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.