Importance Measure Analysis Research Articles

Probabilistic assessment of rockburst risk in TBM-excavated tunnels with multi-source data fusion

In recent years, tunnel boring machines (TBMs) have become extensively utilized in underground engineering projects. However, as a prevalent type of dynamic geological hazard, rockburst poses a serious threat to the safety of personnel, equipment, and property. To ensure the safe advancement of TBMs, we integrate the data of microseismic monitoring, TBM excavation, and surrounding rock and establish multiple probabilistic assessment models of rockburst risk using probabilistic ensemble learning and quantum particle swarm optimization. In order to identify the most optimal model for geological engineers, a comprehensive evaluation system is devised based on metrics like accuracy, Cohen’s kappa, and F1-score. This system provides a thorough assessment of the models’ global and local generalization performance. The results indicate that the classification and regression tree-extremely randomized trees (CART-ERT) hybrid model achieves the highest score, demonstrating superior generalization performance with an accuracy of 93.06% and a Cohen’s kappa of 0.9074. Moreover, the F1-scores for no rockburst, low rockburst, moderate rockburst, and strong rockburst are 0.9412, 0.9444, 0.9189, and 0.9189, respectively. Based on the operational framework of the CART-ERT hybrid model, a user-friendly graphical user interface (GUI) system is developed. This significantly enhances the practicality and deployability of the model. Through application in a TBM diversion tunnel project in Xinjiang, the on-site early-warning accuracy of rockburst with the GUI system reaches 90%. Notably, the GUI system maintains high early-warning sensitivity and reliability for high-intensity rockburst such as moderate and strong ones. Lastly, to enhance the model’s interpretability, a variable importance measure (VIM) analysis on 14 features extracted from three types of heterogeneous data is conducted to assess their contributions to the early warning of rockburst. We discover that the cumulative energy change rate of microseismic events exhibits the highest importance score, indicating its crucial role in rockburst early warning.

Phased-Mission Reliability and Importance Measure Analysis for Linear and Circular UAV Swarms

The phased-mission reliability of unmanned aerial vehicle (UAV) swarm refers to its capability to successfully complete the missions of each phase under specified conditions for a specified period. In order to study the reliability of phased-mission in UAV swarm, this paper firstly studies the reliability of a single UAV under fault coverage. Then, considering the mission characteristics of UAV swarm, the consecutive k-out-of-n system is studied to model and predict the reliability of UAV swarm phase mission. Some importance measures are introduced to analyze the influence of UAV in different positions on the reliability of the whole system. Finally, numerical examples of linear and circular UAV swarms are given to demonstrate and verify the correctness of the model. The reliability modeling established in this paper can predict the phased-mission reliability of UAV swarm scientifically.

Integrated availability importance measure for multi-state complex systems analysis

As an important tool to evaluate the key components of the multi-state system, the importance degree is essential in the system reliability design stage, to provide the basis for the system reliability improvement and maintenance. To accurately improve the reliability of the system, this paper provides an importance measure analysis method that comprehensively considers the state and maintenance effects. To measure the impact of components on the system more comprehensively, this paper proposes an Integrated Availability Importance Measure (IAIM) to evaluate the relative importance of components by combining component state probability, state transition rate, repair rate, and state repair transition rate and considering the impact of component reliability and maintainability on the performance of multi-state systems. Considering the randomness of system operation, a Monte Carlo simulation based IAIM analysis method for a multi-state system was developed. Taking the series system and the hybrid system as examples, the IAIM of the component is simulated and analyzed. Comparing IAIM with Integrated Importance Measure (IIM) and performance Utility Importance measure (UI), among them, UI considers the impact of the state on performance, while IIM considers state transition on the basis of UI, but does not consider the impact of maintenance. IAIM is more comprehensive than UI and IAIM. It can be seen that IAIM is different from importance measures based on reliability. This is because the IAIM fully examines the impact of component reliability and maintainability on multi-state systems. The IAIM improves the traditional shortcomings of only considering component reliability, and provides a more comprehensive way to evaluate the system.

Sensitivity Analysis of Random Parameters for Earthquake Response of Hakka Tulou in Fujian

The seismic response analysis of the Fujian tulou structure considers the influence of six input random variables of the tulou structure. It obtains the seismic response of the apex displacement, the maximum inter-story displacement angle, and the base shear force of the tulou structure. An analysis method based on moment-independent importance measure is proposed and applied to the stochastic parameter sensitivity (importance measure) analysis of structural seismic response. The results show that the importance metric analysis method of the seismic response of the structure based on the moment-independent importance measure analysis method generates consistent outcomes with the importance measure method of the variance. However, the method in this study can better reflect the actual earthquake response of the tulou structure, and it is also an efficient method.

Integrated availability importance measure analysis for multi‐state manufacturing system based on proportional hazards model

AbstractAvailability is a comprehensive reflection of the overall performance for a manufacturing system, so it is necessary to evaluate the impact of component changes on the availability of a manufacturing system. However, most of the existing importance measures are based on reliability change or state degradation to measure the relative importance of components, which cannot be comprehensively analyzed for multi‐state manufacturing systems. The availability of a multi‐state manufacturing system is influenced by time and state. Therefore, considering the synergistic effect of time and state, this paper proposes an Integrated Availability Importance Measure (IAIM) based on proportional hazards model combining with component state probability and state transition rate. This measure fully examines the time parameters, state parameters and maintenance impact of multi‐state systems, and can comprehensively evaluate the relative importance of multi‐state coherent system components, provide theoretical basis for further developing the availability maintenance strategy, and indicate the improvement direction for further improving the reliability of multi‐state manufacturing systems. A numerical example is given to prove its feasibility, and the influence of related parameters is investigated by sensitivity analysis. Finally, a real‐world case study is used to illustrate its applicability.

Importance measure analysis of design variables and uncertain parameters in multidisciplinary systems

The traditional reliability-based multidisciplinary design optimization consumes large computation resources due to a large number of design variables and uncertain parameters in multidisciplinary systems. Therefore, the screening of important design variables and uncertain parameters contributes to reducing the difficulty of optimization. However, the current methods ignore the interaction between design variables and uncertain parameters. Via a probabilistic and nonprobabilistic reliability model, the work in this paper establishes the concept of a comprehensive reliability-based importance measure that considers both design variables and uncertain parameters. An efficient method based on the Bayesian theorem is applied to calculate the above-mentioned indices of the proposed importance measure, which can estimate importance measure accurately adopting the same samples with reliability analysis. Therefore, the probabilistic method has faster convergence speed than traditional methods. The nonprobabilitic analysis method can promote the computation efficiency but ensure accuracy when the system performance functions are monotonic. Furthermore, the developed approach contributes to decreasing the time cost of reliability-based optimization in multidisciplinary systems. Additionally, the developed approach can also be applicable to single-disciplinary systems. Moreover, two practical engineering examples and the multidisciplinary design of a hypersonic wing are employed to demonstrate the validity and applicability of the proposed method.

Time dependent reliability analysis for a critical reactor safety system based on fault tree approach

Loss of coolant in the operation of any nuclear power plant will eventually become the primary source of hazard in the sequence of events leading to reactor core uncovery. Subsequent failure in removing the nuclear decay heat and preventing the core uncovery will further lead to loss of coolant accident (LOCA). In conjunction to this safety concern, it is crucial that the reliability of the plant emergency core cooling system is systematically and critically analysed. This article presents a case study on the time dependent reliability analysis for a safety injection system (SIS) of an advanced pressurized water reactor, based on the failure mode and effect analysis (FMEA) and fault tree (FT) analysis approach. The identified generic data for component reliability are carefully reviewed and used in this study. Based on the base case model, sensitivity and importance measure analysis for basic events are performed and the outcomes gained are presented and discussed. From the analysis, it is shown that the safety injection pumps of the SIS contribute significantly to the reliability of the system. In the short (at 0.5 hour) and long (7.0 hours and 72.0 hours) run, safety injection pumps are critical and influence the reliability of the SIS the most. The SIS’s FT logic model that has been developed and calculated shows the usability of the FMEA and FT approach that are implemented in analysing SIS time dependent reliability.

Reliability analysis based on dynamic Bayesian networks: A case study of an unmanned surface vessel

Unmanned surface vessels (USVs) have been widely used in various activities to date. Reliability is particularly significant for USVs because of the increasing complexity of USV tasks and their characteristic that cannot be repaired during task execution. This study aims to analyse the reliability of USVs' overall system for providing an optimisation reference for their reliability improvement in the design phase. The method for modelling and analysing the reliability of complex systems based on Dynamic Bayesian Networks (DBNs) is studied. The reliability of a USV for seafloor topography survey is analysed. The reliability model of the USV overall system is established by mapping the Dynamic Fault Trees (DFTs) into DBNs considering the system logical structure, redundant configuration and dynamic behaviour. Reliability analysis, including reliability evaluation, importance measure and sensitivity analysis, are conducted on the basis of DBN inference. The overall reliability parameters of the USV are obtained to determine the critical subsystem and components. This research may provide a reference for the reliability design and optimisation of USVs.

Resonance reliability and importance measure analysis of multi-span pipe conveying fluid embedded in temperature-varying matrix

Pipe system conveying fluid faces the problem of multi-order resonance failure caused by broadband excitation. For solving above problem, the dynamic stiffness method is employed to solve the dynamic equations of multi-span pipes considering the temperature effect. Combining the obtained natural frequency and the rule of resonance failure of pipe system, a multi-order anti-resonance system reliability model is established in this paper. To analyze the effect of input variable uncertainty on the probability of system resonance failure, the variance-based importance measurement index is further established. By introducing the active learning Kriging (ALK) model, the resonance failure probability and importance measurement index can be calculated efficiently. The effects of fluid flow velocity, pressure and temperature on the probability of pipe resonance failure are analyzed in detail, which has significant guidance for the anti-resonance optimization design of pipes.

Estimation of statistical moments of performance functions using efficient importance measure

ABSTRACT This paper introduces an efficient methodology for evaluating the first four central moments of performance functions in structural reliability assessment. The procedure of the proposed method is divided into three major steps. First, a performance function is approximated as the sum of univariate and bivariate functions by the bivariate dimension-reduction method. Next, a new criterion based on importance measure analysis is proposed for dividing the bivariate functions into significant bivariate functions and non-significant bivariate functions, and then the new approximation of the performance function is obtained. Finally, formulas are derived for evaluating the first four central moments of the proposed performance function, in which the first four raw moments of the univariate and bivariate functions can be achieved by a point estimating method in a standard normal space. The effectiveness and efficiency of the proposed method are demonstrated through three numerical examples.

Reliability and importance measure analysis of networks with shared risk link groups

We consider the problem of assessing the reliability and the Birnbaum importance measure of complex networks under the shared link risk group (SRLG) failure scenario. With a view to the fact that SRLG failures can cause a global system breakdown, the reliability and the importance measure analysis is of fundamental importance to the study of critical infrastructures such as sensor and cross-layer networks, supply chains, and other complex systems that support the essential functioning of our society and economy. The mathematical complexity of the reliability and the importance measure calculation implies that one has to rely on approximation techniques, since no analytical method for solving this problem in reasonable computation time is known to exist. This study will build upon the Permutation Monte Carlo paradigm. The major advantage of the proposed solution is that it allows us to obtain reliable estimates of both the network reliability and the Birnbaum importance measure using the same algorithmic machinery. We show that the suggested algorithm is easy to implement and that the method is scalable to meet real-life network sizes.

A Novel Approach Based on Stochastic Hybrid Fault Tree to Compare Alternative Flare Gas Recovery Systems

Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not the possibility to treat or use this type of gas. Concerns about global warming led to several initiatives for reducing flaring or even eliminating combustion. Treating flare gas was made possible by the introduction of flare gas recovery systems that have become increasingly obligatory. Most solutions add a flare gas recovery system to an existing flare system. In a typical scenario, after analyzing the existing facility and collecting the necessary data, alternative designs are proposed and criteria are determined to make a choice between the proposed alternatives. In this paper two designs of a gas control system are proposed, and reliability was chosen as the deciding factor. Using repairable dynamic fault trees, the failure models of the two designs have been implemented. Afterwards, a novel hybrid technique, the Stochastic Hybrid Fault Tree Automaton, is used to model the working conditions in which the system operates, with the aim to achieve a more realistic assessment and evaluate the disaster likelihood associated to these failures. It is shown that the latter enables a richer analysis where the effects of failure can be better assessed. This is important for correct choice between design alternatives because, as shown in the case study, the results of the two analyses can lead to contrasting conclusions of the solution to adopt. Further investigations have been carried out focusing on the safety sub-systems and on the basic events in each design. The Importance Measure analysis revealed that some of the components were responsible for most of the critical failures, thus locating some areas of possible design improvement.

Performance enhancement of renewable energy systems subject to units’ degradation and performance dependence

A performance enhancement model for multi-state renewable energy systems is introduced and developed in this paper. This model’s objective is to provide a maintenance decision support, taking into account units’ degradations and system interdependencies. Performance enhancement actions consider both preventive and corrective strategies and the model’s thresholds are based on units’ performance. The Universal Generating Function was used to assess unit performance, dependence and degradation. A case study is provided based on a simple series–parallel wind energy multi-state system, and the results show how this decision support model can be cost effective and enhance the global performance of the system. Afterwards, importance measure analysis was used to assess the performance thresholds of this model.

Fault Tree Analysis With Interval Uncertainty: A Case Study of the Aircraft Flap Mechanism

This article proposes an uncertainty analysis method for evaluating the reliability of a system and calculating the relative importance of the system inputs when the probabilities of basic events are characterized by intervals. The reliability requirement or the safety criterion is first considered in the system reliability assessment. A nonprobabilistic reliability index, called R-index, is defined to evaluate the reliability of the system. Two importance measure indices, called location effect and size effect, are introduced for estimating the influence of the probability uncertainties of basic events on the R-index, thus identifying influential and noninfluential basic events and comparing their relative importance. Then, the proposed method is applied to an illustrative fault tree example and the fault tree analysis of unilateral asymmetric movement of an aircraft flap mechanism, thereby explaining the significance of this method. The results show that the proposed method provides a viable tool for system reliability and importance measure analysis when probability intervals are involved.

Importance Measure Analysis Method for Maintenance based on Mutil-Function Testability States

Maintenance order plays an important role in industrial field, which influences multiple factors such as costs. Current method is based on importance measure using the fault tree analysis. Simply based on the fault tree of the failure condition, such method ignore some vital information hidden in other testability states, and may results in unsuitable maintenance order. This paper puts forward a novel method to evaluate importance measure based on multi-function testability states, which can be used to optimize the maintenance order. To illustrate the utility of this method, the article uses a vehicle model, and compares the different results to validate the effect of the proposed importance measure. This method can be utilized to evaluate the maintenance order containing multiple functions.

Modeling and Simulation Approaches for Reliability Analysis of Drilling Machines

Drilling, as the initial stage of exploitation in open-pit mining, has a significant effect on subsequent mining stages. Any failure in a drilling machine stops the drilling operation and, as a result, all following operational phases come to a halt. The drilling system is the most important part of the drilling operation. This study selected two new rotary-type drilling machines (A and B) in Sarcheshmeh Copper Mine in Iran as a case study to perform a reliability and availability analysis of their drilling systems. The drilling system was divided into three independent subsystems; drill string, rotary head, and mast. The reliability modeling used a reliability block diagram and Monte Carlo simulation. Reliability and availability were evaluated, and the importance of each subsystem estimated. A preventive maintenance schedule was proposed using weighed reliability allocation to achieve the reliability target. The results show the mast is the most reliable subsystem. Importance measure analysis indicates the drill string and rotary head are the most critical subsystems of machines A and B, respectively. Finally, under the proposed maintenance policy, the reliability of the drilling systems of machines A and B is improved.

Study on safety of multi-satellite launch mission based on importance measure

For multi-satellite launch mission, the velocity and direction of each satellite at separation may deviate from nominal value, which will introduce uncertainty to the distances between satellites after separation, and influence the safety as a result. In order to improve the satellite rocket separation safety of multi-satellite launch mission, importance measure analysis technology is employed to analyze the distance between satellites after separation. A new method is proposed to calculate the importance measures, numerical example shows that the efficiency of the proposed method is higher than the traditional Monte Carlo method and Saltelli’s method. For the situation that distance between satellites is too close after separation, importance measure analysis technology can be used to identify the input variables those have greater influence on the distances between satellites, then the distribution parameters of those input variables can be optimized, which can increase the minimum distance between satellites, and decrease its uncertainty as well. As a result, the safety during separation can be insured.

A new moment-independent importance measure analysis method and its efficient algorithm

A new moment-independent importance measure analysis method and its efficient algorithm

Resilience based importance measure analysis for SoS

In a system of systems (SoS), resilience is an important factor in maintaining the functionality, stability, and enhancing the operation effectiveness. From the perspective of resilience, this paper studies the importance of the SoS, and a resilience-based importance measure analysis is conducted to provide suggestions in the design and optimization of the structure of the SoS. In this paper, the components of the SoS are simplified as four kinds of network nodes: sensor, decision point, influencer, and target. In this networked SoS, the number of operation loops is used as the performance indicator, and an approximate algorithm, which is based on eigenvalue of the adjacency matrix, is proposed to calculate the number of operation loops. In order to understand the performance change of the SoS during the attack and defense process in the operations, an integral resilience model is proposed to depict the resilience of the SoS. From different perspectives of enhancing the resilience, different measures, parameters and the corresponding algorithms for the resilience importance of components are proposed. Finally, a case study on an SoS is conducted to verify the validity of the network modelling and the resiliencebased importance analysis method.

GIS-Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents.

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk-informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard-independent social vulnerability index for the local population; (2) developing a location-specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS-based socio-technical risk map by combining the social vulnerability index and the location-specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio-technical risk. The methodology is applied using results from the 2012 Surry Power Station state-of-the-art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location-specific SVI themes based on their influence on risk, providing input for EPPR.