Reliability Analysis Of Systems Research Articles

A bi-objective model for reliability analysis of a multi-state system with performance sharing mechanism

A bi-objective model for reliability analysis of a multi-state system with performance sharing mechanism

Reliability analysis of anti-UAV systems: an analytical approach

Reliability analysis of anti-UAV systems: an analytical approach

An efficient surrogate model for system reliability with multiple failure modes

AbstractThe failure modes of complex systems are often highly dependent. Aiming at the reliability analysis of engineering systems with multiple failure modes, an efficient surrogate model for system reliability with multiple failure modes is proposed, namely, the Copula Adaptive Kriging surrogate model (CAKSM). First, the initial Kriging model is established based on the initial samples. In order to select more effective added samples to update the Kriging model, a new learning function, namely, Copula Unites with Expected Improvement Function (CUEIF), is established. The function can make a reasonable sample selection by considering the failure correlation. Then, based on the optimal Copula function, the reliability data of each failure mode are coupled and analyzed. In order to adapt to various computing scales, regularization is introduced to improve the prediction speed of the surrogate model, and the probability density function of each performance function is obtained by introducing nonparametric kernel density estimation. Finally, the effectiveness of the CAKSM method and the learning function CUEIF is verified through four examples.

Evaluating human error probability in maintenance task: An integrated system dynamics and machine learning approach

AbstractHuman error is often implicated in industrial accidents and is frequently found to be a symptom of broader issues within the sociotechnical system. Therefore, research exploring human error during maintenance activities is important. This article aims to assess the probability of human error in maintenance tasks at a cement factory using the Cognitive Reliability and Error Analysis Method and System Dynamics modeling. Given that human error probability (HEP) is influenced by various common performance conditions (CPCs) and their sub‐factors, and changes dynamically in response to other variables, the SD method offers a practical approach for estimating and predicting human error behavior over time. This study identifies and quantifies the variables affecting HEP, explores their interactions and feedback in maintenance tasks, and assesses the associated costs. The machine learning technique is then used to estimate the relationship between HEP and these costs. The optimal value of the HEP function, 0.000772, is determined by identifying the minimum point of a cubic function, thereby minimizing associated costs and occupational accidents. Determining the optimal HEP is crucial for minimizing excessive costs and investing in improved ergonomics and CPCs for better performance. This addresses a significant gap in existing research where the impact of human error on maintenance tasks has not been estimated as a function. Furthermore, three scenarios are presented to help managers allocate the organization's budget more effectively.

Reliability modeling of multi-state phased mission systems with random phase durations and dynamic combined phases

Random phase durations and dynamic combined phases challenge the application of existing reliability models in the reliability analysis of multistate-phased mission systems (MS-PMSs). To this end, this paper presents a new reliability modeling method for multi-state phased mission systems with random phase durations and dynamic combined phases. Initially, a multi-state multi-valued decision diagram-based (MMDD-based) reliability modeling method is created to efficiently map random phase durations and the dynamic combined phase nature of MS-PMSs into the reliability model. To solve the MMDD-based reliability model, a path probability evaluation method is subsequently constructed with the assistance of the Markov regenerative function. The effectiveness and the superior performance of the proposed MMDD-based reliability model and its solving algorithm are validated by the application to the reliability modeling and analysis of an attitude and orbit control system with multiple modes. Overall, this paper provides the reliability sector with a new reliability model and its solving algorithm to enhance the reliability and safety of multi-state phased mission systems.

PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region

In practical engineering, system reliability analysis is highly concerned since many structures or products have multiple failure modes. Accordingly, this paper develops an innovative method for system reliability analysis by parallel learning of influential component limit-state functions with filtered sample region (PLIC-FSR-SYS) based on Kriging modeling. Different from the traditional adaptive learning methods that train only one component in each iteration when constructing the surrogate of the composite limit-state function, a new strategy is explored to adaptively identify several important components in one iteration so as to train them simultaneously. In the meanwhile, a filtering formula is explored to determine the fatal region so that the unimportant samples can be removed to further accelerate the training process. Based on the join forces of parallel learning of influential components and avoiding the training at unimportant samples, PLIC-FSR-SYS can achieve a fairly efficient system reliability analysis with multiple failure modes. Finally, four different case studies, including an engineering application to the ultra-voltage on-load tap-changer, are conducted to prove the effectiveness of the proposed method. The results indicate that compared to traditional adaptive learning methods, the proposed method makes a significant efficiency improvement for system reliability analysis with multiple failure modes.

Fatigue strength reliability assessment of turbofan blades subjected to intake disturbances based on the improved kriging model

This paper aims to develop an efficient and precise reliability analysis method to enhance the numerical prediction accuracy for complex structures. Kriging, an implicit surrogate model, used to address highly nonlinear and complex problems. In this study, genetic algorithms (GA) are utilized to optimize the parameters of the Kriging model, which is then integrated with a distributed collaborative strategy to introduce the Genetic Algorithm Optimized Distributed Collaborative Kriging Model (DCGAK). Using the CFM56-fan blade as a case study, the impact of intake disturbances at the engine inlet is evaluated to assess the fatigue strength reliability of the blade. Comparison with different mathematical models demonstrates that the prediction accuracy of DCGAK closely aligns with the Monte Carlo sampling results, suggesting promising prospects for its application in numerical prediction and reliability analysis. This approach enriches the current methods for structural reliability analysis of complex mechanical systems.

A dynamic system reliability analysis model on safety instrumented systems

This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.

A new active learning surrogate model for time- and space-dependent system reliability analysis

This study introduces a novel method for time- and space-dependent system reliability analysis, integrating an active learning surrogate model with an innovative parallel updating strategy. A global Kriging model is developed to represent the signs of random samples using efficient global optimization. From a Bayesian perspective, the prediction probabilities of random sample signs within the time-space domain are calculated, and the sample with the lowest prediction probability is chosen to update the global Kriging model. The system extremum for each sample in the time-space domain is determined, and the corresponding random variables, time-space coordinates, and failure modes are selected. To further decrease iteration times, a parallel updating strategy that considers both the predicted probability and the correlation among candidate samples is proposed. Additionally, a new stopping criterion is introduced to balance accuracy and efficiency, terminating the updating process appropriately. The method's accuracy and efficiency are validated through three examples.

System Reliability Analysis of Concrete Arch Dams Considering Foundation Rock Wedges Movement: A Discussion on the Limit Equilibrium Method

In this paper, a discussion on the applicability and limitations of the limit equilibrium method is presented. In this manner, the reliability of a concrete arch dam-foundation system under static loading is evaluated by considering a set of potentially moveable rock wedges in the foundation. The safety of the system is assessed utilizing a quasi-analytical method, which employs the limit equilibrium method and numerical analysis to calculate the sliding safety factors and the dam trust forces, respectively. The reliability is evaluated using the Latin Hypercube Sampling method. Random variables in the system are the friction angle, cohesion, and the Grout Curtain Efficiency Coefficient. In the end, the influence of two parametric variables of discontinuities, elastic slip and rock mass deformability modulus, on the rock wedges’ sliding safety factor is evaluated by comparing the results of the quasi-analytical method with the purely numerical method. The results show that in the case of complicated geotechnical conditions, the limit equilibrium method may not reflect real-world failure scenarios.

Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions

Long-span cable-stayed bridges are complex systems with numerous components, and prone to failures of multiple components under near-fault ground motions with long-period velocity pulses. The evaluation of dynamic system reliability of cable-stayed bridges is an important challenging issue. In this work, a unified framework for simultaneously determining both the stochastic dynamic responses and system reliabilities of long-span cable-stayed bridges under near-fault stochastic ground motions is proposed based on direct probability integral method (DPIM) with adaptive strategy. Firstly, the 3-dimensional finite element model of typical cable-stayed bridge and the stochastic synthesis model of near-fault ground motions are established. Based on probability density integral equation, new formulas in input probability space of time-variant moments of response are derived. Then, DPIM with adaptive strategy is suggested to calculate the mean value and standard deviation of response via new formulas. Dynamic system reliability of cable-stayed bridge under near-fault stochastic ground motions is evaluated by establishing failure criteria of extreme value mappings of multiple performance functions. Finally, numerical results indicate that the long-period velocity pulse of near-fault motions imposes an important effect, resulting in large mean responses and failure of cables. Failure probability of cables is significantly increased with the occurrence of velocity pulses, which causes the decrease of system reliability of cable-stayed bridge. Thus, the randomness and velocity pulse effect of near-fault ground motions should be seriously considered for reliability-based design and safety assessment of cable-stayed bridges.

Reliability analysis of subsea manifold system using FMECA and FFTA

Subsea manifold system is a complex system that occupies a pivotal role in contemporary ocean engineering and has a significant impact on the safety of marine resource exploitation. Reliability technology plays a significant role in ensuring the safe operation of the subsea manifold system. To perform a comprehensive analysis of the reliability of complex systems, a combination method of FMECA-FFTA (Failure Modes, Effects and Criticality Analysis - Fuzzy Fault Tree Analysis) is introduced in this study. Firstly, FMECA is used to accomplish a qualitative analysis of system reliability considering multifactorial failure modes, which included analyzing potential failure modes, causes of system failure, and evaluating the degree of hazard to the system through a risk matrix diagram. Then, FFTA is applied to build a fault tree model to divide the system into “system-subsystem-component” and determine the minimal cut sets for quantitative analysis of system reliability. In addition, fuzzy set theory is incorporated to improve the accuracy of handling uncertainty in quantitative reliability analysis. Finally, a qualitative and quantitative reliability analysis is conducted by using FMECA-FFTA method for subsea manifold system. The failure modes of the subsea manifold system are clearly identified, including high-risk modes such as external leakage, medium-high-risk modes such as fail to close/lock on demand, and medium-risk modes such as leakage of critical location, plugged, and effective measures should be taken to focus on preventive protection and regular testing for the high risk, medium-high risk and medium risk modes.

Reliability analysis of load-sharing system with the common-cause failure based on GO-FLOW method

The load-sharing system (LSS) with the common-cause failure (CCF) is widely used in industrial engineering applications. If a component in this system fails, the total load is shared by the other components, leading to an increased failure rate of the surviving components. The traditional GO-FLOW method is difficult to calculate the reliability of this system accurately. To address this issue, a new reliability analysis approach is proposed in this paper. In this approach, a new GO-FLOW operator is established to simulate the LSS with CCF. Firstly, the state transfer relationship between components in the LSS is identified. Secondly, the α-factor is used to establish the relationship between the independent failure rate λI and the CCF rate λC. Finally, the Markov method is employed to calculate the transient-state and steady-state reliability of the system, and the calculation process for the parallel system and k-out-of-n(F) system are given, respectively. The feasibility of the proposed method is illustrated through a numerical example of a distributed electric propulsion system. This approach extends the applicability of the GO-FLOW method.

System Reliability Analysis of Reservoir Landslides: Insights from Long-Term Reservoir Operation

System Reliability Analysis of Reservoir Landslides: Insights from Long-Term Reservoir Operation

Physics-informed machine learning for system reliability analysis and design with partially observed information

Physics-informed machine learning for system reliability analysis and design with partially observed information

Reliability analysis of an automotive car braking system using logic diagram technique

Reliability analysis of an automotive car braking system using logic diagram technique

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models.

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Improved Bayesian model updating of geomaterial parameters for slope reliability assessment considering spatial variability

In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the “curse of dimensionality”. Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment.

An improved sequential importance sampling method for structural reliability analysis of high dimensional problems

Reliability analysis for rare events has proven to be a significant challenge, particularly in non-linear and complex scenarios. Despite the effectiveness of Sequential Importance Sampling (SIS) and Subset Simulation (SUS) in addressing high-dimensional small failure probability problems, the computational burden associated with mechanical simulations for complex structures remains substantial. To address this issue, this paper introduces a novel approach referred to as SIS and Kriging Metamodel Integration (SISAK), designed for computing small failure probabilities in high dimensions. This method involves generating a small number of samples from the original distribution to construct an adaptive metamodel, which is then iteratively optimized using a series of intermediate distributions introduced by SIS. By substituting the function for intermediate distributions with a continuously optimized metamodel, the enhanced approach significantly reduces the computational load associated with mechanical model evaluations compared to the direct use of SIS. This approach does not depend on determining the most probable failure point and does not require consideration of the failure domain shape. Additionally, it inherits the advantages of SIS in addressing high-dimensional small failure probabilities, making it well-suited for structural reliability analysis of nonlinear systems, discontinuous failure domains, cascading functions, and high-dimensional problems.

Stochastic aspects of reversed aging intensity function of random quantiles

This paper studies some stochastic properties of random quantiles according to the newly defined reliability measure called reversed aging intensity function. Preservation property of reversed aging intensity order under random quantile is obtained and using it, a lower bound and an upper bound for the reversed aging intensity function of a random quantile are derived. Preservation of two related monotonic reliability classes under random quantiles is also studied. We finally apply our results for reliability analysis of series systems with heterogeneous component lifetimes. Examples are included to examine and analyze the obtained results.