System Failure Probability Research Articles

A novel reliability analysis methodology based on IPSO-MCopula model for gears with multiple failure modes

In order to accurately and quickly predict the failure probability of gears with multiple failure modes, a novel reliability analysis methodology based on the mixed Copula (MCopula) function model is proposed to deal with the complex correlation among different failure functions. Firstly, we construct a novel MCopula model based on three famous Copula functions: Gumbel Copula, Clayton Copula, and Frank Copula functions. Secondly, we use and improve the particle swarm optimization (PSO) method to optimally calculate the weight coefficients in the proposed MCopula model. Thirdly, the maximum likelihood estimation (MLE) method is adopted to estimate related parameters in the proposed MCopula model. Finally, we verify the proposed reliability analysis methodology with a standard life-prediction case of a strut system and a practical life-problem case of a gear pair system. Comparison results of both cases show that, by using the proposed methodology, the failure probability of a gear pair system with multiple failure correlations can be quickly calculated through a small number of samples and can be estimated as accurately as that by the Monte Carlo scheme. Consequently, our proposed novel methodology successfully analyzes the reliability problems for a gear pair system with multiple failure modes. The proposed methodology can be further applied to solve the reliability problem for other machine parts.

A novel single-loop simulation algorithm combined with adaptive Kriging model for estimating the system failure probability function with multi-dimensional distribution parameter

A novel single-loop simulation algorithm combined with adaptive Kriging model for estimating the system failure probability function with multi-dimensional distribution parameter

Calculation of the System Unavailability Measures of Component Importance Using the D2T2 Methodology of Fault Tree Analysis

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum’s measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system.

Energy harvester reliability study by Gaidai reliability method

AbstractThis study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.

Application of RSM optimization model for slope stability analysis in geotechnical engineering

Abstract Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.

Improved stochastic subset optimization method for structural design optimization

The Stochastic Subset Optimization (SSO) algorithm was proposed for optimal reliability problems that minimizes the probability of system failure over the admissible space for the design parameters. It is based on the simulation of samples of the design parameters from an auxiliary Probability Density Function (PDF) and exploiting the information contained in these samples to identify subregions for the optimal design parameters within the original design space. This paper presents an improved version of SSO, named iSSO to overcome the shortcomings in the SSO. In the improved version, the Voronoi tessellation is implemented to partition the design space into non-overlapping subregions using the pool of samples distributed according to the auxiliary PDF. A double-sort approach is then used to identify the subregions for the optimal design. The iSSO is presented as a generalized design optimization approach primarily tailored for the stochastic structural systems but also adaptable to deterministic systems. Several optimization problems are considered to illustrate the effectiveness and efficiency of the proposed iSSO.

Numerical investigation on design of soil nailed slopes by limit equilibrium method

ABSTRACT This paper introduces a numerical investigation into the stability analysis of soil nailed slopes. It establishes a simplified framework within the limit equilibrium method, focusing on the physical distribution of tension force along nails. MATLAB® was adapted for the Analysis of Soil Nailed Walls (ASNW) based on Bishop’s method, with major command loops optimized through ‘array operations’. Employing K-fold cross-validation, five regression models predicted the maximum tensile force (Tmax), revealing Gaussian process regression (GPR) as the best model based on root mean square error and r-squared metrics. To capture the amount of uncertainty in the nail load model, we have used a clustering method for estimating the measured Tmax. Self Organizing Map (SOM) was employed for clustering the nail load dataset. We randomly generated Tmax between the measured Tmax obtained by SOM and the predicted Tmax gained by the GPR model for each soil nail. We apply these loads in ASNW program to compute the Factor of Safety, and finally estimate system failure probability for 35 different nail layouts. A parametric study with a uniform nail layout explores the impact of nail length on system failure probability. Results show diminishing significance of nail length on failure probability beyond a nominal length.

Computationally efficient approach for risk-informed decision making

Probabilistic risk assessment (PRA) is used as an essential tool for risk-informed decision-making in the nuclear industry. The fault and event trees play a crucial role in PRA to estimate the probability of system failure based on the failure probabilities of components. The fault trees or event trees for an actual power plant unit can be fairly large in size with several different types of logic gates, interconnected events, dependent events, etc. A large fault tree can include hundreds of gates, basic events (BEs), multiple occurring events (MOEs), and dependent events. Complex connectivities can give rise to excessive computational demand and storage requirements for the analysis. Fault and event trees can be solved using the minimal cut-set approaches, or advanced quantification techniques such as Binary decision diagrams or Bayesian networks. However, these techniques can be computationally inefficient for larger fault trees and can run out of memory/storage space. This study focuses on developing and proposing a new approach for accurate estimation of the system-level risk while improving the computational efficiency significantly. More specifically, an attempt is made to reduce the complexity of the analysis of MOEs and dependent events in fault trees. The proposed algorithms in this study present a significant improvement over traditional approaches which makes it highly promising for additional development. The computational efficiency of the proposed approach over the traditional approach is illustrated for fault trees with a varying number of events and different types of logic gate connections.

Reliability modeling for balanced systems considering mission abort policies

This paper studies a mission abort policy to avoid the high probability of system failure for a balanced system performing a specific mission continuously over a period of time. The system comprises two subsystems with multiple states, and a balance criterion proposed in this paper is based on the state distance between the two subsystems. A mission abort policy is implemented when the system enters into unbalanced subsets reaching a limited number of times. The system is considered to have failed if one of the subsystems enters into an absorbing state. To assess the system's performance, two probability indices, namely mission success probability and system survivability, are derived by using the theory of aggregated stochastic processes. To validate the findings presented in this paper, a case study involving an unmanned aerial vehicle performing a mission is conducted. The results of this case study effectively demonstrate the efficacy of the proposed mission abort policy and the derived probability indices.

GemV-tool: A Comprehensive Soft Error Reliability Estimation Tool for Design Space Exploration

With aggressive technology scaling, soft errors have become a major threat in modern computing systems. Several techniques have been proposed in the literature and implemented in actual devices as countermeasures to this problem. However, their effectiveness in ensuring error-free computing cannot be ascertained without an accurate reliability estimation methodology. This can be achieved by using the vulnerability metric: the probability of system failure as a function of the time the program data are exposed to transient faults. In this work, we present a gemV-tool, a comprehensive toolset for estimating system vulnerability, based on the cycle-accurate gem5 simulator. The three main characteristics of the gemV-tool are: (i) fine-grained modeling: vulnerability modeling at a fine-grained granularity through the use of RTL abstraction; (ii) accurate modeling: accurate vulnerability calculation of speculatively executed instructions; and (iii) comprehensive modeling: vulnerability estimation of all the sequential elements in the out-of-order processor core. We validated our vulnerability models through extensive fault injection campaigns with <3% correlation error and 90% statistical confidence. Using the gemV-tool, we made the following observations: (i) the vulnerability of two microarchitectural configurations with similar performance can differ by 82%; (ii) the vulnerability of a processor can vary by more than 10×, depending on the implemented algorithm; and (iii) the vulnerability of each component in the processor varies significantly, depending on the ISA of the processor.

SS-MASVM: An advanced technique for assessing failure probability of high-dimensional complex systems using the multi-class adaptive support vector machine

The integrity of structures in contemporary engineering practices is heavily influenced by inherent uncertainties, which necessitates the development of reliable structural reliability analysis methods for uncertainty quantification and probabilistic analysis. This paper investigates the integration of surrogate models and subset simulation (SS) to accurately compute the failure probabilities of high-dimensional complex systems. Focus is placed on enhancing the computational efficiency and accuracy through using the multi-class adaptive support vector machine (MASVM). The proposed SS-MASVM method incorporates more efficient learning functions, integrates K-means clustering, and employs SS technology for precise estimation of failure probabilities of high-dimensional complex systems. Numerical and engineering structural examples validate the effectiveness of the proposed method and highlight its potential in practical reliability analysis, and the results disclose that the SS-MASVM is efficient and accurate for assessing the complex system reliability.

Global sensitivity analysis and optimization of a multistage thermoelectric generator based on failure probability

Thermoelectric generator (TEG) is a thermoelectric conversion device that mainly utilizes the Seebeck effect, and the electrical energy generated is usually directly converted from thermal energy. However, research on the global sensitivity analysis and optimization of three-stage TEG systems is currently lacking. In this study, a multistage semiconductor TEG system model based on functional failure probability and a global sensitivity analysis model were established. These models are first used to compare the performance of TEG configurations with different stages. Then, based on the double random uncertainties of design variables, a series of studies were conducted. The results showed that the performance parameters of the all TEG configurations exhibited similar performance variation trends. An first-best working current was identified to obtain the first-best output power and conversion efficiency. Next, as the sample size used to determine the variable uncertainty for a three-stage TEG increased, it increased to 4 × 106, the sensitivity indicators of the output response (P and η) tended to constant values of 0.02322 and 0.02039. Among the 11 considered design parameters, the uncertainties of the heat and cold source temperatures had the most significant influence on the functional failure probability of the three-stage TEG system, followed by the working current, Zeebek coefficient, number of temperature difference components in each stage, and heat transfer coefficient of the cold source. The remaining variables had negligible influence. Finally, small changes in the mean heat and cold source temperatures changed the sensitivity of the output response of the three-stage TEG to each critical design variable, but did not affect the relative ranking of the sensitivity indicators corresponding to each. The results of this study can provide some basis for the further development of TEG systems.

Leakage failure probability assessment of submarine pipelines using a novel pythagorean fuzzy bayesian network methodology

Obtaining precise and adequate failure data can be challenging in the probabilistic risk assessment of process industries, like submarine pipelines. This study proposes a novel approach, the Pythagorean fuzzy Bayesian network methodology, to tackle the challenge of obtaining prior data in the assessment of leakage risk in submarine pipelines. First, the qualitative evaluation of experts is converted by Pythagorean fuzzy sets. Next, the enhanced Pythagorean trapezoidal fuzzy Einstein hybrid geometric operator is integrated with the subject-objective weighting approach to consolidate the expert opinions in order to obtain prior probabilities. Following this, the leaky Noisy-OR gate in the Bayesian network is utilized to access the conditional probabilities, which depict the uncertain logical connection of events. Ultimately, the Bayesian network is utilized for inference and analysis to anticipate the probability of system failure and detect any vulnerabilities. Furthermore, a case study is performed to demonstrate the practicality of the approach. The reliability of the methodology is verified by results comparison and analysis. The suggested approach and evaluation findings can offer valuable guidance for the safety supervision of the submarine pipeline network.

Importance sampling-based probabilistic performance modeling of low-, mid- and high-strength pipelines under coupling effect of hydrogen-induced damage and corrosion

In-service long-distance pipelines are a practical approach to implementing large-scale hydrogen transportation under the context of energy transition. However, the hydrogen-induced damage (HID) and corrosion would deteriorate the material properties and load-bearing capacity of the pipelines. In the present study, the coupling effect of HID and corrosion are probabilistically incorporated into the mechanical characterization models for low-, mid- and high-strength pipelines. Spatiotemporal stochastic growth is modeled using the stochastic gamma processes with significantly affected defect geometric parameters to facilitate the uncertain performance status of pipelines. All unknown parameters of the proposed method can be inferred by inducing in-line inspection (ILI) data. The computational framework for importance sampling (IS) has been developed to estimate the system failure probability of corroded natural gas pipelines considering HID. Apply several programs to conduct probability analysis to demonstrate the applicability and effectiveness of the proposed method. The results demonstrate that HID affects the failure behavior and safety probability state of pipelines containing corrosion defects. The performances of various strength pipelines have significant differences in deterioration paths due to HID, causing additional maintenance or potentially disastrous consequences. The proposed method has been proven to capture the potential uncertainties of HID and corrosion on different-strength steel pipelines, which is beneficial for the lifecycle management of blended hydrogen natural gas pipelines.

Quantum Learning on Structured Code With Computing Traps for Secure URLLC in Industrial IoT Scenarios

Resilient and secure ultra-reliable low-latency communications (URLLC) over radio interface is expected to play a crucial role in next generation Industrial IoT scenarios. However, attacking wireless pilot signals has been a potential easy way to interrupt URLLC services. In this work, we propose a random structured code to encode and decode pilot signals on multidimensional physical resources, and also design a quantum learning framework to make this code secure and reliable. Specifically, the code suggests using random encoding with little structures to disperse the effect of attacks. We find that the decoding process can be modeled as a computing trap if the group spatial channel features are employed. The security problem is therefore transformed as a random computing with redundancy. We employ a quantum algorithm to learn the computing trap model such that the computing redundancy can be removed quickly while the dispersed attack can be eliminated. In this respect, we can prove the existence of the quantum black-box model corresponding to the computing trap, and derive a precise expression of computing performance. Based on the result, we can formulate novel analytical closed-form expressions of system failure probability to characterize the reliability of the URLLC. Numerical results show that the proposed system can maintain ultra-high reliability and low latency against attacks on wireless pilots.

A novel second‐order mixture importance sampling strategy for system reliability analysis

AbstractReliability analysis and evaluation of systems with multiple failure modes is extremely challenging. In this paper, a novel, efficient, and accurate system reliability method is proposed based on the second‐order approximation and the mixture important sampling (mixture‐IS) method. The proposed method does not require the construction of surrogate models and integrates the advantages of second‐order approximation and mixture‐IS strategies, which can greatly reduce the computational effort of estimating the system failure probability as well as ensuring the accuracy. The accuracy and efficiency of the proposed method are verified by several numerical and engineering problems, and comparisons are made against other existing reliability methods. The results indicate that the method can effectively reduce the number of limit state function calls and achieve high computational accuracy.

Unavailability Assessment Based on Improved-Dependent Uncertain Ordered Weighted Averaging Operator and Fault Tree Analysis

The fire-fighting system is one of the proactive technical barriers related to liquefied petroleum gas storage tank safety. This paper presents an integrated approach that uses fuzzy set theory, an improved-dependent uncertain ordered weighted averaging operator and fault tree analysis to handle uncertainty in the unavailability assessment of fire-fighting systems. In this study, the center of area is used to defuzzify triangular fuzzy numbers. Furthermore, for the fire-fighting system fault tree, importance analysis, including Fussell–Vesely importance measure and risk reduction worth of basic events, are performed to identify the weak links of the fire-fighting system. In addition, a real case study on a fire-fighting system for a liquefied petroleum gas storage system in an LPG unit in Algeria is provided to illustrate the effectiveness of the proposed methodology. The research results show that the proposed methodology makes it possible to assess the unavailability of the entire system by analyzing weak links. Consequently, some suggestions are given to take preventive–corrective actions in advance, in order to reduce the failure probability of fire-fighting system and assist the practitioners in setting priorities for improving safety procedures in liquefied petroleum gas storage tanks. The study provides references for analyzing safety barriers in a complex system.

Multi-hazard assessment of transmission line systems subjected to independent non-concurrent turbulent winds and ground accelerations

The design of transmission lines is based on the Load and Resistance Factor Design approach, considering that their collapse limit state is governed by climatic actions (wind and ice). Even so, accident reports indicate the regular observation of moderate to severe damage of towers after strong ground motions, affecting the structure’s capacity to withstand future load events. Therefore, integrating the concepts of Performance-Based and Multi-Hazard Design, successfully applied to buildings and still embryonic to transmission lines, seems to be promising. With such performance-based multi-hazard approaches, intermediate performances can be evaluated even when they are controlled by a non-dominant hazard. Hence, the present paper aims at performing an original multi-hazard assessment of a Chilean 230kV transmission line segment. For this purpose, direct geometric and material nonlinear time history analysis, using a finite element suitable for eccentrically loaded angle profiles (L-shaped cross-sections), is employed to construct the fragility curves. In addition, uncertainties in loads and material properties are accounted for to address the transmission line system failure probability. In the existing literature, fragility analysis has been performed through pushover approaches, whereas reliability has been assessed for a single structure rather than for the transmission line system, as recommended by line codes. Contrary to design practice, the results show that seismic loads control all limit states in TLs located from central to northern Chile. Numerical results presented in this paper are based on 5000 h of total computation time.

Nested Hash Access With Post Quantum Encryption for Mission-Critical IoT Communications

Secure ultra-reliable low-latency communication (URLLC) has become a crucial requirement of mission-critical Internet of Things (IoT) applications and use cases including automotive driving, remote surgery, and many others. However, it is still challenging to protect initial access of massive IoT devices over wireless channels, especially when malicious quantum adversaries paralyse the initial access by overhearing and tampering critical wireless messages, i.e., preambles. We propose a nested hash access system with post-quantum encryption to solve this issue. The system performs random repetition coding and nested hash coding on multi-domain physical-layer resources to encode and decode preambles precisely and resiliently. Particularly, a subtle compression and encryption mechanism based on quasi-cyclic (QC)-moderate-density parity-check (MDPC) code is proposed between repetition and hashing operations to avoid passive eavesdropping during preamble encoding process. We show that the code information can be maintained at 128-bit or higher privacy level, depending on the length of repetition code. Besides, the preamble decoding process can be proved secure agaisnt active attacks with a tolerable loss of decoding errors. Then we formulate two non-convex integer programming problems, each problem corresponding to the minimization of upper bound of preamble decoding error in an example application scenario. Finally, we can derive the expressions of system failure probability to evaluate the reliability of URLLC system under mission-critical IoT scenarios. Simulation results show the effectiveness of our proposed scheme despite attack.

Introducing a new system performance function to formulate reliability analysis problems of wind turbines

Consideration of safety is one of the current requirements to design a new system that is often implemented by defining the system failure probability. Renewable energy systems (RESs) do have the same requirements when it comes to safety and reliability. When designing a wind turbine, as a RES, its reliability is of the highest importance. So, efficient reliability models are required to ensure the turbine is working safely to generate electricity. A new model is introduced in this paper by taking into account the wind speed and the wind angle as two effective factors. These two random variables are reported to follow the Gaussian and Weibull probability distributions, and so are employed to define a limit-state function for the turbines. This limit-state function, which is also called system performance function, will then be used to find out the system failure probability.