Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review

Song, Chenxiao; Kawai, Reiichiro

doi:10.1016/j.probengmech.2023.103479

Cited by 45 publications

(7 citation statements)

References 401 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When dealing with larger failure probabilities, it is recommended to use simulation methods. Monte Carlo simulation (MCS) techniques are a useful tool for estimating failure probabilities [49][50][51][52]. One advantage of these methods is their simplicity, as they are easy to understand and execute.…”

Section: Reliability Methodsmentioning

confidence: 99%

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Chen,

Liu,

et al. 2024

Energies

View full text Add to dashboard Cite

This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation.

show abstract

Section: Reliability Methodsmentioning

confidence: 99%

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Chen,

Liu,

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…The Monte Carlo (MC) method is an important tool to generate random numbers for solving analytical problems. It also provides the convergent result for an unknown exact value [ 24 , 25 ]. In this paper, a beam with multiple damages is simulated in MATLAB R2021a software using the MC concept.…”

Section: Methodsmentioning

confidence: 99%

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages

Saha,

Yang

2023

Sensors

View full text Add to dashboard Cite

Natural frequency is an important parameter in the structural health monitoring (SHM) system. Any changes in this parameter indicate structural alteration due to damage. This study provides a neural network (NN) solution as an alternative to the finite element (FE) method to measure the natural frequencies of a cantilever beam with random multiple damage. It is based on a statistical dataset of a free vibration test obtained from the APDL (Ansys parametric design language) simulation using a MATLAB (matrix laboratory) script. The script can generate an unlimited number of possible damage combinations for any given parameters with the help of the Monte Carlo (MC) technique. MC helps to generate a random number of damages in random locations at each simulation. Damage conditions are controlled by three parameters including damage severity and damage size (in terms of the mean and standard deviation of damage). Moreover, the method proposes a curve-fitting equation to validate the predicted natural frequency for the first three modes obtained from the neural network model. Both methods are in good agreement with each other, having minimal errors in the range of 0.2–3% for each mode. The frequency result shows that the beam frequency is 8.6486 Hz if the area reduction is 10%, whereas it comes down to 7.2338 Hz if there is a 30% area reduction. A two-level factorial test shows that damage severity is the most impactful factor compared to the damage sizes on the frequency shift event. This indicates that damage alters the composition of the beam and has an impact on its frequency change with the assumed damage parameters. Therefore, the proposed NN model can estimate the frequency shift for various damage scenarios. It can be utilized in the vibration-based damage identification process to predict the frequency changes of the damaged beam without any computational burden.

show abstract

“…The failure probabilities of the main power and the backup power within a time interval ∆t are denoted as P m and P b , respectively. The number of system states and the numbering of each system state were calculated using Equations (10) and (11). The state transition diagram of the channel power subsystem is shown in Figure 5.…”

Section: Reliability Modelingmentioning

confidence: 99%

“…The disadvantages of these methods are that the corresponding inference algorithm is required, and that the models are difficult to solve [9]. MCS is noted for their powerful ability to express dynamic characteristics, although it is computationally intensive and present significant difficulties in model validation [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes

Pan,

Chen,

Shen

et al. 2024

Sensors

View full text Add to dashboard Cite

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the “curse of dimensionality”, which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2×2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system’s non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method.

show abstract

Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review

Cited by 45 publications

References 401 publications

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages

A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes

Contact Info

Product

Resources

About