Approach For Reliability Analysis Research Articles

A Multi-Performance Reliability Evaluation Approach Based on the Surrogate Model with Cluster Mixing Weight

Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical assets with multiple failure modes. Consequently, this paper devises a multi-performance reliability analysis approach within the surrogate model framework, particularly innovative in its use of cluster mixing weight. Specifically, high-value test points are selected to fit the surrogate model after sorting the samples referring to the corresponding values; then, a cluster-based active learning strategy is employed to accomplish rapid convergence, and the particle swarm algorithm is utilized to optimize relevant parameters. Afterwards, the mixing weight for every performance referring to the contributions to the final reliability is determined, and the failure probability is subsequently predicted. Furthermore, the superiority of the proposed approach with the clustering surrogate model and mixing weight, compared with traditional sampling as well as other surrogate models, has been verified via case studies, contributing to overcoming the multi-performance reliability analysis oriented to complicated mechanical assets.

Adaptive Importance Sampling approach for structural time-variant reliability analysis

A novel sampling approach, called Adaptive Importance Sampling is proposed to efficiently perform time-variant reliability analysis. In practice, structures are generally subject to time-variant deterioration processes and external loads, and the Time-variant Failure Probability Function (TFPF), which is the failure probability as a function of time, is a critical quantity of interest in engineering applications. The proposed approach leverages an adaptive strategy and an optimal combination algorithm to further improve the accuracy and efficiency of TFPF estimation using the Importance Sampling approach. The adaptive strategy is to seek for the best setting of Importance Sampling components to iteratively obtain estimator components of the TFPF. The optimal combination algorithm is to collect all these adaptive estimator components to form an overall estimator by its coefficient of variation (C.o.V.). The proposed approach outperforms traditional Importance Sampling methods in the sense that it ensures the convergence with minimal computational cost, specifically the C.o.V. of the TFPF estimator is below a predetermined threshold over the entire time domain. Therefore, the proposed approach offers an extension to traditional Importance Sampling methods for time-variant reliability assessment. Numerical examples are provided to demonstrate the effectiveness of the proposed approach in accurately estimating the TFPF of structures subjected to time-variant loads and deterioration processes.

Experimental Approach for Reliability Analysis of Medium-Power Zener Diodes under DC Switching Surge Degradation

This study investigated the reliability of Zener diodes subjected to a gradually increasing DC switching surge amplitude with delay internals between surges to avoid thermal degradation from different manufacturers with similar specifications. The analysis involved applying occasional 3 ms direct current (DC) switching surges with a gradual increasing surge voltage, followed by a constant current test to verify device functionality for three different selected manufacturer 5.1 V Zener diodes. This experimental approach was used to identify the maximum surge current that each Zener diode could handle before failing to clamp the surge voltage at the specified Zener reference voltage. Statistical analysis revealed significant differences in the maximum average surge current between different manufacturers. The maximum average surge current findings just before failure were 1.98 A, 3.18 A, and 3.33 A, respectively, and associated 95% confidence interval ranges can be used as a reliable metric to compare Zener diode population reliability against occasional DC switching surges. The findings revealed variations in the DC switching surge current handling capabilities between Zener diodes from different manufacturers with similar electrical specifications. The statistically measured maximum average surge current just before device failure can be considered an effective metric to compare the reliability of Zener diodes against DC switching surge degradation.

Reliability analysis of steel truss bars with imprecise statistical data

The article describes the probabilistic approach of reliability analysis for steel truss bars with imprecise statistical data. An approach to recovery the probability density of a random variable by the kernel density estimation method is illustrated for a small number of tests/measurements cases. The estimation of the distribution density parameters is presented in an interval form, which makes it possible to form a p-box for the random variable. Evaluation of the probability of non-failure allow to get a quantitative assessment for the safety of steel trusses, to identify the most unreliable trusses for monitoring their technical condition or to calculate the effect of strengthening the truss according to the reliability criterion. The probability of non-failure is estimated in the interval form. If the lower limit of the interval does not allow to get a conclusion about the truss safety level, then it can be increased by obtaining additional statistical data or by strengthening the truss or reducing the permissible load.

Reliability analysis method of energy storage stations considering the correlation of battery clusters

In the context of dual carbon, in order to address the volatility of new energy output, energy storage power stations (ESPSs) are thriving. However, it is challenging to assess the dependability of ESPS operation because of the steady functioning of individual battery clusters (BCs), which will have an impact on how safely and steadily the power system is evaluated. Therefore, a reliability model for energy storage BC units based on the structural analysis of battery structure modules is constructed in this manuscript. Then, a method for constructing a correlation model of energy storage units based on the Copula function was proposed. Finally, a simulation analysis based on the ESPSs of pertinent demonstrative application projects in Fujian Province is used as an example to confirm the viability and efficacy of the proposed reliability analysis approach.

Evaluating the lifetime distribution parameters and reliability of products using successive approximation method

AbstractThe Weibull distribution is an extensively used statistical model for analyzing the reliability of mechanical and electrical components. Due to the complexity of the nonlinear equations and the scarcity of failure data, the common method may not provide satisfactory results of the reliability. In this case, a new approach for Weibull parameter estimation and reliability analysis, based on successive approximation schemes, is presented. The shape and scale parameters are estimated by maximizing the likelihood functions, and the location parameter is obtained by constructing an approximate correction model between it and the failure data. In order to show the performance of the proposed method, an extensive Monte‐Carlo simulation study is conducted. Simulation results show that the proposed method provides better estimates and efficient confidence intervals for Weibull parameters. In addition, the proposed method works well in presence of small sample sizes. Finally, two real examples are analyzed to illustrate the application of the proposed method.

A hybrid human reliability analysis approach for a remotely-controlled maritime autonomous surface ship (MASS- degree 3) operation

Maritime autonomous surface ships (MASS) are one of the hot topics in maritime transportation even though they bring many challenges in terms of safety, security, and environment. This paper tackles the safety-related challenges of remotely controlled ships without seafarers on board but controlled at the shore. In this context, the reliability of the operator is of paramount importance for safe and efficient MASS operation. This paper performs systematic human reliability analysis for the operator of MASS (for degree 3) under the Bayesian belief network (BBN) and evidential reasoning (ER)- cognitive reliability and error analysis method (CREAM) approach. In the model, BBN is capable of determining the probability distribution of Contextual Control Modes in CREAM, while ER tackles the uncertainty and subjectivity of expert judgments. The outcome of the paper shows that the human reliability for remote control MASS operation is 8.88E-01. Besides its robust theoretical background, the paper will provide the utmost contributions to operators, managers, safety inspectors, and ship owners of MASS for safer and reliable operations.

Bayesian network approach for reliability analysis of mining trucks

Having a safe and efficient system for mineral transportation is a top priority for all mining operations. Trucks are the most widely used material transportation systems that are applied in both surface and underground mines. Any truck failure disrupts the mineral transportation process and consequently decreases the overall output. Therefore, the reliability analysis of such equipment plays a critical role in increasing the efficiency and productivity of a mining operation. This paper proposes a novel method for analyzing the reliability of a fleet of mining trucks based on the Bayesian Network modeling. Considering the reliability block diagram, the fault tree of trucks was developed according to the logical relationship between the units. Then, a dynamic Bayesian network was constructed according to the conditional probability analysis. Moreover, the relative contributions of each truck’s component to the occurrence of the fleet failure were studied by using critical analysis. The results of this paper show that the successful operation of the fleet of trucks is most sensitive to truck no. 5, which has the highest reliability level in all time intervals. The reliability of the fleet of trucks reaches 0.881 at 20 h, and the fuel injection system of the truck’s engine is the main leading cause of the trucks failure. A proper preventive maintenance strategy should be paid more attention to improve the reliability and availability of the engine system.

A modified interval perturbation statistical energy analysis method for structural reliability analysis of vibro-acoustic systems with interval uncertain parameters

Structural reliability analysis has become a critical aspect of engineering design for vibro-acoustic systems. With the increasing complexity and uncertainty of design parameters, it is essential to develop methods and tools that can accurately account for all the sources of uncertainty. This paper presents a novel approach for structural reliability analysis of vibro-acoustic systems with interval parameters within the framework of Statistical Energy Analysis (SEA). By introducing the Sherman-Morrison-Woodbury formula and the sub-interval technique, a modified interval perturbation statistical energy analysis is proposed to predict the interval bounds of energy and stress response. Then, the non-probabilistic structural reliability is assessed by using the volume ratio theory. Case studies of a plate-cavity system and a launch fairing model, in which the interval uncertainties in external loads and material properties are considered, are presented to demonstrate the effectiveness of the proposed approach. The results show that the proposed method can effectively handle uncertainties and provide accurate estimates of the probability of failure.

Development and Validity Test of Fuel Station Consumer Safety Behavior Questionnaire

Fuel Stations are facilities for selling fuel to the public which has a potential hazard and a high risk of accidents. During 2017-2018 there were 120 gas station accidents in Indonesia. Additionally, 29 fuel accidents occurred in 2022, most of them involving consumer vehicles. Previous studies have explored fuel station safety by examining its safety climate, health risks to workers, risk and fire assessments. Study on consumer behavior in fuel stations has not yet been discovered. Therefore, this study attempts to fill the gap by developing a Fuel Station Consumer Safety Behavior (FSCSB) Questionnaire. The aim of this study is to introduce a novel instrument to assess fuel station consumer safety behavior and assess its validity and reliability. The first phase of this study uses a qualitative approach on developing the questionnaire through literature review, content validity with field experts and face validity with actual fuel station consumers. In the second phase, a quantitative approach for validity and reliability analysis was carried out by calculating Pearson's Product Moment and Cronbach's Alpha on each variable. All questionnaire items passed the Pearson's validity test, but 3 items did not meet minimum Cronbach's Alpha criterion and have been removed. The final result is a novel FSCSB Questionnaire with 21 items. The questionnaire can be used as a reference for future research with a larger number of respondents.

A new active learning method for reliability analysis based on local optimization and adaptive parallelization strategy

In recent years, an active learning method combining Kriging and Monte Carlo Simulation (AK-MCS), has been developed for calculating the failure probability. However, the original AK-MCS only uses serial computing, which limits its ability to take advantage of distributed computing. Thus, this work introduces a novel adaptive learning approach for reliability analysis by combining local optimization and a parallelization strategy. The new approach identifies the points of greatest uncertainty through local optimization and adds them into the design of experiments. An inner learning loop is implemented, searching for additional best points with a pseudo Kriging model so that the performance function can be evaluated in parallel. Furthermore, an adaptive strategy is proposed to determine the amount of additional points during iteration based on the minimum value of the learning function. We conducted a comparison between the proposed method and the original AK-MCS as well as a few additional methods in order to assess its efficacy and precision. Five examples were considered to assess performance.

A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.

Case study of Block 1, Highland Towers Condominium collapsed in Taman Hillview, Ulu Klang, Selangor, Malaysia on 11th December 1993

Landslide has become the primary focus in slope engineering as the case increases. Most landslides are resulted from an unknown confluence, and most occur on slopes created by humanity. Highland Towers were built between 1976 and 1979, and its occupants were primarily first and middle-class people. There is a steep hill behind the three blocks. The main draws here are the natural scenery and panoramic vista of Kuala Lumpur. This case study investigates what factors led to Block 1, Highland Towers condominium’s collapse on 11th December 1993 at 1.35 p.m. using reliability analysis approaches and human fault impact factors. The Block 1 collapse is caused by an unstable pile foundation. The engineers incorrectly estimated the horizontal load design, causing surcharge loads to be created by the downhill forward movement when the rotating retrogressive slide happens. However, several precautions may be taken to protect the building structures from landslide. It is common knowledge that structural reliability analysis yields failure probabilities that are not considered for human faults. Inadequate drainage, collapse of retaining wall, and rail piling foundation are all plausible explanations for this landslide initiation. Thus, combining structural and human reliability analysis are recommended to mitigate landslide, including slope hazards.

A comparative study of various metamodeling approaches in tunnel reliability analysis

Various metamodeling approaches are applied in conjunction with Monte Carlo simulation and or the second moment-based method for reliability analyses of underground tunnels. However, there is no study regarding the suitability of such metamodels for reliability analyses of tunnels. An attempt is made here to make a comparative assessment of different metamodeling approaches for tunnel reliability analysis to comprehend the performances of various metamodels from the subset of machine learning methods. In doing so, the least square method based polynomial response surface method (RSM), mostly used in tunnel reliability analyses, and its improved version i.e., moving least square method-based RSM, are taken up for comparison. Further, the most successful empirical risk minimization-based Kriging model and the structural risk minimization principle-based support vector regression model are considered for comparison. Also, the sparse Bayesian regression found to be useful in solving various structural reliability analysis problems, is taken up for the present comparative study. Two numerical examples demonstrate the effectiveness of the selected metamodels in tunnel reliability analysis. It has been generally noted that the Kriging and SVR-based metamodels outperform in reliability estimates of underground tunnels.

A FENN-TL Approach for Reliability Analysis of a Primary Ice Detection System

A FENN-TL Approach for Reliability Analysis of a Primary Ice Detection System

Structural reliability analysis based on improved wolf pack algorithm AK-SS

To improve the accuracy and efficiency of solving structural reliability problems with highly nonlinear black box functions, this paper proposes an active learning Kriging approach for structural reliability analysis based on an improved learning strategy of the Improved Wolf Pack Algorithm (IWPA).Firstly, we optimize the kriging surrogate model by IWPA. Secondly, a parallel plus point strategy named IWPA-NLF which is based on IWPA and a new learning function (NLF) is used to update the kriging surrogate model. Finally, the subset simulation (SS) method is used to calculate the reliability. A numerical example and three engineering examples are used to verify the proposed method in this paper. The results show that the proposed method in this paper has a strong function fitting ability and can calculate the accurate failure probability with fewer performance function calls.

A Novel Multi-state Bogie System Reliability Evaluation Approach by Extended d-MC Model for High Speed Train

Abstract Bogie is a pivotal system and plays a critical part in the safety and reliability management of high speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to a low efficiency. In order to overcome the mentioned drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d-MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d-MC model is established to remove unnecessary d-MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and the examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to testify the practicability of the method projected in this article, and is is found that the proposed method outperforms a newly developed method in solving the multi-state reliability problems.

Probabilistic machine learning approach for structural reliability analysis

In recent years, the surrogate modeling technique has been increasingly employed for the failure probability estimation of civil structures. The surrogate modeling technique acts as a black box that predicts the performance of expensive-to-evaluate problems. However, the uncertainty of failure probability estimation due to the discrepancy between the surrogate model and physical model has not been well studied. This study introduces a new surrogate modeling technique for failure probability estimation of civil structures that utilizes a Bayesian neural network. Specifically, a fully connected neural network is constructed to approximate the limit state function. The hyperparameters in the neural network are obtained within the Bayesian paradigm, and the probability distributions are estimated through Laplace approximation. When the Bayesian neural network method is used for reliability analysis, the failure curve and the corresponding uncertainty can be estimated. Numerical studies on 2d truss, a simple support beam under moving load, and dynamic analysis of subway station considering the soil–structure interaction are conducted to validate the efficiency of the approach. Results with Monte Carlo simulation and subset simulation are also presented. The results demonstrate the proposed method’s potential for improving prediction accuracy by factoring in the uncertainty embedded in the surrogate model. Additionally, it exhibits higher levels of efficiency than traditional methods.

Reliability analysis of flexible pavements based on the quantile-value method

ABSTRACT This paper presents a simple yet efficient approach for the reliability-based design of flexible pavements, based on the concept of quantile-values. The reliability-based approaches currently adopted in the pavement design guides are simplified techniques, based on an overall reliability factor. On the other hand, simulation-based probabilistic techniques, widely discussed in literature, can be computationally expensive and tedious to implement. The ‘Simplified Effective Random Dimension Quantile Value Method’ (Simplified ERD-QVM), adopted in this study, presents an elegant technique to bridge the gap between the precision of simulation-based probabilistic techniques and the efficiency of design guide approaches. The accuracy of this technique is verified through Monte Carlo Simulations for different pavement design scenarios. The main advantages of the method are (i) the significant savings in computational times for reliability-based design of flexible pavements without the use of surrogate models (ii) the use of simple techniques already familiar to practitioners who adopt the Mechanistic-Empirical Pavement Design Guide and (iii) it is a comprehensive reliability analysis approach that addresses the drawbacks of the currently used approaches, while capturing the effect of both design parameter and model uncertainty. The methodology for integrating the Simplified ERD-QVM with the existing flexible pavement design codes is also presented.

Reliability Study of an Intelligent Profiling Progressive Automatic Glue Cutter Based on the Improved FMECA Method

This study introduces the fuzzy theory approach as an enhancement to the traditional failure mode, effect, and criticality analysis (FMECA) method in order to address its limitations, which primarily stem from subjectivity and a lack of quantitative analysis. The proposed method, referred to as FMECA improvement based on fuzzy comprehensive evaluation, aims to quantify the qualitative aspect of the analysis and provides a detailed outline of the analysis procedure. By applying the enhanced FMECA method to assess the reliability of an intelligent profiling progressive automatic rubber cutter, the hazard ranking for each failure mode of the cutter can be determined, thereby identifying areas that require reliability improvement. The analysis outcomes demonstrate that this method establishes a theoretical foundation for subsequent cutter improvement designs, enables early identification of potential failures, and consequently leads to a reduced failure rate and an enhanced reliability level for the intelligent profiling progressive automatic cutter. Furthermore, this innovative agricultural equipment reliability analysis and testing approach holds significant value in elevating the reliability standards of agricultural equipment as a whole and can be explored and implemented in other agricultural machinery contexts.