Engineering Reliability Analysis Research Articles

Reliability engineering principles and maintenance scheduling for plant recovery

Plant operation and management require data from which information can be extracted to determine the state of the plant, and predict its health and anticipated failure interval in the future. The data is lacking in most companies in Nigeria and sometimes when available are not organized in the proper form to extract useful information. This research described the use of reliability engineering principles to perform maintenance practice in a brewery industry. The operational data were obtained from the log book of the plant and subjected to reliability engineering analysis. The average reliabilities of the plant was: 0.431, 0.376, 0.434, 0.603 and 0.722 with average availability of 0.985, 0.986, 0.987, 0.988 and 0.996 respectively. The results show that reliability engineering principles can be used to determine the health and performance of the plant is highly suggested for adoption and implementation.

Improving Spacecraft Reliability Through Digital Transformation

This paper demonstrates the reliability engineering benefit achievable by spacecraft digital transformation. The space industry has evolved in the past decade to increasingly incorporate new computing practices into the sector’s ground service and backoffice operations, hailed as a “digital transformation” of their information technology (IT) infrastructure. Parallel sectors that heavily engage control systems, such as utilities and manufacturing, have not only embraced an IT digital transformation but also incorporated new computing architectures, chipsets, and data processing techniques into their operational technology (OT) systems. The space sector has lagged to adopt such digital technologies for their OT, such as spacecraft, and has not realized many of the associated benefits. Through a reliability engineering analysis of small satellites and a comparative case study of a digitally transformed solar sail, we demonstrate the reliability impacts of digital transformation for spacecraft.

A probabilistic approach for assessing the mechanical performance of intertrochanteric fracture stabilized with proximal femoral nail antirotation.

Maintaining post-operative mechanical stability is crucial for successfully healing intertrochanteric fractures treated with the Proximal Femoral Nail Antirotation (PFNA) system. This stability is primarily dependent on the bone mineral density (BMD) and strain on the fracture. Current PFNA failure analyses often overlook the uncertainties related to BMD and body weight (BW). Therefore, this study aimed to develop a probabilistic model using finite element modeling and engineering reliability analysis to assess the post-operative performance of PFNA under various physiological loading conditions. The model predictions were validated through a series of experimental test. The results revealed a negative nonlinear relationship between the BMD and compressive strain. Conversely, the BW was positively and linearly correlated with the compressive strain. Importantly, the compressive strain was more sensitive to BW than to BMD when the BMD exceeded 0.6 g/cm3. Potential trabecular bone compression failure is also indicated if BMD is equal to or below 0.15 g/cm3 and BW increases to approximately 2.5 times the normal or higher. This study emphasizes that variations in the BMD significantly affect the probability of failure of a PFNA system. Thus, careful planning of post-operative physical therapy is essential. For patients aged > 50 years restrictions on high-intensity activities are advised, while limiting strenuous movements is recommended for those aged > 65 years.

Floor plan reconstruction from indoor 3D point clouds using iterative RANSAC line segmentation

Indoor Floor Plans (IFPs) are crucial in many fields, such as architectural design, BIM generation, indoor navigation, as well as reliability analysis and safety assessment in civil engineering. The traditional manual creation of IFPs is labor-intensive and time-consuming, hence the growing interest in their automatic generation. To automate IFP creation and ensure their consistency with original indoor layouts, a method for generating IFPs from indoor 3D point clouds is proposed. This method uses a complete point cloud from an indoor scene as the sole input and produces a corresponding vectorized IFP. It first identifies wall points from the 3D point cloud, and projects them onto a 2D plane. An iterative RANSAC-based clustering algorithm is then used to detect and segment line segments from the 2D point cloud. Finally, we restore the topological structure of the discrete line segment set and outputs a vectorized IFP composed of continuous line segments. When tested on two different scale indoor point cloud datasets, this new method achieved over 90 % of accuracy in line segment reconstruction and 97 % of IoU in IFPs reconstruction, proving its effectiveness in non-Manhattan indoor scenes. Compared with other representative methods, Floor-sp and ASIP, this new method offers more detail and higher accuracy.

Reliability and Risk Analysis in Geotechnical and Geoenvironmental Engineering

Reliability and Risk Analysis in Geotechnical and Geoenvironmental Engineering

Statistical inference of an $ \mathit{\alpha } $-quantile past lifetime function with applications

<abstract> <p>In reliability engineering and survival analysis, quantile functions are fundamental and often the most natural way to represent probability distributions and data samples. In this paper, the $ \alpha $-quantile function of past lifetime was estimated for right-censored data by applying the Kaplan-Meier survival estimator. The weak convergence of the proposed estimator to a Gaussian process was investigated. A confidence interval for the $ \alpha $-quantile of the past life function that does not depend on the density function was proposed. The strong convergence of the estimator to a Gaussian process was also discussed. The properties of the estimator and the confidence interval were investigated in a simulation study. Finally, two real datasets were analyzed.</p> </abstract>

Bayes analysis of one-shot device testing data with correlated failure modes using copula models

Copula models are capable of modeling the dependence structure among the random variables, a phenomenon that is often required in the statistical analysis. Such models are the flexible substitutes of multivariate distributions because they model both the marginal distributions and the joint dependence structure distinctly. Because of such important features, the models are recognized as popular tools in a variety of situations including reliability engineering and survival analysis. The present paper studies a Bayesian approach using three Archimedean copulas, namely, the Gumbel Hougaard copula, the Frank copula and the Joe copula for analyzing one-shot device testing data with two correlated failure modes collected from a constant stress accelerated life test. One-shot devices are units that can be used only once and destroyed immediately after the use. Obviously, one obtains either left or right censored data on the failure times instead of actual failure times of the devices. Finally, all the considered copula models are compared using the Bayesian model selection tools. A real dataset is analyzed as an illustration of the proposed Bayesian methodology.

Engineering Reliability-Based Condition Assessment for Stay Cables Using Non-Destructive Interferometric Radar

Structural health monitoring (SHM) of cable-stayed bridges requires periodic assessment for the deteriorating stay cables to ensure a long-term service life of the bridge. However, conducting the non-destructive SHM for operating cable-stayed bridges and analyzing the safety statuses of all the working cables are still challenging, due to the lack of in situ cable data for previously constructed bridges. This study developed an innovative framework of health condition assessment for stay cables based on cable vibration frequencies from an interferometric radar (IBIS-FS) using engineering reliability analysis (ERA). Taking a cable-stayed bridge in Victoria, Australia as a target structure for the case study, it shows that the presented framework can remotely monitor the accurate real-time load bearing conditions of stay cables by calculating tension forces, and effectively assess their health conditions. The results show that the natural frequency (up to the fifth mode) of a healthy cable remains constant under different external loadings but varies for damaged cables. The measured reliability index of all the stay cables is higher than the safety threshold factor at ultimate limit states, while one carries tension force higher than the maximum design load (lower than the minimum breaking load) and other three cables need to be monitored regularly due to their low reliability indices. This is attributed to an integrated effect of applied tension force, cable diameters, and minimum breaking loads.

The Gull Alpha Power Lomax distributions: Properties, simulation, and applications to modeling COVID-19 mortality rates.

The Gull Alpha Power Lomax distribution is a new extension of the Lomax distribution that we developed in this paper (GAPL). The proposed distribution's appropriateness stems from its usefulness to model both monotonic and non-monotonic hazard rate functions, which are widely used in reliability engineering and survival analysis. In addition to their special cases, many statistical features were determined. The maximum likelihood method is used to estimate the model's unknown parameters. Furthermore, the proposed distribution's usefulness is demonstrated using two medical data sets dealing with COVID-19 patients' mortality rates, as well as extensive simulated data applied to assess the performance of the estimators of the proposed distribution.

Modified Generalized Linear Exponential Distribution: Properties and applications

In this paper, we propose a new four-parameter lifetime distribution called modified generalized linear exponential distribution. The proposed distribution is a modification of the generalized linear exponential distribution. Several important lifetime distributions in reliability engineering and survival analysis are considered as special sub-models including modified Weibull, Weibull, linear exponential and generalized linear exponential distributions, among others. We study the mathematical and statistical properties of the proposed distribution including moments, moment generating function, modes, and quantile. We then examine hazard rate, mean residual life, and variance residual life functions of the distribution. A significant property of the new distribution is that it can have a bathtub-shaped, which is very flexible for modeling reliability data.The four unknown parameters of the proposed model are estimated by the maximum likelihood. Finally, two practical real data sets are applied to show that the proposed distribution provides a superior fit than the other sub-models and some well-known distributions.

Assessment of multi-dimensional joint probability distribution for uncertain mechanical strength parameters under small sample test data

The multi-dimensional joint probability distribution of uncertain mechanical strength parameters is crucial for reliability analysis in geotechnical engineering. However, due to the limited number of test data samples, the analysis faces significant challenges. This paper presents a modeling method for joint probability distribution based on multidimensional Gaussian copula, which is suitable for small sample conditions. Firstly, the copula theory is introduced, and a construction method for multidimensional Gaussian copula is proposed. Secondly, measurement data for 192 sets of coal seam roof strength parameters were collected from 24 coal mines in China. For the four-dimensional Gaussian copula, analyses of goodness of fit and simulation error were performed using the Pearson method, Kendall method, and Spearman method, respectively. Finally, the impact of different copula functions on the fitting capability of the original data is discussed. The results indicate that the established multi-dimensional Gaussian copula joint distribution model possesses an advantage in characterizing the uncertainty of relevant parameters. This model also offers an effective approach for studying the uncertainty of coal mine strength parameters. Different construction methods for multidimensional Gaussian copulas yield varying simulation outcomes. The Pearson method exhibits the most favorable fitting effect for constructing correlation parameters. The two rank correlation methods have the advantages of broad applicability, excellent fitting effect, and minimal variation in simulation error. For the bivariate copula fitting ability of uncertain parameters, the Gaussian Copula exhibits the best fitting ability under both positive and negative correlation scenarios. When the correlation is weak, Plackett copula and Frank copula can also serve as alternative candidate copula functions. These findings offer a significant theoretical foundation for conducting reliability analysis of a coal seam roof under limited test data conditions.

Progress and Opportunities in Computational Modeling of Localized Corrosion

Corrosion can lead to mechanical damage near the material surface and reduce the material’s strength. It is essential to understand and simulate corrosion damage evolution for predicting the residual service life of engineering structure, reliability analysis, and corrosion-resistant design of materials. Several major novel corrosion simulation methods in the past 10 y are mainly introduced: cellular automata method, finite element method, phase field model, and peridynamics model. The computational modeling of localized corrosion is discussed and the advantages and disadvantages are compared. Finally, some difficulties in practical engineering applications such as dynamic interface tracking, multiscale and multiphysical field corrosion simulation, and standardization of corrosion simulation are proposed, and the future investigation direction is explored. With the rapid development of software science and computer technology, the operation speed and accuracy of numerical simulation will be greatly improved. The application advantages of numerical simulation in the field of corrosion will be more prominent.

Neutrosophic Log-Logistic Distribution Model in Complex Alloy Metal Melting Point Applications

The log-logistic distribution is more comprehensively applied in the area of survival and reliability engineering analysis for modeling the lifetime data practices of both human and electronic designs. The goal of this paper is to develop a generalization of the classical pattern log-logistic distribution, known as the neutrosophic log-logistic distribution (NLLD), to model various survival and reliability engineering data with indeterminacies. The developed distribution is especially useful for modeling indeterminate data that is roughly positively skewed. This paper discusses the developed NLLD’s main statistical properties such as neutrosophic survival function, neutrosophic hazard rate, neutrosophic moments, and neutrosophic mean time failure. Furthermore, the neutrosophic parameters are estimated using the well-known maximum likelihood (ML) estimation method in a neutrosophic environment. A simulation study is carried out to establish the achievement of the estimated neutrosophic parameters. As a final point, the proposed NLLD applications in the real world have been discussed with the help of real data. The real data illustrated that the efficiency of the proposed model as compared with the existing models.

Probabilistic Analysis on Aerostatic Displacement-dependent Wind Loads on a Stream-lined Box Girder

Due to the limitations of the bridge deck modeling and wind tunnel installations, the parametric uncertainties of aerostatic displacement-dependent wind loads have become an urgent problem for reliability analysis in bridge engineering. Computational fluid dynamic (CFD) simulations were conducted to investigate the distribution model of aerostatic coefficients. The Monte Carlo simulation (MCS) was used to generate random samples of stream-lined box cross-sections. Among these samples, wind attack angles of −8°–8° were considered, and the sample size of each angle group was set as 30. Then, the statistical properties for the variation and distribution of aerostatic coefficients were discussed. In addition, sub-samples with sizes of 5–20 were randomly sampled from the initial sample group to evaluate the influence of the sample scale. The coefficient of variation (COV) of aerostatic coefficients with respect to geometric uncertainties increased up to 0.217, which occurred at −7° wind attack angle. Lognormal distribution models obtained by hypothesis tests maintained a low deviation for estimating the distribution of aerostatic coefficients. The sample size had a significant effect on quantifying the error between empirical and theoretical distribution models. Simulation method proposed in this paper provides a systematic approach for probabilistic problems involving fluid-structure interaction and parametric uncertainties.

The utility of probability plotting in palaeobiology

AbstractWe introduce probability plotting for general use in the analysis of biological and palaeontological morphometric data. When applied to morphometric data, this analytical tool can yield palaeoecological, ontogenetic and demographic information. We have adapted and expanded a statistical tool used in reliability engineering, mechanical or electrical lifetime analysis, geochemistry and ore mineral analysis to analyse fossil populations. The technique is explained stepwise in a ‘user's manual’ format. Its power is to characterize statistical distribution types, to determine whether these distributions are skewed or truncated, and to resolve and quantify the components of mixed distributions, which in palaeontological data potentially represent morphological groupings such as cohorts or sexual dimorphs. The ease of application and simplicity are surprising. A probability plot of growth series data (e.g. skull lengths, femoral lengths, snout‐to‐vent lengths or other morphometrics) typically shows complex non‐normal distribution of the data because of multimodal composition. We show that these component modes, which are generally, but not always normal (i.e. Gaussian), can be resolved, and that they correspond to yearly age groups (cohorts), sexual dimorphs, pre‐ and post‐metamorphic forms, other ontogenetic stages or possibly mixed taxa. When these component size distributions are associated with an age group or sex, several important palaeoecological calculations become possible. These include growth curves, age and sex distributions, sexual dimorphism indices, etc. We demonstrate the biological and palaeontological utility of probability plots with several examples including a Permian wingless insect, an extant unionid bivalve, a Triassic metoposaurid amphibian, an extant lizard, and a Triassic theropod dinosaur.

Optimal maximum entropy quantile function for fractional probability weighted moments and its applications in reliability analysis

The maximum entropy principle (MEP) under the constraint of fractional probability weighted moments (FPWM) is a well-developed method for a direct estimation of the quantile function of a random variable. The accuracy, unbiasedness, and efficiency of maximum entropy quantile functions (MEQFs) have been demonstrated in the literature. However, the issue of how to select an optimal order of FPWMs given a sample of data is not fully solved, and the application of this approach to reliability analysis in civil engineering has not been fully investigated. This paper presents a FPWM-based MEP with a new nondimensional analysis and proposes the Akaike information criterion to determine the optimal order of FPWM in MEP analysis. To illustrate this approach, two examples of the estimation of optimal quantile functions for soil undrained shear strength and annual maximum daily discharge are presented. The paper presents a novel approach to use the quantile function for the First Order Reliability Method, a widely used method in civil engineering. As a case study of this approach, the reliability analysis of a rock slope is included. The FPWM-based MEQFs are compared with common empirical distributions and MEQFs based on integral probability weighted moments to demonstrate the advantages and disadvantages of the developed method.

Reliability analysis of boom of new lifting equipment based on improved wolf swarm algorithm

Aiming at the low accuracy or inability to solve the problem when the traditional reliability optimization method deals with the practical engineering reliability analysis problem, this paper proposes a structural reliability analysis method using the improved wolf swarm algorithm (IWSA). Firstly, the reliability optimization mathematical model is established with the minimum reliability index as the objective function and the limit state equation as the equality constraint. Then the augmented multiplier method is used to transform the constrained optimization mathematical model into an unconstrained optimization mathematical model. Finally, IWSA is used to solve the problem. The proposed method is applied to a numerical example and an engineering example. The computation results of the proposed method in solving the numerical example are compared with the Monte Carlo simulation (MCS), which verifies that the proposed method in this paper has good calculation accuracy. The maximum deformation under the working condition is used as the target to establish a limit state function, and the reliability index is obtained by the proposed method combining with the finite element analysis software, which provides a certain theoretical guiding significance for the reliability analysis of engineering structures.

Construction of adaptive Kriging metamodel for failure probability estimation considering the uncertainties of distribution parameters

A critical problem in engineering reliability analysis is obtaining an accurate failure probability with a high computational efficiency. This study aims to present failure probability estimation under the conditions of uncertain input variables and their uncertain distribution parameters. An adaptive Kriging model of failure probability with respect to distribution parameters (FP-DP model) is developed, which avoids coupling modeling among the distribution parameters, input variables, and failure probability. An improved U-learning function that simultaneously considers the statistical information of uncertain distribution parameters and failure probability is proposed to select new sampling points for the FP-DP model. The stopping criteria based on sample distances and relative errors of the predicted failure probability are constructed to improve the convergence performance around the limit state function. Three numerical and four engineering examples with different complexities are considered to verify the effectiveness of the proposed adaptive FP-DP Kriging metamodel. The results show that the proposed method can obtain an accurate failure probability with fewer sampling points of uncertain distribution parameters than some existing methods, indicating that the proposed method can be efficiently integrated into reliability-based design optimization problems considering both the uncertainties of input variables and their distribution parameters.

Reliability analysis of small failure probability based on subset simulation method

In the engineering, to ensure the quality and safety, it is necessary to carry out reliability analysis on it. When conducting reliability analysis in engineering, a large number of small failure probability problems will be encountered. For such problems, the traditional Monte Carlo method needs a lot of samples, and the calculation efficiency is extremely low, while the subset simulation method can efficiently estimate the reliability index of the small failure probability problem with little samples. Therefore, this paper takes the application of the subset simulation method in the reliability analysis of the small failure probability structure as the object, constructs the reliability analysis method of the single failure mode of the system, and applies the method to a mathematical example and a single-story gate. Through the rigid frame example, it can be seen that this method is beneficial to improve the calculation efficiency and accuracy.

Contributions Towards New Families of Distributions: An Investigation, Further Developments, Characterizations and Comparative Study

In the past couple of years, statistical models have been extensively used in applied areas for analyzing real data sets. However, in numerous situations, the traditional distributions are not flexible enough to cater to different aspects of the real phenomena. For example, (i) in the practice of reliability engineering and biomedical analysis, some distributions provide the best fit to the data having monotonic failure rate function, but fails to provide the best fit to the data having non-monotonic failure rate function, (ii) some statistical distributions provide the best fit for small insurance losses, but fails to provide an adequate fit to large claim size data, and (iii) some distributions do not have closed forms causing difficulties in the estimation process. To address the above issues, therefore, several methods have been suggested to improve the flexibility of the classical distributions. In this article, we investigate some of the former methods of generalizing the existing distributions. Further, we propose nineteen new methods of extending the classical distributions to obtain flexible models suitable for modeling data in applied fields. We also provide certain characterizations of the newly proposed families. Finally, we provide a comparative study of the newly proposed and some other existing well-known models via analyzing three real data sets from three different disciplines such as reliability engineering, medical, and financial sciences.