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One of the tasks required of most statistics researchers and academic faculty is to publish their innovative ideas in the peer‐reviewed literature. In this paper, we provide guidance about the different stages of the process as experienced authors and offer advice from those who hold the decision about the success or failure of these papers, namely the editors of applied statistics journals. The paper is organized into four sections focusing on the different stages of publishing: (1) Planning what to write about, where to submit and how to organize the paper; (2) The process of writing the paper; (3) Interpreting and responding to peer‐reviews from the journal editors and referees to prepare for resubmission; and (4) General comments about the publication process, including collaboration and mentoring. Each section starts with fundamentals provided by the moderators (C.A.C. and L.L.) on key aspects to consider on each topic and then is followed with discussion from some current and past editors of impactful journals in the field of applied statistics. Our hope is that this collection of insights may help accelerate learning about the process for young researchers and help all researchers to understand some of the important often‐unspoken aspects of the process. [ABSTRACT FROM AUTHOR]

This is the author's second bibliography on process capability indices (PCIs) and contains approximately 1080 journal papers and books for the period 2010–2021. The related literature is classified into six major categories, namely, books, review/overview papers, theory‐ and method‐related papers, special applications, software packages, and papers omitted in the author's previous bibliography. Theory‐ and method‐related papers are further classified into univariate, multivariate, and functional PCI‐related papers. Special applications include acceptance sampling, control charts, supplier selection, and tolerance design and other optimizations. The present bibliography consists of two parts. Part I contains books, review/overview papers, and univariate PCI‐related papers, while Part II includes multivariate and functional PCI‐related papers, special applications, software packages, and papers omitted in the author's previous bibliography. [ABSTRACT FROM AUTHOR]

This is the second part of the bibliography on process capability indices (PCIs) for the period 2010–2021, and includes multivariate and functional PCI‐related papers, special applications, software packages, and papers omitted in the author's previous bibliography. [ABSTRACT FROM AUTHOR]

A novel maintenance policy for a two‐component warm standby system with multiple standby states is presented in this paper. Two standby states, that is, cold and warm standby, for components in the system are considered. Components can realize the transition from the cold standby to the warm standby state by periodic switching, intended to shorten recovery time and save system operating costs. Preventive maintenance (PM) and preventive switching (PS) of components are considered. In the PS strategy, the standby component can switch before the operating component fails. In addition, additional standby failure modes based on idle time are studied. Derive the long‐term average cost of the system through the semi‐regenerative process. A numerical example eventually verifies the feasibility of this paper's proposed maintenance and switching strategy. [ABSTRACT FROM AUTHOR]

In this paper, we have considered the classical and Bayesian inference of the unknown parameters of the lifetime distribution based on the observations obtained from a simple step‐stress life‐testing (SSLT) experiment and when more than one cause of failures are observed. We have used the Tampered Random Variable (TRV) approach. The main advantage of the TRV approach is that it can be easily extended to a multiple step‐stress model as well as for different lifetime distributions. In this paper, it is assumed that the lifetime of the experimental units at each stress level follows Weibull distribution with the same shape parameter and different scale parameters. Further, we have introduced different tempering co‐efficient for different causes of failures. The maximum likelihood estimators and the associated asymptotic confidence intervals are obtained based on Type‐II censored observations. Further, we have considered the Bayesian inference of the unknown model parameters based on a fairly general class prior distributions. An extensive simulation study is performed to examine the performances of the proposed method, and the analysis of a real data set has been provided to show how the method can be used in practice. We have compared the TRV model with some of the other existing models, and the TRV model provides a better fit in terms of information theoretic criteria. We have also provided some optimality criteria, to determine the optimal stress change time and some sensitivity analyses have been performed. Most of the methods can be extended for other lifetime distributions also. [ABSTRACT FROM AUTHOR]

Degradation modeling serves as a valuable tool for assessing the lifetime information of highly reliable products. One frequently employed approach for describing the degradation phenomenon involves the use of a degradation model that relies on stochastic processes. In a stochastic‐process‐based degradation model, it is assumed that the increments follow a distribution with the additivity property. This property makes the further inferences mathematically and statistically tractable. However, it limits the choices of the distributions. This paper aims to use those distributions without the additivity property to model the increments and explores distributions from the log‐location‐scale family. Under the frame of Bayesian analysis, Markov Chain Monte Carlo algorithms are developed for executing the necessary computations. Given that the proposed degradation models do not adhere to the additivity property, this paper tackles the challenges involved in predicting the lifetime of both on‐line and off‐line products. Two illustrative examples are subsequently analyzed to demonstrate the procedural steps outlined. The suitability of the proposed model is finally validated through a simulation study. [ABSTRACT FROM AUTHOR]

Highly reliable mechanical systems can lead to significant losses in the event of failure, and the lack of comprehensive failure data presents challenges for developing techniques such as critical part identification and failure prediction. In light of this, this paper proposes a meta‐action‐based fault prediction method that effectively addresses the issue of limited fault data. Initially, the mechanical system is decomposed utilizing the “Function‐Motion‐Action” (FMA) methodology to derive individual meta‐action units (MAUs). Subsequently, the limited sample of fault data from the mechanical system is combined with processed expert knowledge to construct the corresponding fault propagation‐directed graph. Furthermore, the key MAUs are determined by applying the Decision–Making Trial and Evaluation Laboratory (DEMATEL) method. Last, the degradation data of the key MAUs is acquired by monitoring them, and a non‐homogeneous discrete grey model (DNGM) integrated with an improved BP neural network is proposed to facilitate the fault prediction of MAUs. Using an industrial robot as a case study, the prediction results demonstrate the superiority of the method proposed in this paper over a single gray model and neural network, thereby providing a reliable prediction approach for anticipating the future trends of data‐deficient mechanical systems. [ABSTRACT FROM AUTHOR]

Fault diagnosis is a novel technology crucial for monitoring the proper functioning and ensuring the stability of mechanical devices and components. Nevertheless, most existing data‐driven methods for rolling bearing fault diagnosis exhibit limited diagnostic capabilities in scenarios characterized by noise interference and inadequate training data. To address this issue, this paper proposes a novel intelligent fault diagnosis method for rolling bearings based on Capsule Network with Fast Routing algorithm (FCN). Firstly, the vibration signal is transformed into a time‐frequency map through continuous wavelet transform (CWT), and the transformed time‐frequency map is input into the network model to enable the network to learn features more fully. Subsequently, this paper introduces FCNs into capsule networks, effectively mitigating the extended training times typically associated with capsule networks and reducing the demands on training equipment. Extensive experiments are conducted utilizing two distinct bearing datasets to assess the method's stability and generalization. The results of these experiments demonstrate the proposed approach's ability to maintain robust fault diagnosis capabilities, even in the presence of noise interference and limited training data. This innovative method lays the foundation for intelligent rolling bearing diagnosis and is readily adaptable to other rotating components. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to a Bayesian accelerated life testing model is presented. The Weibull distribution is used as the life distribution and the generalized Eyring model as the time transformation function. This is a model that allows for the use of more than one stressor, whereas other commonly used acceleration models, such as the Arrhenius and power law models, incorporate one stressor. The use of the generalized Eyring‐Weibull model developed in this paper is demonstrated in a case study, where Markov chain Monte Carlo methods are utilized to generate samples for posterior inference. [ABSTRACT FROM AUTHOR]

The mathematician and bio‐scientist Sam Karlin is quoted stating that "The purpose of models is not to fit the data but to sharpen the question". In this paper, we describe a journey between questions, models and data analysis to reach specific goals. This journey is typical in industrial, engineering, biology and social science applications. It contrasts regulated clinical research where a statistical analysis plan is declared before data collection. We consider random forests, ridge regression, lasso and elastic nets. To make our point, we use a case study of 63 sensors collected in the testing of an electronic system. The paper lists a sequence of questions and how they were tackled by statistical analysis to meet the analysis goal. Eventually, we were able to provide a robust parsimonious and effective model for predicting the system condition using a subset of the 63 sensors. In handling this problem, we develop and apply several innovative methods and insights that can prove useful in other contexts. [ABSTRACT FROM AUTHOR]

Considering the development of deep learning and the emergence of intelligent control demands in nuclear reactors, along with the presence of plant‐level real‐time information monitoring systems in most nuclear power plants, there is a considerable accumulation of sensor measurements from long‐term operation. This makes it feasible to conduct medium to long‐term predictions for various real‐time conditions in nuclear power plants. Therefore, this paper proposes the utilization of a gate‐based recurrent neural network called GRU (Gated Recurrent Unit) and its variants for parameter prediction of LOCA (Loss of Coolant Accident) scenarios. The main content of this paper consists of two parts: (1) Experimental verification is conducted to demonstrate that GRU has excellent capability in capturing long‐term sequential information and generalization ability, making it suitable for predicting accident conditions in nuclear power plants. Two accident trend prediction methods based on the GRU network are proposed for scenarios with limited data. The results show that these methods can effectively provide short‐term development trends for accident conditions. Additionally, by considering the feature extraction capacity of CNN, the fusion of CNN and GRU models is employed for parameter prediction under different sizes of broken area. The results indicate an improvement in the model's generalization ability. (2) In scenarios with limited and incomplete data, a more robust variant of GRU called GRU‐D model is utilized for both univariate and multivariate synchronous prediction of accident conditions with different missing values. Experimental results demonstrate that even with a data missing rate of 90%, the GRU‐D network exhibits excellent predictive accuracy and generalization ability in parameter prediction for the given conditions. [ABSTRACT FROM AUTHOR]

The instability of product quality and low printing efficiency are the main obstacles to the widespread application of 3D printing in the manufacturing industry. Optimizing printing parameters can substantially improve product quality and printing efficiency. However, existing methods for optimizing process parameters primarily rely on computationally expensive numerical simulations or costly physical experiments, which cannot balance model accuracy and experiment cost. To the best of our knowledge, almost no relevant papers have been found to address the issues of product quality and printing efficiency in 3D printing from experimental data‐driven perspective. In this paper, we propose a method that integrates multiobjective Bayesian optimization (MOBO) with experimental data‐driven, aiming at obtaining more accurate optimization results at a lower cost. Distinguishing from previous studies, the proposed method utilizes experimental data instead of predicted values to update the model and find the optimal process parameters based on expected hypervolume improvement. The results of the 3D printing case study show that the proposed method can better model and optimize the highly fluctuating 3D printing process and obtain the optimal process parameters at a much lower cost. In addition, confirmatory experiments verify that the proposed method achieves higher printing efficiency while maintaining product quality. [ABSTRACT FROM AUTHOR]

Organizations are confronted with quality failures that must be addressed. Often, statistical methods can be helpful, but the organization lacks an internal statistician. This paper explores the use of a statistical problem‐solving team using Six Sigma professionals as problem‐solving team leaders, trainers, and coaches. A case study methodology is used. The actions and events from the implementation of a statistical problem‐solving team in a global manufacturing organization in the automotive industry through the first decade after implementation are described. Statistical problem‐solving bridges the gap between engineer and statistician using engineers trained to awareness‐level knowledge of statistics and supported by Six Sigma Master Black Belts. The statistical problem‐solving team members need to be highly trained in the three pillars of statistical problem‐solving: Key statistical concepts, key statistical analysis, and the key test method as well as a solid foundation in quality tools. This paper provides a unique view of a global problem‐solving team using both Six Sigma tools and methods and the 8D process as a problem‐solving methodology. [ABSTRACT FROM AUTHOR]

The present paper focuses on the group acceptance sampling plan when the characteristic of interest follows Another Generalized Transmuted‐Exponential distribution. The probability of acceptance is calculated using mean life as a quality parameter for obtaining design parameters such as sample size 'n' and acceptance number 'c'. The values of the operating characteristic function are determined for different quality levels. The plan satisfies both the producer's and consumer's risks at specified quality levels. Relevant r codes using Monte Carlo simulation are appended for use by academia and practitioners. The simulated results of the study are exhibited in the form of tables and explained with relevant examples. Results of the proposed plan are compared with the results generated using maximum likelihood estimates of Another Generalized Transmuted‐Exponential distribution. Easy to follow methodology of the current paper will open new vistas for applying the technique of group acceptance sampling plan to a variety of transmuted probability distributions. For illustration purposes, real data set is also analyzed to show the implementation of the proposed sampling plan. [ABSTRACT FROM AUTHOR]

In mixed elastohydrodynamic lubrication (EHL), the load distribution between the gear meshing surfaces is shared by the oil film and the asperities of the gear's rough surface. Based on the load‐sharing concept, this paper proposes a time‐varying meshing stiffness (TVMS) model for gears with mixed EHL. The initial step involves the utilization of the Greenwood‐Williamson model to calculate the contact stiffness of surface asperities, while the lubricating film is assessed using a curve‐fitting formula to investigate the influence of gear surface morphology on TVMS. Subsequently, the incorporation of gear fillet foundation deformation and friction enables accurate TVMS determination. The proposed method is employed to examine the meshing stiffness of the gear pair under both dry lubrication and EHL conditions. Comparative analysis reveals favorable agreement between the proposed model and experimental results obtained under dry lubrication, thereby highlighting the superior performance of the proposed approach. Moreover, the time‐varying friction coefficient under EHL is computed, and the impacts of gear surface morphology parameters, temperature, speed, and load on lubrication conditions and TVMS are investigated. The findings presented in this paper contribute significantly to advancements in gear design and performance. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) have a scale of applications linking to the industrial machines. As these machines often work in a wide temperature range, the PMSM quality consistency is threatened by the temperature fluctuate. In fact, at the start of the production of the PMSMs, the quality consistency distribution already exist due to the manufacturing dispersion. However, traditional experiment‐based consistency analysis method is inadequate for the PMSM, due to its inevitable destructive measurement for the magnetic properties. To deal with the problem, this paper proposed a model‐based quality consistency analysis method. In this paper, mechanism analysis of the temperature influence on PMSMs is carried out by the finite element analysis and the digital modeling method, so that the impact of dispersion can be depicted by model rather than destructive measurement. The relationship between the temperature and PMSM cogging torque as well as other features is quantitatively revealed. To obtain the NdFeB temperature coefficient, magnetic samples were prepared and measured. Then, the temperature characteristics of the sensitive components of PMSM are established, and the PMSM temperature characteristics can be simulated by integrating temperature coefficients into the PMSM digital model. The qualitative and quantitative temperature characteristics of cogging torque are verified by experiments with a batch of PMSMs. [ABSTRACT FROM AUTHOR]

As the central nervous system of modern information warfare, the command‐and‐control network becomes the brain of command and the strategic target of the enemy's primary attack during the operation. In this paper, a method based on edge extension diagram (EED) and binary decision diagram (BDD) is proposed to evaluate the reliability of connectivity within the basic command post of command‐and‐control network, which is solving the problem that the conventional methods failed to model failure probabilities of node links and with low calculation efficiency. First, graph simplification of network topology, extension of nodes under reliable state, construction of path functions, replacement of associated edges, variable sorting, construction of binary decision graphs, and recursive calculation to evaluate the reliability of the connection between the two ends of the command‐and‐control network. Then, further analyze and sort the variable importance of the command‐and‐control network and analyze the sensitivity of the network topology. This paper contributes to the reliability assessment of command‐and‐control network system, the identification and maintenance of key network nodes, and the design of network topology structure. [ABSTRACT FROM AUTHOR]

This paper considers a problem of determining the optimal number of components for maximizing reliability of a load‐sharing series system in which no redundant components exist. Previously, this problem has been studied numerically, under the assumption that the load–life relationship is described by a power function, and the component follows an exponential or a Weibull lifetime distribution. In this paper, our objective is to obtain analytical results for the optimal number of components given that a general link function explains a load–life relationship and the component follows a general lifetime distribution. We show that the optimal number of components non‐decreases with the elasticity of the link function and the ratio of the component failures sensitive to the changing design load. We also show that the premature failures occurring early in the life of a component affect the optimal solution, depending on whether a link function is used for scaling the nominal failure rate independently of the mission time, or it is used for scaling the nominal life dependently with the mission time. We believe that the result is useful in selecting the number of the components to use in a system when studying design trade‐offs at an early design stage. [ABSTRACT FROM AUTHOR]

Establishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling. [ABSTRACT FROM AUTHOR]

This paper develops two novel process monitoring schemes for the mean of a Gaussian process: the Bayes factor (BF) and the improved Bayes factor (IBF) schemes. Conjugate priors are used to construct the plotting statistics. The performance of the proposed schemes is evaluated in terms of average run length (ARL), standard deviation of run length (SDRL), and several percentiles, and these performance metrics across different hyper‐parameters and various sample sizes are evaluated via Monte Carlo simulations. Both zero‐state and steady‐state out‐of‐control (OOC) performances are investigated comprehensively. The simulation results show that the IBF scheme outperforms the existing Bayesian exponentially weighted moving average (EWMA) schemes under different loss functions in zero‐state. In steady‐state conditions, the IBF scheme outperforms for small shifts. Finally, we present two examples to illustrate the practical application of the proposed schemes. [ABSTRACT FROM AUTHOR]

Applying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making. [ABSTRACT FROM AUTHOR]

The step stress accelerated degradation test (SSADT) is an effective tool for assessing the reliability of highly reliable products. However, conducting an SSADT is expensive and time consuming, and the obtained SSADT data has an impact on the accuracy of the subsequent product reliability index estimations. Consequently, devising a cost‐constrained SSADT plan that yields high‐precision reliability estimates poses a significant challenge. This paper focuses on the optimal design of SSADT for the Tweedie exponential dispersion process with random effect (TEDR), a general degradation model capable of describing product heterogeneity. Under given budget and boundary constraints, the optimal sample size, observation frequency and observation times at each stress level are obtained by minimizing the asymptotic variance of the estimated quantile life at normal operating conditions. The sensitivity and stability of the SSADT plan are also studied, and the results indicate the robustness of the optimal plan against slight parameters fluctuations. We use the expectation maximization (EM) algorithm to estimate TEDR parameters and reliability indicators under SSADT, providing a systematic method for obtaining the optimal SSADT plan under budget constraints. The proposed framework is illustrated using the case of LED chips data, showcasing its potential for practical application. [ABSTRACT FROM AUTHOR]

The turbine disc plays a crucial role in aerospace engine, significantly influencing both their fatigue life and overall reliability. Due to the substantial uncertainties present, devising a method for the probabilistic analysis of turbine disc fatigue life becomes crucial. This paper introduces a novel framework that combines adaptive Kriging Monte Carlo simulation (AK‐MCS) with a mean stress correction model to evaluate fatigue life distribution effectively. AK‐MCS, stands out as an algorithm that constructs a Kriging model, effectively reducing the need for an extensive finite element analysis. Furthermore, a mean stress correction model is proposed for the Masson‐Coffin equation tailored for fatigue life prediction of GH4133 disc material. This model has been empirically validated to be effective. The presented framework for probabilistic fatigue life analysis not only holds considerable engineering value but also offers innovative approaches for tackling analogous challenges in the field. [ABSTRACT FROM AUTHOR]

This paper deals with an optimization problem of determining the maintenance units and age‐based inspection schemes to minimize the expected maintenance cost rate and satisfy a target system availability for multi‐indenture systems that have a modular structure with redundant units. Failures of primary components directly lead to system failures and can therefore be identified and maintained immediately, while failures of redundant components need to be identified by inspections. And the designated maintenance units need to be replaced during maintenance. By constructing the dual structure of the fault tree, we present a failure path enumeration method, based on which the system availability and expected maintenance cost rate models considering maintenance time are derived. For redundant units, two types of inspection policies are considered in our models: one is the corrective maintenance (CM)‐based inspection policy, and the other one is the age‐based inspection (PM) policy, inspections are performed at failures or age h, depending on which occurred earlier. A heuristic algorithm is proposed to find the reasonable maintenance units and inspection intervals simultaneously, and its performance is also compared and analyzed. Several numerical examples are studied to investigate the effect of parameters on optimal solutions. [ABSTRACT FROM AUTHOR]

In complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given. [ABSTRACT FROM AUTHOR]

In this paper, we have introduced the concept of a new multicomponent reliability function ρk(t).${\rho }_k( t ).$ Estimation procedures are developed for ρk(t)${\rho }_k( t )$ under progressive type II censoring scheme, which is subsequently used to develop the estimation procedures for the multicomponent stress‐strength reliability function ρs,k${\rho }_{s,\ k}$. Herein, we consider a system having k statistically independent and identically distributed strength components, Xi,i=1,2,…,k${X}_i,\ i = 1,2,\ldots,k$, say, which are exposed to a common random stress Y. Both strength and stress variables are independent and follow different scaled proportional hazard rate family of distributions with common scale parameter. The reliability functions ρk(t)${\rho }_k( t )$ and ρs,k${\rho }_{s,\ k}$ are estimated by using maximum likelihood and Bayesian methods of estimation. The performances of these estimators are studied on the basis of their mean squared errors through Monte Carlo simulation technique. Finally, the utility of the proposed estimators is examined and explained through two different real data sets. [ABSTRACT FROM AUTHOR]

In practice, structural limit state function (LSF) is often combined with finite element (FE) models, and the corresponding time‐dependent reliability assessment (TRA) inevitably involves dealing with computational inefficiency. In this paper, a general moment‐based outcrossing rate (GMO) method is proposed to conduct TRA efficiently, with no limitation in the form of LSF. There are two innovations of GMO method: firstly, an explicit moment‐based outcrossing rate is developed; secondly, a separate algorithm is proposed that facilitates the separation of FE analysis from TRA cycle. The proposed method minimizes the computational cost associated with FE analysis and enables TRA cycle to be conducted more efficiently with the assistance of an explicit moment‐based outcrossing rate and explicit moment‐based outcrossing rate. Three numerical examples are investigated to examine the accuracy and efficiency of GMO method for TRA cycle. GMO method is found from the results that it can efficient and accurate to conduct TRA. Such improvements in computational efficiency are particularly valuable for complex structures where FE analysis is computationally expensive and time‐consuming. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction (RUL) is a critical procedure in the application of prognostics and health management for devices or systems. It is difficult to predict the RUL in a time‐varying external environment. Specifically, many mechanical systems typically experience various operating conditions, which have impacts on the degradation process and degradation rate. In particular, the linear degradation modeling of the Wiener process‐based RUL prediction method has attracted considerable attention recently. However, the dependency of degradation rate and operating conditions is generally ignored in the current degradation modeling, which leads to inaccurate issues in the RUL prediction. Therefore, to solve the above issues, a novel RUL prediction method based on the Wiener process considering parameter dependence is proposed in this paper. At first, a linear Wiener process degradation model considering parameter dependence is constructed to describe the dependency of the drift coefficient and operating conditions. Secondly, the probability density function of RUL is derived under the concept of first hit time. After that, the collaboration between the Bayesian update and expectation maximization algorithm is introduced to update and estimate the model parameters. Finally, the validity and applicability of the proposed method are verified by a numerical simulation and three case studies of bearings. [ABSTRACT FROM AUTHOR]

This paper presents Bayesian techniques for conservative claims about software reliability, particularly when evidence suggests the software's executions are not statistically independent. We formalise informal notions of "doubting" that the executions are independent, and incorporate such doubts into reliability assessments. We develop techniques that reveal the extent to which independence assumptions can undermine conservatism in assessments, and identify conditions under which this impact is not significant. These techniques – novel extensions of conservative Bayesian inference (CBI) approaches – give conservative confidence bounds on the software's failure probability per execution. With illustrations in two application areas – nuclear power‐plant safety and autonomous vehicle (AV) safety – our analyses reveals: (1) the confidence an assessor should possess before subjecting a system to operational testing. Otherwise, such testing is futile – favourable operational testing evidence will eventually decrease one's confidence in the system being sufficiently reliable; (2) the independence assumption supports conservative claims sometimes; (3) in some scenarios, observing a system operate without failure gives less confidence in the system than if some failures had been observed; (4) building confidence in a system is very sensitive to failures – each additional failure means significantly more operational testing is required, in order to support a reliability claim. [ABSTRACT FROM AUTHOR]

In the paper, we conduct an assessment of the conditional expected delay (CED) profiles of the EWMA, CUSUM, and dual CUSUM charts based on a statistic linked to Brownian motion. The study reveals a noteworthy observation: the CED curves for these control charts may exhibit an increasing trend over time. Moreover, in a steady‐state scenario, it becomes evident that these control charts could be outperformed by their established counterparts. [ABSTRACT FROM AUTHOR]

This paper proposes a novel condition‐based maintenance model for a two‐component warm standby system considering an imperfect switching policy. Unlike the conventional models, joint optimization of preventive and failure switching strategies is investigated. The standby component can be switched preventively when the component reaches the preventive maintenance (PM) threshold before the online one fails. The failure switching is triggered when the online component reaches the corrective maintenance (CM) threshold. In addition, the switching mechanism between the two components is related to the order fulfillment rate. The optimal PM threshold and order fulfillment rate can be derived by minimizing the long‐term average cost rate. A numerical example is provided to demonstrate the robustness of the decision model under different scenarios through sensitivity analysis. Ultimately, the effectiveness of the proposed model in this article was verified by comparing the models. [ABSTRACT FROM AUTHOR]

Monitoring the ratio of two normal random variables, which is usually denoted as RZ, is one of the most important directions in statistical process control (SPC). In production scenarios, when the product specification is related to the relative ratio of two components in a mixture, or when the ratio represents a property of the product, or the difference between a product's quality measurement before and after an operation (such as a chemical reaction), the product quality can be improved by applying monitoring schemes for RZ. To further enhance the sensitivity of traditional RZ schemes in detecting process changes, especially small and moderate ones, this paper proposes an improved exponentially weighted moving average (IEWMA) monitoring scheme. The run length (RL) distribution of the proposed IEWMA‐RZ scheme is obtained by using the Monte‐Carlo (MC) simulation. The superiority of the proposed monitoring scheme is shown by comparing with the existing EWMA‐RZ scheme. [ABSTRACT FROM AUTHOR]

In this paper, a reliability evaluation method based on the hidden Markov model (HMM) considers the influence of degradation trajectory fluctuation. To address the problem in which the performance degradation trajectory modelling of complex systems is limited by sampling time points, this method constructs an approximate degradation trajectory model based on effective discrete data points through a radial basis function to realize the degradation data expansion of small samples. Then, the average degradation rate is defined, and the fluctuation degree of the degradation trajectory is quantitatively evaluated to obtain the evaluation function of the fluctuation degree of the degradation trajectory. In addition, to determine the relationship between the fluctuation degree of the degradation trajectory and the operating state of the system, the method combines HMM with the evaluation function of the fluctuation degree of the degradation trajectory and obtains the transition probability matrix of different operating states of the system affected by the fluctuation degree of the degradation trajectory. On the basis of the probability matrix of the failure state, the reliability change curve of the system is derived. Finally, the effectiveness of the proposed method is verified via an example of a computer numerical control machine tool. [ABSTRACT FROM AUTHOR]

Even though most control chart developments have revolved around the normal distribution, productive operations, in fact, often use non‐normal data, and some even use skewed distributions. This occurs with cycle times and many processes with a single specification limit. Under these conditions, gamma distributions often offer a better characterization where CUSUM schemes provide optimal performance to detect changes of a known in‐control (IC) parameter. In practice, gamma parameters have to be estimated, and limited phase I samples have undesirable effects on the phase II average run length (ARL) that has not been examined. To address this situation, this study uses Markovian models to assess the ARL of a CUSUM developed to monitor scale changes when IC parameters are estimated. This paper discusses the conditional ARL performance and proposes an adaptation of a stochastic estimation algorithm to guarantee a minimum performance. IC and out‐of‐control performance is compared with the conditional performance of a traditional approach where no corrections are made to guarantee a specific behavior. To demonstrate the practical application of this new approach, an implementation was made in a packaging process using multihead weighing machines. Practitioners interested in using reliable monitoring schemes will find, in this proposal, several implementation guidelines such as algorithms and look up tables to facilitate their implementations. [ABSTRACT FROM AUTHOR]

Accurate capacity estimation and degradation trajectory predictions are crucial part of effectively monitoring the current and future health status of lithium‐ion batteries, which ensures their optimal performance and longevity. However, it is hard to acquire precise predictions under various application conditions when relying on limited historical data. In this paper, we propose a novel deep time‐index framework based on meta‐learning to achieve online capacity estimation and degradation trajectory predictions with only a few monitoring data from early cycles. Highly correlated health feature from lithium‐ion battery degradation data is extracted for online capacity estimation. The deep time‐index framework consists of three parts, Implicit Neural Representations (INRs), Long Short‐Term Memory (LSTM) and Ridge regressor. Cycle numbers are selected as time feature and fed into the INRs to obtain informative representations. A LSTM module is employed to process the temporal representations and capture the long‐term dependencies to Ridge regressor to generate a direct multi‐step predictions. Three distinct lithium‐ion battery degradation datasets are applied to validate the efficacy of the proposed approach. The experimental results validate that within the deep time‐index framework, the short‐term predictions show remarkable performance. This high level of accuracy makes the proposed deep time‐index framework suitable for a wide range of real‐world scenarios, as it demonstrates precise predictions even under varying charging and discharging conditions with limited historical data. [ABSTRACT FROM AUTHOR]

The paper discusses the objective Bayesian estimation of the reliability of a multistate stress‐strength model (MSSM) based on different kernel functions. For the MSSM, we first derive the reliability function and Fisher information matrix. The Jeffreys prior, reference prior, and probability matching prior for the reliability function of the MSSM are constructed based on the objective Bayesian paradigm. Subsequently, we demonstrated that these priors are improper density, then evaluated the effects of these priors on Bayes estimates for MSSM's reliability based on a complete sample. The Bayesian estimates are calculated using random walk Metropolis–Hastings techniques. We employ Monte Carlo simulation to examine the effectiveness of Bayes estimates for MSSM's reliability in terms of average bias and mean squared error, meanwhile the highest posterior density credible intervals are investigated in terms of average length and coverage probability. Finally, two real datasets were examined, demonstrating the viability of the objective Bayes technique for small sample data. [ABSTRACT FROM AUTHOR]

The occurrence of probabilistic common cause errors (PCCEs) in man‐machine systems can lead to multiple human errors being affected by common causes and will contribute greatly to the reliability of the system. In this paper, A reliability modeling method based on event tree (ET)‐fault tree (FT) model for man‐machine systems subjected to PCCEs is proposed. Under the influence of PCCEs, risk factors in the system are not independent, and human errors affected by the same common cause will occur with different probabilities. To describe the dependencies among risk factors, a PCCE gate is proposed to extend FTs in the ET‐FT model. To analyze the impact of common causes on the human error probabilities and quantify the extended FT, an explicit method and an implicit method based on the Cognitive Reliability and Error Analysis Method are proposed. The proposed quantification methods consider the different relationships among multiple common causes in a single model. Finally, the proposed methods are validated in the reliability assessment of the emergency response system under malfunction of the solar wing mechanism. [ABSTRACT FROM AUTHOR]

A primary objective in detecting disease outbreaks is to precisely identify substantial increases in process means to control the spread of outbreaks. In this paper, we introduce a novel directionally sensitive adaptive multivariate exponentially weighted moving average chart for efficiently detecting either increases or decreases in the mean vector of a multivariate normally distributed process. In addition, the chart's sensitivity is enhanced through features such as variable sample size, variable sampling interval, and a combination of both. Monte Carlo simulation is employed to estimate zero‐state and steady‐state average time‐to‐signal (ATS) profiles. Performance measures, including expected weighted and relative ATS, are used for evaluating the performances of multivariate charts. Our results indicate that the proposed charts outperform existing charts in detecting various mean shift sizes. The application of these multivariate charts is demonstrated through illustrative examples. [ABSTRACT FROM AUTHOR]

Cyber‐Physical Systems (CPS) are at the forefront of the intersection between information technology and physical operations, revolutionizing industries such as communication, automation, control, and intelligent transportation. However, evaluating the reliability of CPS, especially in the presence of multilateral random attacks, poses a complex challenge. This paper introduces an inventive probabilistic modeling approach to enhance the analysis of CPS reliability under the influence of multilateral random attacks. We provide an overview of fundamental CPS concepts and their significant role in modern society, followed by a comprehensive exploration of multilateral random attacks. Our novel probabilistic modeling approach is presented as a means to improve the accuracy and robustness of CPS reliability assessments when faced with multilateral random attacks. Numerical experiments and simulations validate the effectiveness of our innovative approach, emphasizing its potential to fortify the reliability of Cyber‐Physical Systems. [ABSTRACT FROM AUTHOR]

Due to the noise interference and the weak characterization ability of the fault vibration signal of rotation vector (RV) reducer crankshaft bearing, it is difficult to obtain satisfactory results for the available fault diagnosis methods. For that, this paper proposes a variable‐speed‐condition fault diagnosis method with WSO‐VMD and ResNet‐SWIN. A signal reconstruction method with WSO‐VMD was carried out, Firstly, the performance of VMD algorithm is improved by using war strategy optimization algorithm to select parameters adaptively. Then the signal is reconstructed considering the fault characteristic frequency, so as to realize the noise reduction of the signal. By using the residual network module and attention mechanism to replace the first stage of the original SWIN model, a novel ResNet‐SWIN fault diagnosis model is established to enhance the feature extraction ability for the weak signal. The experiments with the constant‐operating‐condition and the variable‐operating‐condition are carried out to verify the effectiveness of the proposed method. The results show that, whether at variable‐speed or constant‐speed conditions, WSO algorithm has been proven to be the fastest convergence speed compared with WOA, SSA, and NGO optimization algorithms, and by the signal reconstruction with WSO‐VMD, the variance evaluation indicator of the reconstructed signal has 36%, 21%, 46%, and 40%, respectively. ResNet‐SWIN model has achieved the optimal diagnosis accuracy compared with SWIN, VIT, and CNN‐SVM models in both variable‐speed and constant‐speed conditions. [ABSTRACT FROM AUTHOR]

Web service composition (WSC), a distributed architecture, creates new services atop existing ones. Ensuring trust and assessing performance and dependability in online services coordination is essential. In this paper, “Web Service Reliability and Scalability Determination Using Depth Wise Separable Convolutional Neural Network” (WSRS‐DWSCNN) is proposed to assess the trustworthiness of online service compositions, particularly focusing on performance and dependability. This work addresses the need to predict the reliability and scalability of Business Process Execution Language (BPEL) composite web services. The proposed approach transforms the BPEL specification into a Depth Wise Separable Convolutional Neural Network (DWSCNN) and annotates it with probabilistic properties for prediction. The DWSCNN model classifies the outcomes as correct or incorrect, and to enhances the prediction of web service composition scalability and reliability, we optimize the DWSCNN's weight parameters using the Adolescent Identity Search Algorithm (AISA). The proposed technique is activated in Python and its efficacy is analyzed under some metrics, such as reliability, scalability, accuracy, sensitivity, specificity, precision, F‐measure. The proposed method provides 12.36%, 45.39%, and 25.97% better reliability, 41.39%, 11.39%, 34.16% better accuracy compared with existing methods like, Web service reliability prediction depending on machine learning (WSRS‐K‐means), reliability prediction method for multiple state cloud/edge‐basis network utilizing deep neural network (WSRS‐DNN‐BO), and improving reliability of mobile social cloud computing utilizing machine learning in content addressable network (WSRS‐CAN), respectively. [ABSTRACT FROM AUTHOR]

Bearings are foundational supporting components in diverse mechanical systems, essential for the reliable operation of these systems through real‐time monitoring and precise health state assessment. However, vibration signals from bearings in practical equipment often contain excessive noise and redundant information, complicating health state assessment. To address this challenge, this paper proposes a neural network‐based method named parallel encoder‐decoder (PED). This method features a parallel architecture that combines the long short‐term memory network and the temporal convolutional network for the encoder, along with a self‐attention module for the decoder. PED is adept at learning the temporal representations hidden in original signals and filtering vibration signals to remove noise and redundant information. Additionally, a multi‐objective loss function is developed to enhance the prediction results. A normalized Mahalanobis distance‐based metric is then employed to compare residual signals during bearing operation with those under normal conditions. The case study evaluates the PED observer's proficiency in accurately predicting vibration signals and assessing the performance of health indicator curves, demonstrating the proposed PED observer's superiority over conventional networks. [ABSTRACT FROM AUTHOR]

During the operational phase, complex systems are subject to various shocks, both random and continuous degradation. This coupling effect poses significant challenges for reliability assessment. The proposed method aims to provide a clear and objective evaluation of the system's reliability. In this paper, we propose a competitive failure reliability analysis method that considers the coupling effect of multiple shock processes and degradation processes. The random shocks are subdivided into external and internal shocks. The degradation increment is mainly influenced by external shocks, whereas internal shocks affect the degradation rate. The combined effect of these two shock processes accelerates the failure processes. Additionally, two different shock models are combined to create an extreme‐cumulative shock model to assess the impact of various shock processes and the degradation process on system reliability. The analysis examines the coupling effect between the degradation process and shock processes to explore the influence law between hard failure and soft failure. Subsequently, the reliability modeling of competitive failure processes is completed. Finally, practical case studies demonstrate the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Timely monitoring is essential for effective disease control and public health management. Early outbreak detection and swift responses inform optimal resource allocation for effective public health protection. Disease incidence datasets often consist of time series counts that exhibit threshold characteristics. Currently, there is limited research on the monitoring of such data in the field of statistical process control. In this paper, we propose a new threshold‐based control chart for threshold autoregressive models, which has been proven to be effective. Several existing efficient control charts are also employed for comparison. We conduct an extensive simulation study using the Monte Carlo method. Finally, the method is applied to the meningitis data that motivated this investigation. [ABSTRACT FROM AUTHOR]

Accurately predicting the remaining useful life (RUL) is crucial for the safety and stability of battery systems. Considering the inherent challenges in directly measuring the capacity of lithium‐ion batteries during operation, this paper proposes an online hybrid cascaded data‐driven prediction algorithm for RUL. Health indicators (HIs) are extracted from the charge–discharge voltage and incremental capacity curves, following which gray correlation analysis is employed to quantitatively assess the relevance between the HIs and batteries' capacities. Redundancy of HIs is eliminated through kernel principal component analysis, which enhancing the efficiency of subsequent analysis. The proposed framework incorporates the sparrow search algorithm‐based kernel extreme learning machine (SSA‐KELM) as the first‐level prediction model, establishing the relationship between HIs and capacities. The bidirectional long short‐term memory (BiLSTM) is utilized as the secondary‐level model, which integrates the preliminary capacity predictions of SSA‐KELM. Finally, experimental validation using battery datasets from NASA and Oxford showed that the method has remarkable generalization ability and superior prediction accuracy. Quantitatively, the RMSE and MAPE of NASA batteries are within 0.03, while the errors of Oxford batteries are within 0.003. The RUL prediction errors of all lithium‐ion batteries are within two cycles. [ABSTRACT FROM AUTHOR]

This paper proposes a risk assessment method based on the best‐worst method (BWM) and decision‐making trial and evaluation laboratory (DEMATEL) to evaluate the fire safety level of alternative design schemes for cruise ships relative to the standard‐based options. Firstly, the analytic hierarchy model of fire safety assessment is constructed by analyzing the passive fire protection system, active fire protection system, and evacuation and escape capability of cruise ship large cabins. Then, the linguistic scale functions and Z‐numbers are used to deal with the fuzzy evaluation of fire safety factors by experts, and the maximizing consensus technique is adopted to determine the weight of each expert to enhance group consistency. Considering the influence degree of each factor on fire safety and the correlation between factors, the BWM is combined with DEMATEL to establish the risk source weight calculation model. The fire safety level of alternative cruise ship designs is further determined by the fuzzy comprehensive evaluation approach. Finally, to validate the effectiveness of the proposed method, a fire safety assessment is conducted on the alternative design scheme of a luxury cruise passenger restaurant. The results confirm that the proposed method can effectively evaluate the fire safety of alternative design compared to the standard‐based scheme. [ABSTRACT FROM AUTHOR]

The prognosis of organic light‐emitting diodes (OLEDs) not only requires early detection of a bearing defect, but also the capability to predict their life data under all operational scenarios. The use of sophisticated machine learning (ML) algorithms is undoubtedly becoming an increasingly exciting research direction, as these algorithms can yield high predictive models with minimal domain expertise. The central question of this perspective is: how well can ML models advance our ability to forecast the lifetime of OLEDs compared to the physics‐based models? In this paper, data‐driven methods, feed‐forward neural networks (FFNN), support vector machines (SVMs), k‐nearest neighbors (KNNs), partial least squares regression (PLSR), and decision trees (DTs), are used to predict the lifetime and reliability of OLEDs through analyzing the lumen degradation data collected from the accelerated lifetime test. The final predicted results indicate that both the data‐driven and our physics‐based OLED lifetime models fit well the experimental data. The main drawback of the former method is that their efficacy is highly contingent on the quantity and quality of the operational dataset. Among all these methods, much more reliability information (time to failure) and the highest prediction accuracy can be achieved by FFNN. [ABSTRACT FROM AUTHOR]

The surging global demand for renewable energy has fueled the expansion of offshore wind energy, leveraging superior wind profiles and technological advancements in offshore wind turbine technology. Despite this growth, the operational experience of floating offshore wind turbines still needs to be improved as an emerging technology. Addressing this knowledge gap, our study compiles a comprehensive database of failure events associated with these turbines. Employing a rigorous analytical approach through the Fuzzy Failure Mode and Effect Analysis (FMEA) methodology, we conduct an in‐depth failure analysis of floating offshore wind turbines. This investigation allows us to identify the most critical failure modes and pinpoint components vulnerable to failures. The paper delves into the root causes of these significant failure modes, proposing preventive and corrective measures based on our findings. Our recommendations serve as a strategic guide for stakeholders, offering insights to enhance offshore wind turbines’ design, operation, and maintenance practices and the broader wind farm infrastructure. Despite this, further clarification on the application and highlights of the fuzzy FMEA model is warranted in the abstract, a point we address in the revised conclusion to enhance our study's overall clarity and completeness. [ABSTRACT FROM AUTHOR]

Risk‐adjusted control charts have been widely used in monitoring surgical quality in detecting risks of surgical performance. Most of the previous approaches focus on shifts in the location parameter as well as the existence of the scale parameter, which cannot get the full measure of the scale parameter under different levels. Ignoring the magnitude of the scale parameter, the monitoring methods cannot detect different variations of surgical mortality that is measured by scale parameter and required to reflect surgical quality improvement. The method of detecting variations in surgical quality is of interest in surgical quality improvement. This paper uses a new weighted h‐likelihood method to obtain a weighted score test for the surgical risks from the logistic model. Then an exponentially weighted moving average chart can be constructed to monitor the changes in the variance of risks, which could be of interest in practical surgical monitoring programs. Simulation results indicate that the proposed approach performs more efficiently than existing methods under various magnitudes of shifts in scale parameters on top of different pre‐set threshold stability. In addition, the application of the proposed method to real surgical data from the Surgical Outcome Monitoring and Improvement Program in Hong Kong shows the improvement and deterioration in a hospital's outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes a more computationally efficient approach for resilience assessment of rail transit system under disruptions. An improved linear programming model is developed to depict commuter flows and estimate system statuses. To address the computational challenge caused by the complexity of system, a four‐step approach is proposed based on the proposed commuter flow model. In the first step, Origin‐Destination (OD) pairs are divided into smaller groups and their flows under normal conditions are estimated by the proposed model separately, with the assumption that the railway capacity is sufficient relative to demand. Next, overall system statuses under normal conditions, including commuters on each train and spare capacities of each train are calculated by integrating results obtained in the first step. In the third step, system statuses under disruptions are estimated. In this step, we assume that unaffected commuters will not change their routes and flows of all affected commuters are estimated by a modified commuter model with given spare space of trains. Based on these outputs, several critical measures are introduced and calculated to quantify the resilience, resistance, and recovery ability of rail network systematically. We also demonstrate how our approach could be used to facilitate design and evaluation of bus bridging service. The proposed approach is demonstrated on the core part of Hangzhou rail transit network. [ABSTRACT FROM AUTHOR]