In practice, a shift in the process parameters (location and/or dispersion) is unknown in prior and cannot be diagnosed precisely with the classical cumulative sum (CUSUM) control chart. To overcome this issue, this study proposed two adaptive CUSUM (ACUSUM) control charts. The proposed control charts utilized linear weighted function that is inspired by generalized likelihood ration test (GLRT) to

Despite many advances in the field of computational system reliability analysis, estimating the joint probability distribution of correlated non‐normal state variables on the basis of incomplete statistical data brings great challenges for engineers. To avoid multidimensional integration, system reliability estimation usually requires the calculation of marginal failure probability and joint failure

In industrial applications, a functional relationship between the quality characteristics of interest is well represented by profile functions. These are often characterized by linear or nonlinear relationships that depend on the nature of the experimental study. A monitoring method based on linear profiles is known as linear profiling, which is commonly used because of its simplicity. In this article

Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed

Failure mode and effects analysis is a widely applied risk assessment method in various engineering and management domains. However, the identification of failure modes is difficult and uncountable. Therefore, a function–motion–action (FMA) decomposition method is developed to identify failure modes from the perspective of motion and extraordinarily suitable for mechatronic products. In the typical

In this paper, a novel multistage reliability model is provided as systems are often divided into many stages according to system degradation characteristics. Multistage hard failure (caused by random shock) process (MHFP) and multistage soft failure (caused by random shock and continuous degradation) process (MSFP) are introduced to describe the competing failure processes, where either the MSFP or

Burn‐in is an effective method to screen out early failures of electronic devices. Typically, this is achieved by operating the devices under accelerated stress conditions. This paper focuses on a burn‐in concept where a random sample of devices is drawn out of the running production, put to burn‐in, and investigated for early failures. This procedure is called burn‐in study. In parallel, as long as

In this paper, some efficient monitoring and post‐signal follow‐up approaches are studied and compared for joint surveillance of location and scale of a process using the notions of circular‐grid (CG) schemes. Precisely, three variants of CG Cucconi schemes are introduced and compared with three variants of percentile modified Lepage (PML) schemes. One of the PML schemes is equivalent to the traditional

In this paper, reliability estimation of multicomponent system under a multilevel accelerated life testing. When the lifetime of components follows Weibull distribution, the problem of point and interval estimates are discussed from different perspectives. Under a general life‐stress assumption that there are multiple nonconstant and stress‐dependent scale and shape parameters, the maximum likelihood

With the development of modern acquisition techniques, data with several correlated quality characteristics are increasingly accessible. Thus, multivariate control charts can be employed to detect changes in the process. This study proposes two multivariate control charts for monitoring process variability (MPVC) using a progressive approach. First, when the process parameters are known, the performance

With ISO 7870‐8, a standardized application of charting techniques for short runs and small mixed batches was presented in 2017. Similar to various scientific approaches, it requires that sample values from grouped processes follow nearly identical distributions. In practice, however, there tend to be differences between distribution parameters. Moreover, equal parameters do not ensure that distributions

A 2017 JQT article presented a methodology for disentangling two‐factor interactions that are aliased with other two‐factor interactions in 2 k − p designs. The methodology is based on the concept of conditional main effects, and it resolves aliasing by considering active the confounded interaction whose estimated effect is similar to one of the main effects of the two factors that are involved in

The widely used Weibull distribution could be generalized to be q‐Weibull distribution. To fill out the gap in existing literature, the reliability is studied for q‐Weibull distribution with multiply Type‐I censored data, which is the general form of Type‐I censored data. The point estimates and confidence intervals (CIs) for q‐Weibull parameters and reliability parameters such as the reliability and

Accelerated life testing is an efficient tool frequently adopted for obtaining failure time data of test units in a lesser time period as compared to normal use conditions. We assume that the lifetime data of a product at constant level of stress follows an exponentiated Poisson‐exponential distribution and the shape parameter of the model has a log‐linear relationship with the stress level. Model

Balanced systems have extensive applications in engineering fields such as new energy storage and aeronautics. The reliability analysis of such systems has been reported in literature. However, most existing studies focus on the binary‐state situation, and research on multi‐state cases and the relevant rebalancing mechanism is still limited. To fill this gap, a multi‐state k‐out‐of‐n:F balanced system

Using control charts for monitoring therapeutic processes has become popular lately. As the application of traditional control charts in the therapeutic processes may be misleading due to the inherent differences between patients, a multifactor correlated risk measure is considered in monitoring of these processes. Therefore, using risk‐adjusted control charts for monitoring the therapeutic processes

Existing multivariate cumulative sum (MCUSUM) control charts involve entire associated variables of a process to monitor variations in the mean vector. In this study, we have offered MCUSUM control charts with principal component method (PCM). The proposed MCUSUM control charts with PCM capture the whole process variations using fewer latent variables (principal components) while preserving as much

In this study, a new process dispersion monitoring control chart is proposed based on a function‐based adaptation to select the smoothing constant value, named as a function‐based adaptive exponentially weighted moving average (EWMA) dispersion control chart. It is suggested to track shift ranges first expected in process dispersion by opting for smoothing constant computation with the help of a function

The progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the

Control charts are designed under the normality assumption of the quality characteristic of the process. However, the normality assumption rarely holds in practice. In non‐normal conditions, parametric charts tend to display more false alarm rates and invalid out‐of‐control comparisons. The exponentially weighted moving average chart is a frequently used memory‐type control chart for monitoring the

Space‐filling designs are now commonly used as a flexible model‐free strategy for providing good coverage throughout an input space of interest for a variety of computer and physical experiment design scenarios. Some of the preliminary choices about how to frame the problem and which type of design to use can have a substantial impact on the success or failure of the experiment, and yet how to make

This errata corrects the following Special Issue: ENBIS‐19 Quality and Reliability Engineering International, Volume 36, Issue 6, Pages 2593–2768, First Published on 07 November 2020. Five manuscripts for the issue were misplaced and wrongly assigned to other issues. The misplaced manuscripts are as follows: QRE 2673‐ A Hidden Markov Model to Detect Regime Changes in Cryptoasset Markets. https://onlinelibrary

Lately, the multivariate setup of control charts, especially the memory‐less chart has received less attention of researchers as compared to the univariate setup. However, the multivariate setup is of paramount importance in this big‐data era. In this research work, we study the multivariate Shewhart chart for monitoring location parameter by examining the robustness of this scheme with the mean estimator

Statistical process control (SPC) and monitoring techniques are useful in a variety of applications. In this paper, we consider prospective (Phase II) process monitoring for the balanced random effects (variance components) model with Shewhart‐type charts when parameters are estimated from a Phase I study. Such a model is a nonstandard application of control charts and arises in a number of situations

Some international pharmaceutical companies have succeeded in producing vaccines against COVID‐19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K‐independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics

When a unit should provide reliability at mission time, the mission success probability or failure probability becomes an important constraint to plan inspection policies. From this viewpoint, this paper considers the optimization problems of periodic and sequential inspection policies with mission failure probabilities. In this paper, a mission time is considered as a renewal point for inspection

Nonparametric (or distribution‐free) control charts are used for monitoring processes where there is a lack of knowledge about the underlying distribution. In this article, a triple exponentially weighted moving average control chart based on the signed‐rank statistic (referred as TEWMA‐SR chart) is proposed for monitoring shifts in the location parameter of an unknown, but continuous and symmetric

Fast initial response (FIR) features are generally used to improve the sensitivity of memory‐type control charts by shrinking time‐varying control limits in the earlier stage of the monitoring regime. This paper incorporates FIR features to increase the sensitivity of the homogeneously weighted moving average (HWMA) monitoring schemes with and without measurement errors under constant as well as linearly

Recently, the two‐parameter Chen distribution has widely been used for reliability studies in various engineering fields. In this article, we have developed various statistical inferences on the composite dynamic system, assuming Chen distribution as a baseline model. In this dynamic system, failure of a component induces a higher load on the surviving components and thus increases component hazard

A progressive average chart usually triggers initial out‐of‐control (OC) signals more simply and quickly than other memory‐type charts . In this paper, two progressive average control procedures are proposed for monitoring the coefficient of variation (CV) of a normally distributed process variable, namely, the progressive CV (PCV) and progressive resetting CV (PRCV) control charts , respectively.

Reliability‐based robust design optimization (RBRDO) is a crucial tool for life‐cycle quality improvement. Gaussian process (GP) model is an effective alternative modeling technique that is widely used in robust parameter design. However, there are few studies to deal with reliability‐based design problems by using GP model. This article proposes a novel life‐cycle RBRDO approach concerning response

In reliability research, electronic devices are an important part of our lives and modelling their lives is the most difficult and fascinating area. To investigate the failure functioning of electronic equipments, reliability monitoring of systems is widely used. However, it is stated in the literature that one in five electronic system collapses are a consequence of degradation and saving energy and

There have been many researches focusing on the dynamic characteristics of gear faults, such as spalling, crack and broken, to improve the accuracy of fault diagnosis methods. However, the tooth pitch error caused by actual gear manufacture precision is usually neglected, which may have a direct influence on the gear vibration response. In order to settle the problem of the underlying vibration mechanism

Failure mode and effect analysis (FMEA) is a tool used to define, identify, and prevent known or unknown potential risks. An improved FMEA based on interval triangular fuzzy numbers (IVF) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is proposed in this study to solve problems of expression and processing of uncertain information, weights of risk factors, and ranking

Control charts play a vital role in tracing unfavorable changes in the ongoing process parameters to enhance the quality of the output. Attribute charting schemes are commonly utilized for tracing shifts in fraction nonconforming and number of nonconformities per unit in any production process. A double progressive mean charting structure for monitoring Poisson observations (PDPM) is recently proposed

The reflected power function distribution (RPFD) has applications in the fields of reliability engineering and survival analysis. To identify and remove the variation in different reliability processes and also to monitor the reliability of machines where the number of errors follows RPFD, we develop control charts to keep the process in control. A memory less control chart like a Shewhart control

Control charts are widely known quality tools used to detect and control industrial process deviations in statistical process control. In the current paper, we propose a new single memory‐type control chart, called the sum of squares triple exponentially weighted moving average control chart (referred as SS‐TEWMA chart), that simultaneously detects shifts in the process mean and/or process dispersion

The Poisson distribution assumption often arises in several industrial applications for modeling defects or nonconformities. In this work, we investigate the one‐ and two‐sided performance of a new adaptive EWMA (exponentially weighted moving average)‐type chart for monitoring Poisson count data. An appropriate discrete‐state Markov chain technique is provided to compute the exact ARL (average run

Statistical process control consists of tools and techniques that are useful for improving a process or ensuring that a process is in a stable and satisfactory state. In many modern industrial applications, it is critically important to simultaneously monitor two or more correlated process quality variables, thus necessitating the development of multivariate statistical process control (MSPC) as an

Mann‐Whitney and Signed‐Rank control charts are two well‐known nonparametric charts used for controlling the center of the process when the distribution of the process parameter is unknown or nonnormal. Considering the effect of measurement error on the performance of control charts, the mentioned effect with additive model is investigated on Mann‐Whitney and Signed‐Rank charts. Furthermore, a comparison

Memory‐type auxiliary‐information‐based (AIB) control charts are very effective in detecting small‐to‐moderate shifts in the process mean. In this study, we first develop a unique uniformly minimum variance unbiased estimator of the process mean that requires information on the study variable as well as on several correlated auxiliary variables. Then, based on this estimator, adaptive and nonadaptive

The lifetime distributions with bathtub‐shaped hazard rate functions and censoring scheme have been used widely in life testing and reliability engineering. This paper develops a new approach for estimating parameters of an important two‐parameter lifetime data analysis model with bathtub‐shaped hazard rate function under the assumption that sample is modified progressively hybrid censored. One of

Control charts are widely used in industrial environments for the simultaneous or separate monitoring of the process mean and process variability. The Max‐Mchart is a multivariate Shewhart‐type simultaneous control chart that is used when monitoring subgroups. While this sampling design allows the computation of the generalized variance (GV) used to calculate the process variability, a GV chart cannot

Applications of shift detection are of interest in several disciplines. Sufficient reduction (SR) methods have been developed for detecting a shift in a multivariate process under different conditions in several studies. However, all methods were proposed to detect only a constant, but persistent, mean shift. In practice, there might be other types of (mean) shift to be considered. Our purpose here

Frequent early failures have been the key factor restricting the reliability improvement of electromechanical product, and also reducing its market competitiveness. Most of the current early failure elimination methods are passive elimination technologies after a failure. Their purpose is to quickly restore the production capacity of products, which is mainly used to improve the reliability of the

Event logs, comprising data on the occurrence of different types of events and associated times, are commonly collected during the operation of modern industrial machines and systems. It is widely believed that the rich information embedded in event logs can be used to predict the occurrence of critical events. In this paper, we propose a recurrent neural network model using time‐to‐event data from

The Failure Mode and Effect Analysis (FMEA) is a useful instrument born in the aerospace industry and widely used to improve a process or product's efficiency. Over the years, this instrument has been adopted in increasingly different contexts, such as HealthCare. This paper proposes an approach aimed at improving the defects typical of the classic FMEA in the design phase, that is, in a scenario full

In this paper, we consider interval estimation for the inverse Gaussian (IG) distribution. Using generalized pivotal quantity method, we derive the generalized confidence intervals (GCIs) for the model parameters and some quantities such as the quantile, the reliability function of the lifetime, the failure rate function, and the mean residual lifetime. We verify that the GCI of the scale parameter

The Gamma distribution has been applied in research in several areas of knowledge, due to its good flexibility and adaptability nature. Process capacity indices like C p k are widely used when the measurements related to the data follow a normal distribution. This article aims to estimate the C p k index for nonnormal data using the Gamma distribution. We discuss maximum likelihood estimation and a

In the context of a disease outbreak detection, a prime interest is to only detect increases in the process mean. It is thus desirable to have a directionally sensitive multivariate chart that can effectively detect either increases or decreases in the process mean vector. In this paper, with a suitable transformation that truncates multivariate observations either above or below the process mean vector

Most research in design of experiments focuses on appropriate designs for a system with just one type of response, rather than multiple responses. In a decision‐making process, relying on only one objective can lead to oversimplified, suboptimal choices that ignore important considerations. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of

The average control chart monitors the shifts in the process. The familiar multivariate control charts are used to detect the mean vector of the process such as multivariate cumulative sum (MCUSUM) and Hotelling's T2 control charts. In this paper, the effects of constructing bivariate copulas on multivariate control charts, that is, MCUSUM and Hotelling's T2 control charts are intensively investigated

Decision‐making trial and evaluation laboratory (DEMATEL) analysis is an effective and comprehensive method for identifying accident factors and converting the relationships among them into a visual structural model. Traditionally, the mean value method is adopted to summarize the initial direct‐relation matrix, but it ignores the errors caused by differences in expert knowledge. In addition, a single

Control charts are widely applied in many manufacturing processes to monitor the quality characteristic of interest. Recently, a homogeneously weighted moving average (HWMA) control chart was proposed as an improvement of the exponentially weighted moving average (EWMA) chart for efficiently monitoring of small shifts in the process mean. In the present article, we extend the HWMA chart by imitating

This article generalizes the historical S 2 chart (of equal sample sizes) to subgroups of differing sample sizes. As a sequel to a previous article, Section 2 compares all estimators of process standard deviation for more than M > 1 subgroups of equal sample sizes from a normal universe. Section 3 provides the general probability control limits (CONLs) for the S 2 chart of unequal sample sizes. Section

With recent advancements in Internet technologies and wireless communications, wireless sensor network (WSN)‐based smart systems are gaining an enormous increase in use in various applications (eg, healthcare, smart home, smart manufacturing, smart power grids, and smart transportation). Due to the mission‐critical or safety‐critical nature of smart system applications, it is imperative that a smart

This paper analyzes the competing and dependent failure processes for multi‐state systems suffering from four typical random shocks. Reliability analysis for discrete degradation is conducted by explicitly modeling the state transition characteristics. Semi‐Markov model is employed to explore how the system vulnerability and potential transition gap affect the state residence time. The failure dependence

Abd El‐Monsef, Sweilam and Sabry [Quality and Reliability Engineering International, 2020, doi: 10.1002/qre.2780] proposed a four‐parameter exponentiated power Lomax distribution, derived its properties and illustrated three data applications. In this note, we show that several properties expressed in terms of infinite sums can be simplified to a known special function. We also show that certain one‐parameter

Controlling and reducing process variability is an essential aspect for maintaining the product or service quality. Even though most practitioners believe that an increasing process variability is often a more severe concern than a shift in location, barely a few research paid attention to the cost‐efficient monitoring of process variability. Some of the existing studies addressed the dispersion aspect

This article studies the economic and economic‐statistical designs of the auxiliary information (AI) based side sensitive group runs (SSGR‐AI) chart. The regression estimator that consists of information not only from the primary variable but also from the auxiliary variable is integrated into the control charting statistic. Optimal designs of the SSGR‐AI chart, for the minimization of the expected