The lead‐acid battery has been widely used in various fields. In civil aviation aircraft, it plays a vital role in the power system to maintain normal operation during the flight mission. Thus, an effective abnormal detection system for monitoring and diagnosing the status of aircraft lead‐acid battery is essential to ensure its safety and reliability. This paper aims to effectively identify aircraft

Key responses of manufacturing processes are often represented by spatially or time‐ordered data known as functional data. In practice, these are usually treated by extracting one or few representative scalar features from them to be used in the following analysis, with the risk of discarding relevant information available in the whole profile and of drawing only partial conclusions. To avoid that

Statistical quality control is used to improve performance of processes. Since most of the processes are multivariate in nature, multivariate process capability indices (MPCIs) have been developed by many researchers depending on the context. However, it is generally difficult to understand and calculate MPCIs, compared to their univariate counterparts like C p , C p k , and so on. This paper discusses

Failure mode and effect analysis (FMEA) is a useful technique to identify and quantify potential failures. FMEA determines a potential failure mode by evaluating risk factors. In recent years, there are many works improving FMEA by allowing multiple experts to use linguistic term sets to evaluate risk factors. However, it is important to design a framework that can consider both the weight of risk

Profile monitoring is one of the methods used in statistical process control ( S P C ) to understand the functional relationship between response and explanatory variables by tracking this relationship and estimating parameters. SPC is done in two phases: In Phase I , a statistical model is created and its parameters estimated using historical data. Phase I I implements the statistical model and monitors

Advancements in information technology have made various industrial equipment increasingly sophisticated in recent years. The remaining useful life (RUL) of equipment plays a crucial important role in the industrial process. It is difficult to establish a functional RUL model as it requires the fusion of time‐series data across different scales. This paper proposes a long‐short term memory neural network

Time between events (TBE) charts are used in high‐yield processes where the rate of occurrences is very low. In the current article, we propose a triple exponentially weighted moving average control chart to monitor TBE (regarded as triple exponentially weighted moving average TEWMA‐TBE chart) modeled by a gamma distribution. One‐ and two‐sided schemes of the proposed chart are designed and compared

In this paper, we are interested in monitoring the stability of a Weibull process mean. We introduce a simplified variable sample size (SVSS) procedure, which consists of using alternately sample sizes n A and n B with n A > n B , to improve the performance of the traditional X ¯ control chart (a fixed sample size n). Operationally, the proposal is relatively simple and its adoption yields a faster

This paper proposes a parameter‐free Kullback‐Leibler information control chart for monitoring sustained shifts in the process mean of a normally distributed process in phase II. Two plotted statistics are provided. One is based on our backward empirical sequential test, the other is based on the maximum log‐likelihood ratio change point method. These two achieve similar performances for the control

Circular prediction regions are used in ballistic testing to express the uncertainty in shot accuracy. We compare two modeling approaches for estimating circular prediction regions for the miss distance of a ballistic projectile. The miss distance response variable is bivariate normal and has a mean and variance that can change with one or more experimental factors. The first approach fits a heteroskedastic

This paper proposes a new Shewhart‐type control chart for circular or directional data. This type of data is found in several fields and applications, such as wind direction, the arrival time of a patient in a hospital, and the route of animals. The proposal is based on the Jones–Pewsey distribution, which is a very flexible three‐parameter distribution for modeling processes that can be represented

In this paper, a resubmitted sampling‐based successive sampling over two successive occasions control chart is proposed to monitor the underlying characteristic of interest. Auxiliary information of the first occasion is utilized to monitor the relative change in the study variable over the second occasion successively despite high degree of correlation. The structural and operational design is presented

In this paper, a new extension of Lomax distribution called the exponentiated power Lomax (EPOLO) distribution is proposed. The proposed model accommodates monotonically increasing, decreasing, and unimodal hazard rates. A full study of the proposed model parameters using four techniques is introduced and a simulation study is performed to examine the performance of the four methods of estimation for

In this paper, expected Bayesian (E‐Bayesian) estimation for the simple step–stress model based on type‐II censoring scheme is considered. The case of exponential distribution for the underlying lifetimes is considered assuming a cumulative exposure model. The E‐Bayesian estimation is discussed by considering three different prior distributions for the hyperparameters. The E‐Bayesian estimators as

To accurately predict quality loss of high reliability and long‐life product in service, a dynamic quality characteristics model is established based on linear degeneration and random error. According to this model, the expectation and variance of dynamic quality characteristics can be figured out. Moreover, assuming that the quality characteristics follow the normal distribution at the initial stage

Accelerated life testing (ALT) is the process of testing products by subjecting them to strict conditions in order to observe more failure data in a short time period. In this study, we compare the methods of two‐level constant‐stress ALT (CSALT) and simple step‐stress ALT (SSALT) based on competing risks of two or more failure modes with independent exponential lifetime distributions. Optimal sample

We, I mean colleagues in academia, like to develop models and want to get them published. In fact, a large proportion of the submissions to QREI contain newly developed models. In this editorial, I would like to share with you what are the key elements that we look for in screening/reviewing these submissions. I hope this will serve as a guide for young colleagues in preparing their research work for

The authors would like to correct Reference 11 as: 11. Keefe MJ, Woodall WH, Jones‐Farmer LA. The conditional in‐control performance of self‐starting control charts. Qual. Engin. 2015;27:488–499

This paper proposes a variable sampling interval n p x − X ¯ control chart for monitoring the process mean using combined attribute‐variable inspection. An economic design of the proposed chart using Taguchi's quadratic loss function is presented. The proposed model minimizes the total expected cost per unit time while minimizing out‐of‐control average time‐to‐signal and maintaining reasonable in‐control

The distribution of consumer lifetimes, high‐voltage of current in semiconductor transistors, and the risk associated with monitoring health care often come with a threshold. A two‐parameter (or shifted) exponential distribution is, in general, regarded as a better statistical model in such situations compared with a traditional (one‐parameter) exponential model. Research on inferential problems associated

With the development of industrial Internet, smart manufacturing information systems (SMISs) are faced with more uncertainties, dynamics, and complexity. These problems bring more challenges to the reliability operation of SMISs. To solve the above problem, a prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability

An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it

In practice, measurement errors exist and ignoring their presence may lead to erroneous conclusions in the actual performance of control charts. The implementation of the existing multivariate coefficient of variation (MCV) charts ignores the presence of measurement errors. To address this concern, the performances of the upward Shewhart‐MCV and exponentially weighted moving average MCV charts for

The purpose of this article is to develop an effective method to evaluate the reliability of structures with epistemic uncertainty so as to improve the applicability of evidence theory in practical engineering problems. The main contribution of this article is to establish an approximate semianalytic algorithm, which replaces the process of solving the extreme value of performance function and greatly

The paper introduces two fundamental approaches for reliability improvement and risk reduction by using nontrivial algebraic inequalities: (a) by proving an inequality derived or conjectured from a real system or process and (b) by creating meaningful interpretation of an existing nontrivial abstract inequality relevant to a real system or process. A formidable advantage of the algebraic inequalities

The D M P ‐criterion is proposed to generate optimal designs for the logistic regression model with reduced separation probabilities. This compound criterion has two components: (a) the D ‐efficiency of the candidate design and (b) a penalty term that captures the average distance of the candidate design's support points from the region of maximum prediction variance (MPV). A D M P ‐optimal design

The Shewhart control chart is used for detecting the large shift and an exponentially weighted moving average (EWMA) control chart is used for detecting the small/moderate shift in the process mean. A scheme that combines both the Shewhart control chart and the EWMA control chart in a smooth way is called the adaptive EWMA (AEWMA) control chart. In this paper, we proposed a new AEWMA control chart

The coefficient of variation (CV) is used in process monitoring when the process mean and standard deviation are proportional to each other. In this work, a side‐sensitive modified group runs CV (SSMGR CV) chart is proposed for monitoring the process CV. The run length performance of the SSMGR CV chart is compared to those of the existing CV charts in terms of the average and standard deviation of

Calcium silicate units are versatile and widely used construction materials for edifices. Their production process involves several factors that concern either the mixture of the raw materials or the curing process. The understanding of how raw materials and process variables interact in achieving the compressive strength of the final product enables a cost‐ and energy‐efficient layout of the production

Traditional reliability analysis techniques focus on the occurrence of failures over time. Nevertheless, in certain cases where the occurrence of failures is tiny or almost null, the estimation of the quantities that describe the failure process is compromised. In this context, we introduce a reliability model for systems adopting the degradation process using frailty. The evolved degradation model

Equipment fault is a major factor that affects the reliability of manufacturing systems. Fault source identification and propagation path search are primary means to reduce and eliminate equipment faults. To this end, this paper proposed a two‐layer model of equipment fault propagation in a manufacturing system. One layer, modeled by a small‐world network, is a network of parts for a single equipment

Reliability allocation of industrial robot (IR) system is one of the important means to improve its whole life cycle, reduce maintenance cost, and characterize weak subsystems. The IR system is not only very complex but also has strong customization; meanwhile, its sample data are small, resulting in unclear degeneration and failure. Based on the above two epistemic uncertainties, a new methodology

Memory‐type control charts play a significant role to identify slight changes in the parameters of the production process. In this article, we have proposed a new cumulative sum chart that utilizes the statistic of the homogeneously weighted moving average chart. The performance of the proposed chart is studied using Monte Carlo simulations. The proposed chart is compared with some existing charts

Modern and emerging techniques of technology have brought a revolution in quality inspection of products. When events in highly efficient production processes occur rarely, it requires to inspect and monitor the time between occurrence of these events (TBE). The exponential and gamma distributions are commonly used models for time between events (TBE) data. In this article, a new monitoring scheme

This article deals with the monitoring of censored data using the cumulative sum (CUSUM) control charts for Weibull lifetimes under type‐I censoring. To develop an efficient CUSUM structure for censored data, we use the conditional expected value (CEV) and conditional median (CM) approaches. In particular, we focus on the detection of shifts in the mean of Weibull lifetimes assuming censored data.

It is well known that performance of control scheme in phase II of statistical process control depends on the estimators utilized in phase I. Sometimes, outliers may be present in the data, which could seriously impact the performance of the estimators. In some practical situations, generalized linear models (GLMs) are used to model a wide class of response variables. This study deals with the robust

The number of security failure discovered and disclosed publicly are increasing at a pace like never before. Wherein, a small fraction of vulnerabilities encountered in the operational phase are exploited in the wild. It is difficult to find vulnerabilities during the early stages of software development cycle, as security aspects are often not known adequately. To counter these security implications

In this study, a three‐step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large‐scale rotating machinery in the presence of scarce failure data. In the feature extraction step, eight time‐domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination

Monitoring stochastic processes with control charts is the main field of application in statistical process control. For a Poisson hidden Markov model (HMM) as the underlying process, we investigate a Shewhart individuals chart, an ordinary Cumulative Sum (CUSUM) chart, and two different types of log‐likelihood ratio (log‐LR) CUSUM charts. We evaluate and compare the charts' performance by their average

This paper studies control charts based on the BerG (which is a sum of Bernoulli and geometric random variables) process to deal with the cases of equidispersion, overdispersion, underdispersion, or zero inflation (or deflation). Its probability distribution function can be expressed in terms of the mean parameter and its cumulative distribution has a closed form, thus the construction of an X ¯ control

Acceptance sampling plan is widely used for the inspections of incoming or outgoing products to provide the producer and the consumer a general benchmark for lot sentencing. To reduce the inspection cost, the multiple‐dependent‐state (MDS) plan that takes account of the preceding lots' quality history into the current lot disposition has been proposed. However, recent studies overlook the fact that

In this paper, a novel integrated tool for failure mode and effects analysis (FMEA), opportunely named Risk Failure Deployment (RFD), which is able to evaluate the most critical failure modes and provide analyst with a practical and step‐by‐step guidance by selecting the most effective corrective actions for removal/mitigation process of root causes, is presented. Thanks to the modification of the

In this article, we introduce a new lifetime distribution with increasing and bathtub‐shaped failure rates. Some statistical properties of the proposed distribution are studied. We use the method of maximum likelihood for estimating the model parameters and reliability characteristics and discuss the interval estimates using asymptotic confidence intervals and bootstrap confidence intervals on one

In recent years, several techniques based on control charts have been developed for the simultaneous monitoring of the time interval T and the amplitude X of events, known as time‐between‐events‐and‐amplitude (TBEA) charts. However, the vast majority of the existing works have some limitations. First, they usually focus on statistics based on the ratio X T , and second, they only investigate a reduced

To further study the law of strength degradation, the residual strength degradation model is established based on the definition of fatigue damage, considering the interaction of various uncertain factors and time factors in service environment. Combined with equivalent damage model, a nonlinear cumulative damage model is proposed, which takes the interaction among loading loads into account and improves

Wheelsets absorb a significant part of the maintenance budget of any train operating company. Although wheel wear has been an extensively discussed topic in the literature, wear rates are very rarely characterized by using degradation data in a real‐world case study aimed at identifying optimal maintenance policies including both degradation and recovery modeling. Furthermore, wheel defects, which

Human error is one of the largest contributing factors to unsafe operation and accidents in high‐speed train operation. As a well‐known second‐generation human reliability analysis (HRA) technique, the cognitive reliability and error analysis method (CREAM) has been introduced to address HRA problems in various fields. Nevertheless, current CREAM models are insufficient to deal with the HRA problem

Evidential network is considered to have superiority in conducting reliability analysis for complex engineering systems with epistemic uncertainty. However, existing methods tend to result in combinational explosion when multistate systems are involved in the reliability analysis, which means the reliability analysis cost increases exponentially with the number of components and that of functioning

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root‐cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing

Analytical formulae for computing the average time to signal (ATS) value for the exponentially weighted moving average (EWMA) cumulative count of conforming (CCC) chart using the Markov chain procedure are derived, as the performance of this chart is currently studied using simulation in the literature. Additionally, the variable sampling interval (VSI) EWMA CCC chart for monitoring the cumulative

Monitoring surgical outcomes is of paramount importance especially by accounting for health conditions of the patients prior to surgery. However, the problem arises as the effect of some covariates is pronounced but cannot be measured. In this paper, in order to deal with the effect of measured and unmeasured (categorical) covariates simultaneously, a class of survival analysis regression models called

Accelerated degradation test (ADT) is generally used to accelerate degradation processes in products to estimate their lifespan and to assess their reliability in a short period of time. How to perform the failure mechanism consistency test is crucial in the application of the ADT method. Existing failure mechanism consistency test methods assume that degradation rates among individual products are

In the literature, many control charts monitoring the median is designed under a perfect condition that there is no measurement error. This may make the practitioners confusing to apply these control charts because the measurement error is the true problem in practice. In this paper, we consider the effect of measurement error on the performance of the exponentially weighted moving average (EWMA) control

In this article, we propose “Alternated Charting Statistic‐Modified Group Runs” (ACS‐MGR) control chart for monitoring mean vectors of bivariate and trivariate processes, as an improvement over the ACS chart. This chart is obtained by combining ACS chart with the MGR chart, and it is verified that ACS‐MGR chart significantly reduces “Average Time to Signal” (ATS) as compared with ACS chart, when the

Recently, a new double progressive mean (DPM) control chart has been proposed in the literature of statistical process control. In the said proposal, an important term is missing in the variance expression of the DPM statistic that affects the detection ability of the proposed chart. In this study, we have derived and provided the correct version of the said variance along with its corresponding control

This paper proposes a new space–time cumulative sum (CUSUM) approach for detecting changes in spatially distributed Poisson count data subject to linear drifts. We develop expressions for the likelihood ratio test monitoring statistics and the change point estimators. The effectiveness of the proposed monitoring approach in detecting and identifying trend‐type shifts is studied by simulation under

Exponentially distributed data are commonly encountered in high‐quality processes. Control charts dedicated to the univariate exponential distribution have been extensively studied by many researchers. In this paper, we investigate a multivariate cumulative sum (MCUSUM) control chart for monitoring Gumbel's bivariate exponential (GBE) data. Some tables are provided to determine the optimal design parameters

In this paper, risk modeling was conducted based on the defined risk elements of a conceptual risk framework. This model allows for the estimation of a variety of risks, including human error probability, operational risk, financial risk, technological risk, commercial risk, health risk, and social and environmental risks. Bayesian network (BN) structure learning techniques were used to determine the

Performance management of operating rooms is of great importance in hospital management. From the patients' point of view, operating room capacity management ensures available capacity when needed and thus ensuring short (er) waiting time. From the hospital point of view, efficiently and effectively managing these expensive resources is a major concern. Because there are too many performance indicators

This paper can be considered as an extension of the work of Tran et al (for monitoring compositional data using a multivariate exponentially weighted moving average MEWMA‐compositional data [CoDa] chart) by taking into account potential measurement errors that are known to highly affect production processes. A linearly covariate error model with a constant error variance is used to study the impact