Profile monitoring is an active research area in statistical process control (SPC) because it has many important applications in manufacturing and other industries. Early profile monitoring methods often impose model assumptions that the mean profile function has a parametric form (e.g., linear), profile observations have a parametric distribution (e.g., normal), and within-profile observations are

The conventional CUSUM chart is usually designed for a known shift size against which the protection is required. In practice, the shift sizes are rarely known, but they may vary within a given interval. Given a range of the shift size, the weighted adaptive CUSUM (WAC) chart provides better protection than the CUSUM chart. In this paper, we propose two types of the WAC chart for monitoring different

Control charts are one of the most important tools of statistical process control. Many control charts have been developed for the monitoring of time between events. However, there are situations for which magnitude of events is also important and needs to be monitored along with the time between them. In this case, simultaneous monitoring of both time between and magnitude of events can be very efficient

Monitoring censored data is a challenging task and for this purpose, many control charts have been proposed in the literature using different methodologies. In particular, conditional expected value (CEV) and conditional median (CM) are commonly used to replace the censored observations for efficient monitoring. These central tendency measures do not take into account the variation of the data. Thus

Power generation engines in wastewater treatment plants (WWTPs) are critical and strategic assets as major elements of an effective energy management system. Therefore, water utilities seek a smart tool to optimise the maintenance program on overall cost and reliability of these assets. In the previous study, a reliability model with consideration of redundancy was developed to estimate failure modes

There is growing literature on new versions of “memory-type” control charts, where deceptively good zero-state average run-length (ARL) performance is misleading. Using steady-state run-length analysis in combination with the conditional expected delay (CED) metric, we show that the increasingly discussed progressive mean (PM) and homogeneously weighted moving average (HWMA) control charts should not

Predictive process monitoring aims to produce early warnings of unwanted events. We consider the use of the machine learning method extreme gradient boosting as the forecasting model in predictive monitoring. A tuning algorithm is proposed as the signaling method to produce a required false alarm rate. We demonstrate the procedure using a unique data set on mental health in the Netherlands. The goal

Physical phenomena are commonly modeled by numerical simulators. Such codes can take as input a high number of uncertain parameters and it is important to identify their influences on the outputs via a global sensitivity analysis (GSA). However, these codes can be time consuming, which prevents a GSA based on the classical Sobol' indices, requiring too many code simulations. To address this limitation

We develop Shiryaev–Roberts schemes based on signed sequential ranks to detect a persistent change in location of a continuous symmetric distribution with known median. The in-control properties of these schemes are distribution-free, hence they do not require a parametric specification of an underlying density function or the existence of any moments. Tables of control limits are provided. The out-of-control

The present article examines the performance of the Shiryaev–Roberts (SR) procedure for Markov-dependent count time series, using the Poisson INARCH(1) model as the representative data-generating count process. For the purpose of easier performance evaluation, a comparative analysis with existing cumulative sum (CUSUM) results from the literature is provided. In particular, the zero-state and the steady-state

Multivariate control charts are an effective mean to identify an out-of-control process in several (industrial or non-industrial) fields, where the quality depends on many related variables. However, the main shortcoming of these charts is that they fail to indicate which measured variable or variables has or have shifted. In order to address this issue, several alternative analytical approaches that

Additive manufacturing (AM) or 3D printing is an emerging manufacturing technology that plays a growing role in both industrial and consumer settings. However, security concerns of AM systems have been raised among researchers. In this paper, we present an online detection mechanism for the malicious attempts on AM systems, which taps into both audio and video signals collected during the printing

On modern ships, the quick development in data acquisition technologies is producing data-rich environments where variable measurements are continuously streamed and stored during navigation and thus can be naturally modelled as functional data or profiles. Then, both the CO 2 emissions (i.e. the quality characteristic of interest) and the variable profiles that have an impact on them (i.e. the covariates)

Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever-increasing complexity makes fault diagnostics and prognostics challenging for the system-level functions. A significant

In statistical process monitoring, statistical control tools are used to identify deviations from normal operating conditions. In many industrial processes, such as batch production processes, multiple process variables must be monitored as they play a key role in the quality of the final product. Several monitoring tools are found in the literature that deal with the case of multiple process variables

Multiple auxiliary information-based (MAIB) memory-type t charts are proposed with fixed and variable sampling intervals for an improved monitoring of the process mean, which include adaptive/nonadaptive cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) t charts. These control charts are constructed based on a unique uniformly minimum variance unbiased estimator of the process

Due to the importance of electrical grid reliability, analysis and evaluation of human error in the maintenance of electrical networks should be also considered seriously. The root causes of these errors must be identified and prioritized to plan for human error reduction. One of the objectives of the present study is to identify and predict these roots for power transmission maintenance groups from

Systems of components have a structure that plays an important role in determining how the reliability of the individual components relates to the reliability of the system. The system reliability can be computed from component reliabilities using results from basic probability theory in the simplest case with all of the components assumed to act independently of one another. However, in the case of

With the popularization of big data, an increasing number of discrete event data have been collected and recorded during system operations. These events are usually stored in the form of event logs, which contain rich information of system operations and have potential applications in fault diagnosis and failure prediction. In manufacturing processes, various levels of correlations exist among the

The move towards advanced manufacturing and Industry 4.0 is fed by increased demand for speeding up innovation, increasing flexibility, improving maintenance, and becoming more customized while saving on the total cost of operations. This is accompanied by increased dependence on virtual product and process development, data-driven processes, and product knowledge. Other characteristics, affecting

This paper proposes a time-varying failure mode and effect analysis (FMEA) method based on interval-valued spherical fuzzy theory, which not only improves the limitations in evaluating, weighting, and ranking but also considers the effect of time change. The process of distinguishing time changes enables the FMEA to have dynamic recognition capability, enabling it to identify critical failure modes

Recently, there has been a lot of interest in monitoring and identifying changes in dynamic networks, which has led to the development of a variety of monitoring methods. New methods are often designed for a specialized use-case and rarely compared to competing methods in a systematic fashion. In light of this, the use of simulation is proposed to compare the performance of network monitoring methods

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution

In some complicated cases, a multivariate process/product can be characterized better by a combined technical specification vector. A combined technical specification vector is referred to as the case that the quality of a process/product is evaluated by different continuous and discrete random variables at the same time. In this case that there is also the covariance condition, the vector could not

Artificial intelligence (AI) methods are becoming widespread, especially when data are not sufficient to build classical statistical models, as is the case for cyber risk management. However, when applied to regulated industries, such as energy, finance, and health, AI methods lack explainability. Authorities aimed at validating machine learning models in regulated fields will not consider black-box

For the systems that experience competing failure processes, an uncertain process–based degradation model is developed to describe the systems. The competing degradation process is composed of internal continuous degradation and external shocks, and the mutual dependence between them is considered. When the magnitude of the internal degradation exceeds the threshold, the soft failure occurs. While

In some statistical process control applications, the quality of a process is described by a linear relationship between the response variable(s) and the independent variable(s), which is called a linear profile. Process capability is a significant issue in statistical process control. The ability of a process to meet customer specifications or standards is measured by the process capability indices

The frequentist approach-based control charts (CCs) have been widely used to monitor the performance of the process. As compare to frequentist approach, the CCs based on the Bayesian approach can be used with a small phase-I dataset. This paper aims to propose a new homogenously weighted moving average CC based on the Bayesian theory. We have used two different loss functions, symmetric loss function

The terms “hotspots,” “blackspot,” or “high-risk” are normally used for an area on a road where the number of car accidents are far beyond the normal level and, more importantly, the causes of the incident always link to one or many specific reasons. Identifying these spots is helpful and may assist people in authority to implement corrective actions. To do the aforementioned, some decision-support

Traditional space-filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response

The CUmulative SUM (CUSUM) charts have sensitive nature against small and moderate shifts that occur in the process parameter(s). In this article, we propose the CUSUM and combined Shewhart-CUSUM charts for monitoring the process mean using the best linear unbiased estimator of the location parameter based on ordered double-ranked set sampling (RSS) scheme, where the CUSUM chart refers to the Crosier's

Many capacitated flow network papers treat reliability as a time-invariant property. However, network components degrade over time suggesting that methods to quantify the time-varying reliability of a network are appropriate. This paper proposes a method to compute the reliability of a multistate capacitated flow network in terms of multistate components characterized by a life distribution. The approach

In this paper, the moving average control chart is developed under time truncated test to monitor the number of failures when the lifetime of an item follows the Rayleigh and inverse Rayleigh distributions. The number of failures is detected when a planned number of items has been placed on a time truncated testing experiment. The average run lengths (ARL) for various parameter settings are used to

One-sided control charts for monitoring changes in the mean level are proposed in this paper. The proposed charts are given in the form of a homogeneously weighted moving average technique that provides efficient monitoring of small shifts in the mean level. The charts accumulate observations that are above the target (or mean value) and truncated the observations that are less than the target to the

Zaka et al provided a new distribution called the Weighted Power function distribution (WPFD), which has application in reliability engineering and survival analysis. They used different estimation methods to estimate the unknown parameters of WPFD and proved that modified maximum likelihood estimator (MMLE) is best to consider for the estimation of parameters. We have constructed the memoryless and

Mixture experiments involve developing a dedicated formulation for specific applications. We propose the weighted optimality criterion using the geometric mean as the objective function for the genetic algorithms. We generate a robust mixture design using genetic algorithms (GAs) of which the region of interest is an irregularly shaped polyhedral region formed by constraints on proportions of the mixture

This paper presents the Bayesian EWMA control chart under two different loss functions (i) squared error loss function (SELF) and (ii) linex loss function (LLF) in the presence of measurement error (ME). We take posterior and posterior predictive distribution under the conjugate prior. We used a linear covariate model in the existence of ME to evaluate the control chart. We also studied the effects

In this paper, X ¯ charts based on robust scale estimators (known as S n and Q n estimators) are proposed, and the performance of control charts based on median absolute deviation (MAD) is compared with those based on some alternatives to MAD, which do not need any location estimate, for normal, skewed, and heavily tailed distributions. MAD is often used as a substitute for standard deviation in constructing

Reliability of products is a key factor for successful businesses. In general, the existing monitoring schemes have poor performance as reliability data are often censored. Also, the products are manufactured in multistage processes where the outgoing quality gets affected by the previous stage quality. Besides this cascade property, historical data with outliers make the analysis even more complicated

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted

In this paper, we propose an auxiliary-information-based (AIB) double EWMA- t (AIB-DEWMA- t ) chart for monitoring the process mean. The DEWMA- t chart encompasses the EWMA- t and AIB-EWMA- t charts. The Monte Carlo simulations are used to compute the run length characteristics of the AIB-DEWMA- t chart. Based on detailed run length comparisons, it is found that the AIB-DEWMA- t chart may uniformly

The sensitivity of a monitoring scheme depends on many factors including the variance of the charting statistic which is very important in the computation of the control limits. This paper discusses the computation of the variance of the recently proposed hybrid homogeneously weighted moving average (HHWMA) X ¯ scheme which was based on an incorrect assumption. The correct variance is used to evaluate

Beta-distributed process outputs are common in manufacturing industry because they range from 0 to 1 based on inputs like yield. Under the normality assumption, Shewarts control charts and Hotelling's control charts based on the deviance residual have been applied to monitor the process mean of the beta-distributed process outputs. The normality assumption can be violated according to the shape of

Proper performance evaluation of subsea system components is of high significance for reliable operation and remote monitoring or the replacement of the components before the occurrence of any failure. As a part of subsea systems, subsea control system (SCS) plays a key role in accomplishing a reliable performance. Hence, achieving knowledge of the components’ failure rates is highly important in the

The traditional process monitoring techniques used to study high‐quality processes have several demerits, that is, high‐false alarm rate and poor detection, etc. A recent and promising idea to monitor such processes is the use of time‐between‐events (TBE) control charts. However, the available TBE control charts have been developed in a nonadaptive fashion assuming the Poisson process. There are many

Association rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text analytics of text records or social media. Here we use Compositional Data analysis (CoDa) techniques to

Software reliability literature consists of various change‐point‐based software reliability growth models and related release time problems. The primary assumption of the existing models is the existence of change‐point before software release time only. This does not look practical as the testing team becomes more proficient in detecting the faults due to their continuous involvement in software development

In many service and manufacturing industries, process monitoring involves multivariate data, instead of univariate data. In these situations, multivariate charts are employed for process monitoring. Very often when the mean vector shifts to an out‐of‐control situation, the exact shift size is unknown; hence, multivariate charts for monitoring a range of the mean shift sizes in the mean vector are adopted

An auxiliary information‐based (AIB) maximum exponentially weighted moving average (MaxEWMA) chart has been proposed to simultaneously monitor both increases and decreases in the process mean and/or variability, called the AIB‐MaxEWMA chart, which is superior to the existing MaxEWMA chart. In this paper, we propose the AIB maximum generally weighted moving average chart, called the AIB‐MaxGWMA chart

Hybrid control charts have become part of statistical process control (SPC) but still, need more emphasis. Researchers are developing charts for joint monitoring of process mean and variance shifts just like Max‐EWMA and their hybrid version using auxiliary information but are ignoring the effect of measurement error on the efficiency of charts. We propose maximum hybrid exponentially weighted moving

The Kaplan‐Meier estimator (KME) is a classical nonparametric reliability estimator for incomplete data. Although it has been widely used, its two drawbacks have not been addressed well in the literature: (a) as a staircase function, it actually has two reliability estimates for each failure observation, and (b) it is biased. This paper aims to address these two issues. First, an ideal reliability

Monitoring disturbances in process dispersion using control chart is mostly based on the assumption that the quality characteristic follows normal distribution, which is not the case in many real‐life situations. This paper proposes a set of new dispersion charts based on the homogeneously weighted moving average (HWMA) scheme, for efficient detection of shifts in process standard deviation (σ). These

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

A network provides powerful means of representing relationships between entities in complex physical, biological, cyber, and social systems. Any phenomena in those areas may be realized as changes in the structure of the associated networks. Hence, change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection, and health care monitoring

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