In-control State Research Articles

Metabolic trajectories of diabetic ketoacidosis onset described by breath analysis.

This feasibility study aimed to investigate the use of exhaled breath analysis to capture and quantify relative changes of metabolites during resolution of acute diabetic ketoacidosis under insulin and rehydration therapy. Breath analysis was conducted on 30 patients of which 5 with DKA. They inflated Nalophan bags, and their metabolic content was subsequently interrogated by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). SESI-HRMS analysis showed that acetone, pyruvate, and acetoacetate, which are well known to be altered in DKA, were readily detectable in breath of participants with DKA. In addition, a total of 665 mass spectral features were found to significantly correlate with base excess and prompt metabolic trajectories toward an in-control state as they progress toward homeostasis. This study provides proof-of-principle for using exhaled breath analysis in a real ICU setting for DKA monitoring. This non-invasive new technology provides new insights and a more comprehensive overview of the effect of insulin and rehydration during DKA treatment.

A variable sample size side-sensitive synthetic coefficient of variation chart

A major challenge for control charts monitoring the coefficient of variation is to quickly detect shifts in this parameter, so that assignable cause(s) can be quickly removed and the process can operate in an in-control state with a stable coefficient of variation. This is especially so when there are constraints in the sample size. One proposed strategy is to vary the sample size according to the most recent information. However, a side-sensitive synthetic chart monitoring the coefficient of variation with variable sample size is not available. This paper contributes to the literature by developing a variable sample size side-sensitive synthetic chart for the coefficient of variation. The main contributions are in terms of illustrating the operations of the chart, deriving the formulae to evaluate its performance and developing the algorithms to optimize its performance. Comparisons with current charts show that the proposed chart outperforms all existing synthetic-type charts monitoring the coefficient of variation. The proposed chart also outperforms the variable sample size coefficient of variation chart for all shift sizes. In addition, it outperforms the variable sample size run sum and variable sample size Exponentially Weighted Moving Average charts monitoring the coefficient of variation for moderate and large shift sizes.

An Integrated Production and Inspection Model With a Heuristic Product Inspection Policy When Inspection Errors Exist

This study proposes a heuristic inspection policy to address the existence of type I and II inspection errors. The deterioration process often shifts from an in-control state to an out-of-control state toward the end of a production lot, producing more nonconforming products. As a result, if the initial (late-produced) products in a production lot are chosen for inspection, the products identified as nonconforming (conforming) may need to be inspected repeatedly. The decision regarding the demarcation point between initial and late-produced products, their respective inspection rounds, and the production lot size must be made simultaneously to minimize the expected total cost per conforming product. This paper provides numerical examples to explore the effect of inspection errors on the optimal production lot size, inspection policy, and the associated cost.

Risk Analysis for Quality Control Part 2: Theoretical Foundations for Risk Analysis.

Risk analysis can be used to determine control limits for quality control (QC). The Parvin model is the most commonly used method for risk analysis; however; the Parvin model rests on assumptions that have been shown to produce paradoxical results and to underestimate risk. There is a need for an improved framework for risk analysis. We developed a dynamic model (Markov Reward Model) to analyze the long-term behavior of an assay under the influence of a QC monitoring system. The model is flexible and accounts for different patterns of assay behavior (shift frequency, shift distribution) and the impact of error on patient outcomes. The model determines the distribution of undetected reported errors and the frequency of false-positive laboratory results as a function of QC settings. The model accounts for the competing risks (false detections, shifts in the mean) that cause an assay to move from an in-control state to an out-of-control state. The model provides a tradeoff curve that expresses the cost to prevent an unacceptable reported result in terms of laboratory cost (false-positive QC). The model can be used to optimize settings of a particular QC method or to compare the performance of different methods. We developed a method to evaluate that determines the cost to reduce the risk to patients (reported results with unacceptable errors) in terms of laboratory costs (false-positive QC).

Effect of contaminated phase I data on the phase II – EWMA control chart performance under non-normality

Estimation of unknown process parameters is critical in statistical process control/monitoring (SPC/M). Retrospective data is usually used to identify the process in-control state behavior in terms of estimating the process parameters. Practitioners analyze the retrospective data in order to handle out-of-control observations before estimating the parameters. However, chances to overlook some out-of-control observations do exist; in which case, the Phase I data is said to be contaminated. In this study, we aim to assess the impact of having contaminated retrospective data on the performance of the EWMA chart; assuming non-normal processes. Two distributions are considered; the t-distribution to represent a symmetric heavy-tailed distribution with two contamination scenarios, and the Gamma distribution to represent a skewed one. A weighted variance EWMA chart is used with the Gamma distribution to account for the skewness. Our results revealed that under the t-distribution a contamination with respect to the mean is more serious than a contamination with respect to the degrees of freedom. Under the gamma distribution, the estimation effect generally and contamination effect specifically are more serious on smaller shape parameter values (heavily skewed).

Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing

For an increasing number of applications, the quality and the stability of manufacturing processes can be determined via image and video-image data analysis and new techniques are required to extract and synthesize the relevant information content enclosed in big sensor data to draw conclusions about the process and the final part quality. This paper focuses on video image data where the phenomena under study is captured by a point process whose spatial signature is of interest. A novel approach is proposed which combines spatial data modeling via Ripley’s K-function with Functional Analysis of Variance (FANOVA), i.e., Analysis of Variance on Functional data. The K-function allows to synthesize the spatial pattern information in a function while preserving the capability to capture changes in the process behavior. The method is applicable to quantities and phenomena that can be represented as clusters, or clouds, of spatial points evolving over time. In our case, the motivating case study regards the analysis of spatter ejections caused by the laser-material interaction in Additive Manufacturing via Laser Powder Bed Fusion (L-PBF). The spatial spread of spatters, captured in the form of point particles through in-situ high speed machine vision, can be used as a proxy to select the best conditions to avoid defects (pores) in the manufactured part. The proposed approach is shown to be not only an efficient way to translate the high-dimensional video image data into a lower dimensional format (the K-function curves), but also more effective than benchmark methods in detecting departures from a stable and in-control state.

An integrated Maintenance and Statistical Process Control Model for a Deteriorating Production Process

This study presents an integrated model of maintenance and statistical process control (SPC) for a production process with three states (in-control, out-of-control, and failure). The process is operational in both in-control and out-of-control states. However, the quality of the process's output deteriorates in an out-of-control state, and the process completely stops in the failure state. If the process enters the out-of-control state and no maintenance action is performed, it eventually enters the failure state. While the failure state is observable, judgment is made about the operational states of the process, i.e., in-control and out-of-control states, based on the quality of the produced items using a control chart. When the process shifts to an out-of-control state, and the control chart identifies this transition, minor repair applies, restoring the process to an in-control state, and continuing the production cycle. However, at most N minor repairs can be conducted during a production cycle. When the process moves to a failure state, the production cycle terminates, and a major repair is conducted. It is assumed after performing each minor repair, the life of the production machine decreases stochastically. In the paper, a mathematical model is developed to determine the optimal sample size, sampling interval, the limits of the control chart, and the maximum number of minor repairs during a process cycle. The model is developed without restrictive assumptions about (1) process failure mechanism (PFM), (2) stochastic process representing the imperfect effect of maintenance, and (3) control charts employed to monitor the process. Hence, from three aspects, the proposed model has a general structure leading to widespread applicability of the model for different working conditions. As a special case, we demonstrated the application of the model considering a Weibull distribution to represent the PFM, a stochastic geometric process (GP) to describe the imperfect effect of maintenance, and X-bar and chi-square control charts to monitor the process. Given the above conditions, numerical examples and sensitivity analyses are conducted. Furthermore, using the properties of GP and considering exponential distribution for PFM, closed-form formulas are derived. The model is optimized using a full enumeration algorithm.

OPTIMAL PRODUCTION POLICY FOR IMPERFECT ITEMS UNDER LEARNING AND FORGETTING ENVIRONMENT

In an imperfect production process with the impact of inflation, this study studies an economic production quantity (EPQ) model with exponential demand. The machine may go from an in-control state to an out-of-control state throughout a lengthy procedure. As a result, the system produces imperfect items. Shortages are permitted and partially backlogged with an exponential rate. We developed the model in both fuzzy and crisp sense. It is well known that as the number of cycle increase the percentage of defective items decrease due to the learning and forgetting effect of the past. Similar in the case of set up cost which also decreases due to the learning and forgetting effect.

Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart. The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

Statistical process monitoring, maintenance policy, and production have commonly been studied separately in the literature, whereas their integration can lead to more favorable conditions for the entire production system. Among all studies on integrated models, the underlying process is assumed to generate independent data. However, there are practical examples in which this assumption is violated because of the extraction of correlation patterns. Autocorrelation causes numerous false alarms when the process is in the in-control state or makes the traditional control charts react slowly to the detection of an out-of-control state. The auto-regressive moving average (ARMA) control chart is selected as an effective tool for monitoring autocorrelated data. Therefore, an integrated model subject to some constraints is proposed to determine the optimal decision variables of the ARMA control chart, economic production quantity, and maintenance policy in the presence of autocorrelated data. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to search for optimal decision variables. An industrial example and some comparisons are provided for more investigations. Moreover, sensitivity analysis is carried out to study the effects of model parameters on the solution of the economic-statistical design.

Change point estimation under a copula-based Markov chain model for binomial time series

Estimation of a change point is a classical statistical problem in sequential analysis and process control. For binomial time series, the existing maximum likelihood estimators (MLEs) for a change point are limited to independent observations. If the independence assumption is violated, the MLEs substantially lose their efficiency, and a likelihood function provides a poor fit to the data. A novel change point estimator is proposed under a copula-based Markov chain model for serially dependent observations. The main novelty is the adaptation of a three-state copula model, consisting of the in-control state, out-of-control state, and transition state. Under this model, a MLE is proposed with the aid of profile likelihood. A parametric bootstrap method is adopted to compute a confidence set for the unknown change point. The simulation studies show that the proposed MLE is more efficient than the existing estimators when serial dependence in observations are specified by the model. The proposed method is illustrated by the jewelry manufacturing data, where the proposed model gives an improved fit.

Coordination of the decisions associated with maintenance, quality control and production in imperfect deteriorating production systems

In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.

Work performance dependent screening process under preservation technology in an imperfect production system

ABSTRACT This study presents an EPQ inventory model which provides some imperfect quality products. Initially, due to the existence of all fresh sources, the system remains in good condition, i.e., in-control state. But, after a certain time of production, this system must produce some defective items with the perfect ones, i.e., the system goes into an out-of-control state. Here, the transition time from one state to another state has been considered as a random variable. Besides that, a generalized screening process has been proposed which is based on the work performance of a worker that also depends on time, this is the key contribution of this study. Also, we have considered a preservation investment cost in the manufacturing system to control the defective rate. A generalized stochastic demand function has been proposed. Finally, numerical implications, managerial implications, and conclusions are made for the justification of my proposed model.

Optimal Condition-Based Maintenance Strategy via an Availability-Cost Hybrid Factor for a Single-Unit System During a Two-Stage Failure Process

This paper develops an optimal condition-based maintenance (CBM) strategy for a single-unit system during two-stage failure, a process that includes a normal stage and delay-time stage. Both stages are divided into the in-control state and out-of-control state. In the in-control state, the items are always produced with 100% quality, whereas in the out-of-control state, the item quality deteriorates, and minor repairs are arranged accordingly to fix this problem. This causes the optimal CBM strategy for system production to exhibit four different scenarios, where different calculations are carried out using renewal reward theory to determine the system profit. Then, the system profit is optimized by an availability-cost hybrid factor that balances the “cost per unit time” and “availability”. Finally, to investigate the effects of different decision objectives on the optimization results, a sensitivity study of the cost parameter and availability-cost weight factor is conducted on the optimized results through numerical simulations. According to the simulation results, this availability-cost hybrid factor, as well as the “cost per unit time” and “availability” factors, become less sensitive when it exceeds 0.6.

A novel semisupervised classification framework for coupling faults in hot rolling mill process

Compared with single fault, the occurrence and composition of coupling faults have more uncertainties and diversities, which make fault classification a challenging topic in academic research and industrial application areas. In this paper, the classification problems of coupling faults are addressed from a new perspective, which will provide diagnostic decisions for online operators to take immediate remedial measures to bring the abnormal operation back to an incontrol state. Specifically, the main innovations are: (1) a semisupervised classification scheme for coupling faults is first proposed, which combines adaptive classification with multi-task feature selection; (2) number of classifications can be learned adaptively and automatically; (3) common and specific features among single and the associated coupling faults can be captured, which are crucial for improving classification performance. A case study on hot rolling mill process is finally given to validate the effectiveness of the proposed scheme, and several competitive methods are employed to carry out the classification process. It can be observed that the obtained classification results for two different cases are more successful than the traditional methods.

Impact of carbon emission on imperfect production inventory system with advance payment base free transportation

Most of the works in EPQ model considered that retailer’s purchasing cost of an order should be paid to the manufacturer at the time of its delivering. In the real world situations, few manufacturers may be expect to receive full or a fraction of the purchasing cost in advance and sometimes allow to prepayment into several equal instalments. Also, in classical inventory models it is assumed that no defective items are produced, but real production process may shift from in-control state to out-of-control state due to occurrence of some assignable causes, which results in the quality loss of items. To make the study more realistic, we develop an imperfect production inventory model that incorporates random carbon emissions under consecutive prepayments. Also, production processes are assumed to be imperfect, so they can produce some defective items and some portion of them are reworked in the same cycle. In addition, it is assumed that manufacturer offers an advanced payment based free transportation to the retailer. Carbon emissions are associated with the decisions of production and transportation from manufacturer to retailer. A carbon emission tax is need to pay by the manufacturer due to the environmental regulations. Numerical examples illustrate the proposed models and sensitivity analysis provides some managerial insights for managers.

Joint Optimization of Production and Maintenance Using Monte Carlo Method and Metaheuristic Algorithms

In the competitive business environment, manufacturers are seeking strategies to improve the product quality and the system reliability while reducing the costs. This paper addresses the problem of finding the optimal production and maintenance schedules for a deteriorating manufacturing system with the objective of minimizing the expected cost per unit time. The system consists of one machine which deteriorates with time and it may shift from an in-control state to an out-of-control state with a larger proportion of imperfect products. In addition, the hedging point policy is applied as the production-inventory control policy. The predictive maintenance is performed based on process inspections, whose sampling intervals are variable. To deal with the proposed problem, we build a joint model that coordinates production, inventory, maintenance, and quality control with 16 scenarios. Then we propose a novel approach speeding up the Monte Carlo simulation to calculate the objective function. Thus it becomes feasible to optimize the objective function by metaheuristic algorithms. Then we use the genetic algorithm to illustrate its feasibility. Next, the advantage of the proposed approach is verified by comparing with the traditional integral method. Finally, a sensitivity analysis with an orthogonal experiment is conducted to help managers find the factors with the most significant effect on the cost.

Effect of Energy and Failure Rate in a Multi-Item Smart Production System

To form a smart production system, the effect of energy and machines’ failure rate plays an important role. The main issue is to make a smart production system for complex products that the system may produce several defective items during a long-run production process with an unusual amount of energy consumption. The aim of the model is to obtain the optimum amount of smart lot, the production rate, and the failure rate under the effect of energy. This study contains a multi-item economic imperfect production lot size energy model considering a failure rate as a system design variable under a budget and a space constraint. The model assumes an inspection cost to ensure product’s quality under perfect energy consumption. Failure rate and smart production rate dependent development cost under energy consumption are considered, i.e., lower values of failure rate give higher values of development cost and vice versa under the effect of proper utilization of energy. The manufacturing system moves from in-control state to out-of-control state at a random time. The theory of nonlinear optimization (Kuhn–Tucker method) is employed to solve the model. There is a lemma to obtain the global optimal solution for the model. Two numerical examples, graphical representations, and sensitivity analysis of key parameters are given to illustrate the model.

Multivariate coefficient of variation control charts in phase I of SPC

Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV.

Coordination of pricing, inventory, and production reliability decisions in deteriorating product supply chains

In this article, we study a two-level supply chain model for deteriorating items, in which the supplier’s production system is unreliable and the retailer’s demand is price-sensitive. The supplier’s production line may randomly shift from the in-control state to the out-of-control state. When the production line is in the out-of-control state, a proportion of the produced products will have bad quality. To mitigate the out-of-control risks, the supplier can improve the production line reliability by investing in high-quality machines, highly skilled workers, or advanced maintenance technologies. We start with the study of pricing and inventory problems concerning endogenous reliability in the integrated and decentralised scenario. To better illustrate the proposed models, two applicable algorithms are designed to determine the optimal production reliability, ordering quantity, and prices. Then, a cooperative reliability investment and revenue-sharing contract is proposed to coordinate the supply chain. Numerical examples and sensitivity analysis of the equilibrium strategies and coordinating results on key system parameters (e.g. deterioration rate, production rate, etc.) are given to verify the effectiveness of the contract, and meanwhile get some managerial insights.