A New Multivariate Process Capability Index Under Both Unilateral and Bilateral Quality Characteristics

Process capability indices (PCIs) are used in statistical process control to evaluate the capability of the processes in satisfying the customer's needs. In the past two decades varieties of PCI are introduced by researchers to analyze the process capability with univariate or multivariate quality characteristics. To the best of our knowledge, most famous multivariate capability indices are proposed when the quality characteristics have both upper and lower specification limits. These indices are incapable to assess the multivariate processes capability with unilateral specification. In this article, we propose a new multivariate PCI to analyze the processes with one or more unilateral specification limits. This new index also accounts for all problems in the best PCIs of the literature. The performance of the proposed index is evaluated by real cases under different situations. The results show that the proposed index performs satisfactorily in all cases considered. Copyright © 2012 John Wiley & Sons, Ltd.     

Diagnosing the variance shifts signal in multivariate process control using ensemble classifiers

Statistical process control charts have been successfully used to monitor process stability in various industries. The need to simultaneously monitor two or more quality characteristics has led to the prevalent adoption of multivariate control charts. However, out-of-control signals in multivariate control charts may be caused by one or more variables, or a set of variables. Therefore, effective quality control requires not only the rapid detection of process fluctuations, but also the correct identification of the variable(s) responsible for those changes. This study approaches the diagnosis of out-of-control signals as a classification task and proposes a support vector machine (SVM)-based ensemble classification model focused on variance shifts in multivariate processes. We address the issues of data diversity and ensemble method in constructing an ensemble model. Simulation results demonstrate the effectiveness of the proposed ensemble classification model in identifying the source of variance change. The proposed method clearly outperforms single classifiers as well as other comparable models including bagging and boosting. The results also reveal that the use of extracted features as input vectors for SVM provides better classification performance than the use of raw data. The proposed SVM-based ensemble classification system provides a reliable tool for the interpretation of out-of-control signals in multivariate process control.     

低温热水地板辐射采暖事故分析与经验总结

针对新建小区地板辐射采暖系统漏水事故,从施工图设计、安装施工、工程验收、采暖试运行到正式运行各个环节对事故进行全方面的分析研究。分析结果表明,地板辐射采暖系统对施工质量的要求很高,系统爆管、漏水的原因主要来自施工过程,按照《地面辐射供暖技术规程》标准选择的管材不存在漏水的问题,施工和验收是质量控制的重点工作。     

The use of LS–PLS for improved understanding, monitoring and prediction of cheese processing

In this paper we demonstrate both a design strategy and a set of analysis techniques for a designed experiment from an industrial process (cheese making) with multivariate responses (sensory data). The design strategy uses two-level factorial design for the factors that can be controlled, and blocking on the raw material to cover other non-designed variation in the raw material. We measure both the raw materials and on several points during the process with FT-IR spectroscopy. The methods of analysis complement each other to give more understanding and better modelling. The 50–50 MANOVA method provides multivariate analysis of variance to test for significance of effects for the design variables. Ordinary PLS2 analysis gives an overview of the data and generates hypotheses about relations. Finally, the orthogonal LS–PLS method is extended to multivariate responses and used to identify the source of the observed block effect and to build models that can be used for statistical process control at several points in the process. In these models, the information at one point is corrected for information that has already been described elsewhere.     

A Distribution-Free Multivariate Control Chart

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This article develops a new nonparametric methodology for monitoring location parameters when only a small reference dataset is available. The key idea is to construct a series of conditionally distribution-free test statistics in the sense that their distributions are free of the underlying distribution given the empirical distribution functions. The conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point can be guaranteed to attain a specified false alarm rate. The success of the proposed method lies in the use of data-dependent control limits, which are determined based on the observations online rather than decided before monitoring. Our theoretical and numerical studies show that the proposed control chart is able to deliver satisfactory in-control run-length performance for any distributions with any dimension. It is also very efficient in detecting multivariate process shifts when the process distribution is heavy-tailed or skewed. Supplementary materials for this article are available online.     

Monitoring Correlated Profile and Multivariate Quality Characteristics

Monitoring multivariate quality characteristics is very common in production and service environment. Therefore, many control charts have been suggested by authors for monitoring multivariate processes. In another side, profile monitoring is a new approach in the area of statistical process control. In this approach, the quality of a product or a process is characterized by a relation between one response variable and one or more independent variables. In practice, sometimes the quality of a product or a process is represented by a correlated profile and multivariate quality characteristics. To the best of our knowledge, there is no method for monitoring this type of quality characteristics. Note that monitoring correlated profile and multivariate quality characteristics separately leads to misleading results. In this article, we specifically focus on correlated simple linear profile and multivariate normal quality characteristics and propose a method using multivariate exponentially weighted moving average control chart to monitor the correlated profile and multivariate quality characteristics simultaneously. The performance of the proposed control chart is evaluated by simulation studies in terms of average run length criterion. Finally, the proposed method is applied to a real case in the electronics industry. Copyright © 2013 John Wiley & Sons, Ltd.     

An upside-down normal loss function-based method for quality improvement

Traditional measures of process quality do not offer much information on how much better or worse a process is when finding optimal settings of a given problem. The upside-down normal loss function (UDNLF) is a weighted loss function that provides a more reasonable risk assessment to the losses of being off-target in product engineering research. The UDNLF can be used in process design and optimization to accurately reflect and quantify the losses associated with the process in a way which minimizes the expected loss of the upside-down normal (UDN). The function has a scale parameter which can be adjusted by the practitioners to account for the actual percentage of materials failing to work at specification limits. In this article, the ‘target is best’ case is addressed to estimate the expected loss of UDN due to variation from target in the robust process design and response surface modelling context. An approach is proposed to find the control factor settings of a system by directly minimizing the expected loss. The procedure and its merits are illustrated through an example.     

预应力混凝土管桩生产企业质量管理

由于预应力混凝土管桩是建筑工程中使用的一种十分重要的建筑材料，其产品质量涉及原材料、生产过程参数控制等诸多方面，根据对管桩常见质量问题的分析，提出质量控制的关键点。     

Multivariate charting techniques: a review and a line‐column approach

The primary objective of multivariate statistical process control is to monitor the related process quality characteristics over time and identify the assignable causes affecting the process using multivariate control charts. When an out‐of‐control signal is obtained from the chart, it is imperative to be able to detect the component variables that have gone out‐of‐control. In this paper we propose a new charting procedure for T², multivariate exponentially weighted moving average and multivariate cumulative sum control charts. The proposed charts will facilitate in identification of the source of out‐of‐control signal and are simple, economical and easier to implement. Copyright © 2009 John Wiley & Sons, Ltd.     

On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines

In multivariate statistical process control (MSPC), most multivariate control charts can effectively monitor anomalies based on overall statistic, however, they cannot provide guidelines to classify the source(s) of out-of-control signals. Classifying the source(s) of process mean shifts is critical for quality control in multivariate manufacturing process since the immediate identification of them can greatly help quality engineer to narrow down the set of possible root causes and take corrective actions. This study presents an improved particle swarm optimisation with simulated annealing-based selective multiclass support vector machines ensemble (PS-SVME) approach, in which some selective multiclass SVMs are jointly used for classifying the source(s) of process mean shifts in multivariate control charts. The performance of the proposed PS-SVME approach is evaluated by computing its classification accuracy. Simulation experiments are conducted and a real application is illustrated to validate the effectiveness of the developed approach. The analysis results indicate that the developed PS-SVME approach can perform effectively for classifying the source(s) of process mean shifts.     

Kernel distance-based robust support vector methods and its application in developing a robust K-chart

Traditional statistical process control (SPC) techniques are not applicable in many process industries due to autocorrelation among data. In addition, most conventional charts are based on the assumption that quality characteristics follow a multivariate normality assumption. Therefore, the reduction in process variability obtained through the use of SPC techniques has not been realized in the industries. However, this may not be reasonable in many real-world problems and its extension poses serious limitations. Hence, it is not only desirable, but also inevitable to have some techniques that can serve the same purpose as SPC control charts used for correlated parameters. In this paper, a robust support vector method drawn from statistical learning theory was applied to develop a multivariate control chart based on kernel distance, which is a measure of the distance between the centre of a class and the sample to be monitored. The proposed robust chart takes advantage of information extracted from in-control preliminary samples. A robust support vector method-based chart aims to solve the over fitting problems when outliers exist in the training data set. The robust support vector method makes the decision function less sensitive towards the noise and outliers. The performance of the robust chart is tested on the problem taken from the literature and the results verify the effectiveness of the chart and validate that the robust chart is better than the conventional charts when the distribution of the quality characteristics is not multivariate normal. Experiments for the problem undertaken confirm the reduction in the number of support vectors and there is significant improvement in performance when compared with the standard support vector methods.     

System modelling for economic raw material selection

A system simulation method for economic raw material selection is presented, based on a realistic example. The total cost considered in raw materials selection is deemed to include the basic raw material cost, the extra manufacturing cost due to the selection of the particular raw material, and the final product quality cost. These costs are systematically simulated for three typical areas of interest: for the purchasing operation, for the manufacturing process and for the final product quality acceptance by the customer.     

连续混炼过程比能耗分析

将连续混炼过程的有效比能耗分为升温、混炼、脱挥和建压比能耗,并提出了计算各项的一种方法。用该方法对不同机型、不同规格、不同加工对象的连续混炼机的比能耗进行了分析。结果表明,在多数连续混炼过程中,升温比能耗在总有效比能耗中所占比例都较大,建压比能耗所占比例都很小;原料热物性是影响有效比能耗及其构成的根本因素;当原料和混炼产物质量确定时,连续混炼机的螺杆与机筒结构、螺杆直径、产量、混炼工艺等都会影响有效比能耗及其构成。     

One‐sided control chart based on support vector machines with differential evolution algorithm

The statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one‐sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non‐normal distribution cases. The ARL performance of the proposed chart is better than the distance‐based SVM chart. A real example is used to illustrate the application of the proposed control chart.     

Robust kernel distance multivariate control chart using support vector principles

It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false-negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process monitoring.     

Good pharmaceutical manufacturing practices

In order to assure the quality of medicines and to encourage the implementation of the international system for the certification of the quality of pharmaceuticals, it was deemed necessary to specify the conditions under which pharmaceutical establishments should operate to provide full assurance that their products are safe for public health.

The French Government Order of 1. October. 1985 therefore provides for the establishement of new recommendations on good pharmaceutical manufacturing practices: “Bonnes Pratiques de Fabrication et de Production Pharmaceutiques: BPF 1985”.

Within the system of quality assurance, good pharmaceutical manufacturing practices represent that part which is concerned with manufacture.

Their implementation requires that the specification of the raw materials and packaging materials, the manufacturing and packaging processes and the control methods be defined and written beforehand, that the premises and equipment be adapted to the intended uses and that the staff have received appropriate training.

Good pharmaceutical manufacturing practices directly concern production departments and packing area, control laboratories, storage areas, purchasing departments, departments receiving raw materials and dispatching finished products. They also concern departments issuing instructions and written or computerised documents intended for the departments previously mentioned.

Although the collection of recommendations thus published constitutes a detailed document, the possibility of there being different methods for attaining the same objective is recognised.     

The effect of process parameters on audible acoustic emissions from high-shear granulation

Product quality in high-shear granulation is easily compromised by minor changes in raw material properties or process conditions. It is desired to develop a process analytical technology (PAT) that can monitor the process in real-time and provide feedback for quality control. In this work, the application of audible acoustic emissions (AAEs) as a PAT tool was investigated. A condenser microphone was placed at the top of the air exhaust on a PMA-10 high-shear granulator to collect AAEs for a design of experiment (DOE) varying impeller speed, total binder volume and spray rate. The results showed the 10 Hz total power spectral densities (TPSDs) between 20 and 250 Hz were significantly affected by the changes in process conditions. Impeller speed and spray rate were shown to have statistically significant effects on granulation wetting, and impeller speed and total binder volume were significant in terms of process end-point. The DOE results were confirmed by a multivariate PLS model of the TPSDs. The scores plot showed separation based on impeller speed in the first component and spray rate in the second component. The findings support the use of AAEs to monitor changes in process conditions in real-time and achieve consistent product quality.     

Robust control charts for percentiles based on location‐scale family of distributions

In this paper, robust control charts for percentiles based on location‐scale family of distributions are proposed. In the construction of control charts for percentiles, when the underlying distribution of the quality measurement is unknown, we study the problem of discriminating different possible candidate distributions in the location‐scale family of distributions and obtain control charts for percentiles which are insensitive to model mis‐specification. Two approaches, namely, the random data‐driven model selection approach and weighted modeling approach, are used to construct the robust control charts for percentiles in order to effectively monitor the manufacturing process. Monte Carlo simulation studies are conducted to evaluate the performance of the proposed robust control charts for various settings with different percentiles, false‐alarm rates, and sample sizes. These proposed procedures are compared in terms of the average run length. The proposed robust control charts are applied to real data sets for the illustration of robustness and usefulness.     

Good pharmaceutical manufacturing practices

Abstract

In order to assure the quality of medicines and to encourage the implementation of the international system for the certification of the quality of pharmaceuticals, it was deemed necessary to specify the conditions under which pharmaceutical establishments should operate to provide full assurance that their products are safe for public health.

The French Government Order of 1. October. 1985 therefore provides for the establishement of new recommendations on good pharmaceutical manufacturing practices: “Bonnes Pratiques de Fabrication et de Production Pharmaceutiques: BPF 1985”.

Within the system of quality assurance, good pharmaceutical manufacturing practices represent that part which is concerned with manufacture.

Their implementation requires that the specification of the raw materials and packaging materials, the manufacturing and packaging processes and the control methods be defined and written beforehand, that the premises and equipment be adapted to the intended uses and that the staff have received appropriate training.

Good pharmaceutical manufacturing practices directly concern production departments and packing area, control laboratories, storage areas, purchasing departments, departments receiving raw materials and dispatching finished products. They also concern departments issuing instructions and written or computerised documents intended for the departments previously mentioned.

Although the collection of recommendations thus published constitutes a detailed document, the possibility of there being different methods for attaining the same objective is recognised.