Fault detection for batch monitoring and discrete wavelet transforms

Batch operations are encountered in many industries and measurements are often recorded from automated sensors. It is important to determine whether an unknown batch is normal or unusual given a set of reference batches from normal operations. The measurements from a single batch can contain temporal readings that comprise a large time series. A discrete wavelet transformation (DWT) is applied to use the time and frequency localization of wavelets to extract features. A large number of coefficients can result and several methods to create summary features from the denoised coefficients obtained from DWT are compared. Also, a new summary feature incorporates information from denoised wavelet coefficients. The proposed study considers discrete wavelet‐decompositions combined with principal component analyses to summarize batch characteristics. Results were validated on an industry data set. Copyright © 2011 John Wiley & Sons, Ltd.     

Multivariate monitoring of batch process startup

A batch process is finite in duration and can be separated into two stages: startup and production. We develop a methodology to monitor a batch process during the startup stage to reduce the length of the startup stage. We focus on processes that are characterized by multiple process parameters and product characteristics. Because of the complex interdependencies characterizing the process parameters and product characteristics, it is more effective to evaluate them simultaneously. To address the multivariate nature of the process we use a multivariate statistical model: PLS (Projection to Latent Structures). PLS has been applied to several applications in statistical process monitoring. We present a new application of PLS to the startup stage of a batch process. Iterative adjustments made during startup in search of an acceptable production zone consume considerable amounts of material, labor and equipment time. We develop a monitoring procedure to reduce the time as well as the number of iterations and adjustments needed for startup. A PLS model is constructed, using baseline data, to characterize the relationship among process parameters during good production. The startup stage is monitored using the PLS characterization to determine if the process is consistent with good production. We illustrate the improved startup operations with an example from a batch process in filament extrusion, the application that motivates this work. Copyright © 2001 John Wiley & Sons, Ltd.     

一种保证预设质量和过程稳态的SPC方法

提出三种过程质量指数(PQI)的过程质量指数系统,基于过程质量指数的统计公差提供了一个过程质量要求和控制图设计之间的标准化界面.通过基于过程质量指数的统计公差带增加对x--R或x--s控制图中线的约束,建立一种保证预设质量和过程稳态的统计过程控制新方法.这不仅增强了控制图的功能,也为过程质量规划、统计公差和保证预设质量的SPC相关参数的并行设计提供了指导.     

The balance chart: A new SPC Concept

This paper outlines a new technique of statistical process control which goes a considerable way to resolving several existing problems. The technique described may be of particular value to automated control, small batch control and control of gauged processes. A new charting technique is described and compared with traditional control charts. The operation of the balance chart is outlined for attribute and variable processes and in precontrol mode. A graphical system for determining estimated Cp, Cpk and process mean values from limited process data is also included.     

Learning by Doing: A Series of Hands-on Projects for SPC

A series of simple hands-on class projects are used to illustrate statistical process control (SPC) tools such as run charts, histograms, probability plots, X and MR control charts, Xbar and R control charts, Xbar and s control charts, process capability analysis, and measurement systems analysis.     

A shewhart-like charting technique for high yield processes

A Shewhart-like charting technique is developed in this paper to overcome the difficulties the traditional control chart encounters in the control of processes with a very low fraction non-conforming. The technique uses the number of conforming items between two consecutive non-conforming ones to monitor the fraction non-conforming of a process, leading to a chart that is informative and easy to interpret. The approach discussed is especially suitable for real-time and automatic statistical quality control.     

An Approach to Build Process‐Oriented Basis for Causation‐Based Multivariate SPC and Process Capability Analysis

Although various multivariate process monitoring techniques have been developed, they do not diagnose the process for finding the root causes of irregularities during production. There have been recent studies on a new method that involves process‐oriented basis representation, which links the process variation to its causes, and thus helps in monitoring and diagnosing a process. However, all the studies done so far focused on its application. In this paper, a method is proposed to build the process‐oriented basis for a process irrespective of the number of variables characterizing it. Along with various other statistical techniques, factor analysis and cluster analysis, with customized distance function, are used in developing the method. The built in process‐oriented basis is further used for multivariate statistical process control and process capability analysis. Multivariate solder‐paste problem from electronics industry is used for illustration. Copyright © 2012 John Wiley & Sons, Ltd.     

多品种小批量生产的SPC应用研究

综述了多品种小批量生产方式的特点以及该生产方式下SPC(统计过程控制)的应用现状,指出常规控制图不适用于多品种小批量生产的问题,提出结合通用控制图、累积和(Cumulative-Sum,CUSUM)控制图和指数加权移动平均(Exponentially Weighted Moving-Averages,EWMA)控制图的解决方法,并总结出该SPC方法用于处理多品种小批量生产中微小偏差的步骤。     

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision‐making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright © 2000 John Wiley & Sons, Ltd.     

A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes

In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon overall statistics. But these charts do not relieve the need for pinpointing the source(s) of the out-of-control signals. In addition, these charts cannot provide more detailed process information, such as quantitative abnormal assessment values and visualisation of process changes, which would be very useful for quality practitioners to locate the assignable causes that give rise to the out-of-control situation. In this study, a hybrid learning-based model has been investigated for monitoring and diagnosing out-of-control signals in a bivariate process. In this model, a minimum quantisation error (MQE) chart based on the self-organization map (SOM) neural network (NN) was developed for monitoring process changes (i.e., mean shifts), and a selective NN ensemble approach (DPSOEN) was developed for diagnosing signals that are judged as out-of-control signals by MQE charts. The simulation results demonstrate that the proposed model outperforms the conventional multivariate control scheme in terms of average run length (ARL), and can accurately classify the source(s) of out-of-control signals. An extensive experiment is also carried out to examine the effects of six statistical features on the performance of DPSOEN.     

Statistical Monitoring of a Sealing Process by Means of Multivariate Accelerometer Data

A multivariate control charting procedure is applied to on-line seal quality evaluation of a packaging process by means of an accelerometer. Based on physical insight it is elucidated in a first step which information in the raw accelerometer data are relevant with respect to the goal of detecting bad seals. Next, a principal component analysis (PCA) based processing of this multivariate information is performed and the related Hotelling's T² and Q test statistics are calculated for further data representation. In a last step proper control charts based on these statistics are used as a process monitoring tool for on-line distinction between good and bad seals. The obtained results show that a correct monitoring of accelerometer signals can be a useful tool for the on-line detection of 'bad seals' in a packaging process.     

面向多品种、小批量制造环境的SPC实施模型的研究

 系统地分析和总结了适合于多品种、小批量制造环境的三类典型的ＳＰＣ方法,并对它们进行了分析与比较,进而提出适应于这一复杂环境的ＳＰＣ实施模型。     

An adaptive dimension reduction scheme for monitoring feedback‐controlled processes

Detecting dynamic mean shifts is particularly important in monitoring feedback‐controlled processes in which time‐varying shifts are usually observed. When multivariate control charts are being utilized, one way to improve performance is to reduce dimensions. However, it is difficult to identify and remove non‐informative variables statically in a process with dynamic shifts, as the contribution of each variable changes continuously over time. In this paper, we propose an adaptive dimension reduction scheme that aims to reduce dimensions of multivariate control charts through online variable evaluation and selection. The resulting chart is expected to keep only informative variables and hence maximize the sensitivity of control charts. Specifically, two sets of projection matrices are presented and dimension reduction is achieved via projecting process vectors into a low‐dimensional space. Although developed based on feedback‐controlled processes, the proposed scheme can be easily extended to monitor general multivariate applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Generalized likelihood ratio control charts for high-purity (high-quality) processes

Generalized likelihood ratio (GLR) control charts are useful for tailor-made monitoring strategies, but they are less developed for discrete processes. In this paper, the GLR control chart framework applied to aggregate cumulative quantities data is extended. Inspired by the technical note on GLR control charts from Lee and Woodall (2018), unnecessary artificial bounds in the GLR chart for geometric data proposed in literature are removed and parameter restriction errors, common in GLR designs, are corrected. Finally, the Gamma GLR chart for continuous-time time-between-event data that can be modeled by a Poisson process is proposed and its performance are evaluated and compared to its traditional competitors.     

Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

In modern manufacturing processes, massive amounts of multivariate data are routinely collected through automated in-process sensing. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio and missing values. Conventional univariate and multivariate statistical process control techniques are not suitable to be used in these environments. This article discusses these issues and advocates the use of multivariate statistical process control based on principal component analysis (MSPC-PCA) as an efficient statistical tool for process understanding, monitoring and diagnosing assignable causes for special events in these contexts. Data from an autobody assembly process are used to illustrate the practical benefits of using MSPC-PCA rather than conventional SPC in manufacturing processes.     

Identifying the period of a step change in high‐yield processes

Quality control charts have proven to be very effective in detecting out‐of‐control states. When a signal is detected a search begins to identify and eliminate the source(s) of the signal. A critical issue that keeps the mind of the process engineer busy at this point is determining the time when the process first changed. Knowing when the process first changed can assist process engineers to focus efforts effectively on eliminating the source(s) of the signal. The time when a change in the process takes place is referred to as the change point. This paper provides an estimator for a period of time in which a step change in the process non‐conformity proportion in high‐yield processes occurs. In such processes, the number of items until the occurrence of the first non‐conforming item can be modeled by a geometric distribution. The performance of the proposed model is investigated through several numerical examples. The results indicate that the proposed estimator provides a reasonable estimate for the period when the step change occurred at the process non‐conformity level. Copyright © 2009 John Wiley & Sons, Ltd.     

A note on GLR charts for monitoring count processes

Various generalized likelihood ratio (GLR) charts have been proposed to monitor count processes such as binomial, Bernoulli, Poisson, and multinomial processes. The advantages of GLR charts are that designing the chart is relatively easy, estimates of the process change‐point and shift size are available for post‐signal diagnosis, and they are effective in detecting a wide range of shifts in the process parameter. However, for some special cases of the observations, such as observing all defective items or all non‐defective items, the GLR chart statistic for monitoring a count process has been said to be undefined. We show that the GLR chart statistic is always well defined.     

Discriminant locality preserving projection chart for statistical monitoring of manufacturing processes

In this paper, we propose a novel multivariate projection chart called the discriminant locality preserving projection chart. The basic idea of the chart is to seek an optimal linear projection of the original data including both the in-control reference data and the newly observed data for monitoring. The projection strives to not only preserve the locality structure of the original data but also maximise the separation between the in-control reference data and the newly observed data. With this projection, the low-dimensional projected data will then be monitored through a T² type of statistics. Comparing with the existing projection-based control chart, the proposed chart preserves the local data structure and adaptively identifies the best projection direction to detect the out-of-control data, and thus has more discriminating power, particularly for non-linearly related multidimensional data. The design issues of this chart are discussed in details in this paper. The effectiveness of the proposed method is verified by numerical studies and a real case study of forging process monitoring.