A forecast‐based monitoring methodology for process transitions

Nonconforming parts are often produced when a process moves from one level to another due to transition events. Control charting, when applied to a stable state process, is an effective monitoring tool to continuously check for process shifts or upsets. However, the presence of transition events can impede the normal performance of traditional control chart with increased false alarms. The presence of autocorrelation also requires modification to the control charting procedure. We present a methodology for characterizing the process transition which involves a tracking signal statistic, based on the forecast‐based exponentially weighted moving average (EWMA). This test will supplement the forecast‐based EWMA control charting as a means of detecting when the transition event is complete. Such a procedure facilitates smooth application of the appropriate control chart by knowing when the transition is over. The transition characterization methodology also carries benefits in cost and material savings. We use a color transition process in plastic extrusion to illustrate a transition event and demonstrate our proposed methodology. Simulation is employed to evaluate the performance of the methodology. Copyright © 2001 John Wiley & Sons, Ltd.     

Transition Monitoring and Adjustment for Dynamic Systems in a Process Improvement Environment

In manufacturing applications, we often encounter process transitions due to a changeover in the production or perhaps an unknown perturbation. The main process improvement goal is to shorten the transition time by monitoring the process in order to quickly identify the start and end of the transition period and by actively adjusting the process during the transition. To address these issues, we propose a transition monitoring and adjustment methodology. A polymer process is used to illustrate this methodology. Using simulation, we characterize the impact of the transition adjustment on the effectiveness of monitoring. We show that the adaptive monitoring procedure is robust to small transition adjustments, thus supporting a complimentary application of process monitoring and process adjustment to improve process transitions. Copyright © 2005 John Wiley & Sons, Ltd.     

基于DSP的印刷品在线监控系统

介绍一种基于嵌入式DSP的印刷品在线监控系统,通过CCD相机采集印刷品测控条的图像信息,由BP神经网络模型转换到CYMK四色墨量,输出与标定墨量的差值,实现对墨量的调整.实验证明,该系统能实现对印刷机全程实时控制,有效解决了印刷品不能在线监控的缺点.     

ISFET-based biosensor to monitor sugar metabolism in bacteria

We report the use of ion-selective field effect transistor devices (ISFETs) with an integrated pseudo-reference electrode for on-line monitoring of bacterial metabolism by monitoring of the pH variation. As a model we tested the ability of Lactobacillus strains to ferment sugars, producing lactic acid, which results in a decrease in pH in the suspension medium. We have tested and compared sugar uptake by L. sakei and a L. curvatus strains. The results obtained show that it is possible to distinguish between both types of Lactobacillus strains through their pattern of ribose uptake. The use of ISFETs represents a non-invasive methodology that can be used to monitor biological activity in a wide variety of systems.     

Residual-life distributions from component degradation signals: A Bayesian approach

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.     

Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition

High-dimensional data monitoring and diagnosis has recently attracted increasing attention among researchers as well as practitioners. However, existing process monitoring methods fail to fully use the information of high-dimensional data streams due to their complex characteristics including the large dimensionality, spatio-temporal correlation structure, and nonstationarity. In this article, we propose a novel process monitoring methodology for high-dimensional data streams including profiles and images that can effectively address foregoing challenges. We introduce spatio-temporal smooth sparse decomposition (ST-SSD), which serves as a dimension reduction and denoising technique by decomposing the original tensor into the functional mean, sparse anomalies, and random noises. ST-SSD is followed by a sequential likelihood ratio test on extracted anomalies for process monitoring. To enable real-time implementation of the proposed methodology, recursive estimation procedures for ST-SSD are developed. ST-SSD also provides useful diagnostics information about the location of change in the functional mean. The proposed methodology is validated through various simulations and real case studies. Supplementary materials for this article are available online.     

Phase II process monitoring for the balanced one-way random effects model

Statistical process control (SPC) and monitoring techniques are useful in a variety of applications. In this paper, we consider prospective (Phase II) process monitoring for the balanced random effects (variance components) model with Shewhart-type charts when parameters are estimated from a Phase I study. Such a model is a nonstandard application of control charts and arises in a number of situations in practice. Effects of parameter estimation need to be accounted for or there maybe too many false alarms that will disrupt the monitoring regime. To this end, two types of corrected (adjusted) control limits are proposed, based on two perspectives, namely the unconditional and the conditional, as recommended in the recent literature. Results and derivations are provided along with tabulations and illustrations with real data. Robustness of the charts is examined and a summary and recommendations are given. An accompanying R package is provided for deploying the methodology. Other Phase II charts such as the EWMA and the CUSUM can be considered along similar lines and will be presented elsewhere.     

改善数字随动系统过渡过程方法

对控制系统阶跃响应的过渡过程进行研究,提出在计算机控制系统中改善过渡过程的新方 法,把过渡过程分成不同的阶段,各个阶段的传递函数不变而初始条件变化。从仿真结果看出,利用新方法可使控制系统获得快速无纹波无超调的过渡特性。     

Time-between-events monitoring using nonhomogeneous Poisson process with power law intensity

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 situations where we need adaptive monitoring, for example, health, flood, food, system, or terrorist surveillance. Therefore, the existing control charts are not useful, especially in sequential monitoring. This article introduces new adaptive TBE control charts for high-quality processes based on the nonhomogeneous Poisson process by assuming the power law intensity. In particular, probability control limits are used to develop control charts. The proposed methodology allows us to get control limits that are dynamic and suitable for online process monitoring with an additional advantage to monitor a process where we believe the underlying failure rate may be changing over time. The average run length and coefficient of variation of the run length distribution are used to assess the performance of the proposed control charts. Besides simulation studies, we also discuss three examples to highlight the application of the proposed charts.     

Should observations be grouped for effective monitoring of multivariate process variability?

A multivariate dispersion control chart monitors changes in the process variability of multiple correlated quality characteristics. In this article, we investigate and compare the performance of charts designed to monitor variability on the basis of individual and grouped multivariate observations. We compare one of the most well-known methods for monitoring individual observations—a multivariate exponentially weighted mean squared deviation (MEWMS) chart—with various charts based on grouped observations. In addition, we compare charts based on monitoring with overlapping and nonoverlapping subgroups. We recommend using charts based on overlapping subgroups when monitoring with subgroup data. The effect of subgroup size is also investigated. Steady-state average time to signal is used as the performance measure. We show that monitoring methods based on individual observations are the quickest in detecting sustained shifts in the process variability. We use a simulation study to obtain our results and illustrated these with a case study.     

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Statistical process control monitoring of nonlinear relationships (profiles) has been the subject of much research recently. While attention is primarily given to the statistical aspects of the monitoring techniques, little effort has been devoted to developing a general modeling approach that would introduce ‘uniformity of practice’ in modeling nonlinear profiles (analogously with the three‐sigma limits of Shewhart control charts). In this article, we use response modeling methodology (RMM) to demonstrate implementation of this approach to statistical process control monitoring of ecological relationships. Using 10 ecological models that have appeared in the literature, it is first shown that RMM models can replace (approximate) current ecological models with negligible loss in accuracy. Computer simulation is then used to demonstrate that estimated RMM models and estimated data generating ecological models achieve goodness‐of‐fit that is practically indistinguishable from one another. A regression‐adjusted control scheme, based on control charts for the predicted median and for residuals variation, is developed and demonstrated for three types of ‘out of control’ scenarios. Copyright © 2013 John Wiley & Sons, Ltd.     

Fault diagnosis in injection moulding via cavity pressure signals

In this paper we demonstrate the feasibility of applying pattern recognition techniques for monitoring and diagnosis to an injection moulding process. Mould cavity pressure signals collected during the process are utilized for monitoring and diagnosis. Principal component analysis is applied to reduce the dimensionality of multivariate signals to a univariate representative signal, while preserving the characteristics of the original signals. Process ‘fingerprints’ are gleaned through wavelet decomposition and multi-resolution analysis of the ‘reduced’ signal. Feature elements defined from these fingerprints are interpreted by an artificial neural network for process condition monitoring and fault diagnosis. The experimental results indicate that this approach is effective for ‘run to run’ process monitoring, diagnostics and control. The diagnostic system can be updated adaptively as new process faults are identified.     

Error cancellation modeling and its application to machining process control

The product quality in a machining process can be affected by datum surface imperfections, fixture locator errors, and machine tool errors. It has been previously observed that these errors can cancel out one another for certain features. The mathematical modeling and analysis of this phenomenon is currently an open issue. We use the concept of an Equivalent Fixture Error (EFE) embedded into a modeling methodology to obtain insights into this fundamental phenomenon and achieve an improved process control. Based on our process fault model we develop a sequential root cause identification procedure and EFE compensation methodology. A case study is presented to demonstrate the proposed diagnostic procedure. A simulation study is also performed to illustrate the error compensation procedure.     

Adaptive Forecast-Based Monitoring for Dynamic Systems

Several forecast-based monitoring methods have been developed for autocorrelated data. One effective method is to use the forecasts based on the exponentially weighted moving average (EWMA). However, during the transition period of dynamic systems, the forecast-based monitoring procedure becomes inadequate due to its use of constant time series model parameters. In this article we present an adaptive forecast-based monitoring approach that performs well on dynamic systems. We examine two competing procedures: the adaptive time series model and the adaptive EWMA. We use a plastic extrusion process with first-order dynamics to illustrate the application of these two procedures, and we also evaluate the performance of the two procedures via simulation.     

基于频域形态滤波的低速滚动轴承声发射信号降噪新方法

声发射检测技术以其灵敏度高、频响范围宽、信息量大等优点,为机械故障诊断提供了一条新的检测途径,但应用于旋转机械设备时,容易混入各种有色噪声。当噪声频率与声发射信号重叠时,传统的降噪方法难以满足要求。将形态滤波应用到信号频域,可以有效消除有色噪声的干扰。根据声发射频响特性,对频谱进行拟合平滑高斯白噪声的影响,最后重构到时域。仿真和实际低速轴承信号表明此方法具有较好的降噪效果,有利于信号后续的处理和分析。     

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.     

图像处理在自动焊接中的应用和展望

图像处理技术在自动焊接领域的应用已引起国内外学者的广泛重视。通过图像传感技术获取焊接熔池直观丰富的图像信息，使用高效的图像处理算法，提取焊接熔池的特征信息，用以实现自动焊接过程质量实时传感与控制。本文综合论述了图像处理技术在自动焊接中的应用原理、数字图像的采集方法、数字图像的特征信息定义、广义数字图像处理技术及其对自动化焊接理论研究和实践应用的推动作用。通过总结图像处理技术的研究和应用现状，综合分析了图像处理技术在现代焊接技术中发展和应用的前景。     

The effect of continuously updating control chart limits on control chart performance

An open topic within statistical process monitoring is the effect on control chart properties of updating the control chart limits during the monitoring period. The challenge is to use the correct data for updating the control limits as in‐control data could be incorrectly classified as out of control and therefore not used for re‐estimating the parameters, and out‐of‐control data could be classified as in control and therefore used for re‐estimating. In the present article, we study the effect of updating the Shewhart, cumulative sum, and exponentially weighted moving average control chart limits. We simulate different scenarios: the monitoring data could be in or out of control, and the practitioner may or may not be able to find out whether the process is indeed out of control when the control chart gives a signal. The results reveal that the variation in the performance of the conditional control charts decreases significantly as a result of updating the control chart limits when the updating data are in control and also when the updating data are out of control and the practitioner is able to classify correctly data samples that produce a signal. However, when a practitioner is not able to classify a signal correctly, the advisability of updating depends on the type of control chart and the level of data contamination.     

Fault Detection of Fuel Systems Using Polynomial Regression Profile Monitoring

Anomaly detection is the characterization of a normal behavior of a system or process and identification of any deviation from such normal behavior. Anomaly detection of critical systems provides an important financial and client competitive advantage because it gives the decision maker a lead time and flexibility to manage the health of the system. Fuel systems are complex and mission critical systems that require high operational availability because of the high costs associated with the services they provide. In complex systems, it is not uncommon to monitor a quality‐related response, which relies on the functional form between several variables using a nonlinear relationship. We present in this paper a new monitoring framework for smart fuel systems utilizing outlying observations detection and monitoring using c‐chart. The traditional control charts based on Hotelling's T² statistic were deficient in detecting smart fuel systems anomalies and a new approach was necessary to isolate faulty profiles. The proposed methodology requires a simple quality performance test that can be performed once assembly is completed to assure readiness for client use or completion of a job. The results were tested and validated using scaled data that mimic an actual system. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health from an inspection check to anticipate and avoid failures. Copyright © 2016 John Wiley & Sons, Ltd.