Statistical Process Monitoring Techniques Research Articles

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.

A time series model coefficients monitoring approach for controlled processes

Statistical process monitoring (SPM) has been adopted widely in manufacturing industry. Traditional SPM techniques such as principal component analysis (PCA) and partial least square (PLS) are applied to monitor a stationary process. When applied to a process with a feedback and/or feedforward controller, there are some monitoring challenges needed to be addressed, such as nonstationarity of process data and false alarm. To deal with these problems, a statistical online process monitoring scheme is presented in this paper. The proposed method consists of two phase: on-line time series model building and process monitoring via SPM. In the model building phase, a process with a controller is represented by a time series model, and a recursive extended least square (RELS) algorithm is used to identify the coefficients of this model. Furthermore, it is proved that the coefficients are stationary even if the process input/output data are non-stationary. In the process monitoring phase, the changes in process input-output relations or disturbance dynamics can be detected by applying SPM on the model coefficients. The validity and effectiveness of the proposed approach are illustrated by three examples in industrial processes, i.e., a semiconductor manufacturing process, a DC motor process and a benchmark Tennessee Eastman process.

Statistical Monitoring of Complex Chemical Processes Using Agent-Based Systems

It is highly desirable to have a statistical process monitoring (SPM) system that detects the abnormalities in process operations quickly with as few missed and false alarms as possible while the process operates under various operating conditions. An agent-based combined monitoring and fault detection framework is proposed in this study. In this framework, different SPM techniques compete with and complement each other to enhance detection speed and accuracy. SPM techniques from literature such as principal component analysis (PCA), multiblock PCA (MBPCA), and dynamic PCA (DPCA) techniques are implemented in this agent-based process supervision system. An agent performance assessment and agent management layer provides dynamic adaptation of the supervision system and improves the performance of SPM. The statistical information coming from each of the statistical techniques is summarized through a consensus mechanism. The performance of the agent-based consensus mechanism using different consensus criteri...

HACCP with multivariate process monitoring and fault diagnosis techniques: application to a food pasteurization process

Multivariate statistical process monitoring (SPM), and fault detection and diagnosis (FDD) methods are developed to monitor the critical control points (CCPs) in a continuous food pasteurization process. Multivariate SPM techniques effectively use information from all process variables to detect abnormal process behavior. Fault diagnosis techniques isolate the source cause of the deviation in process variable(s). The methods developed are illustrated by implementing them to monitor the critical control points and diagnose causes of abnormal operation of a high temperature short time (HTST) pasteurization pilot plant. The detection power of multivariate SPM and FDD techniques over univariate SPM techniques is shown and their integrated use to ensure the product safety and quality in food processes is demonstrated.

Modeling, monitoring and control strategies for high temperature short time pasteurization systems — 3. Statistical monitoring of product lethality and process sensor reliability

Statistical process monitoring (SPM) is used in food processing industries to improve productivity and product quality. SPM can also provide information to operators on how close a process is to non-compliance to product safety limits, and carry out periodic checks of sensor accuracy at high frequency. Traditional SPM tools such as Shewhart charts are not appropriate for continuous food processes because of autocorrelation in data. Four alternative SPM techniques are presented and applied to high temperature short time (HTST) dairy pasteurization. The study attempted to achieve compliance of the HTST process operation with the recommended Pasteurized Milk Ordinance by providing a margin between the alarm limits of the monitoring chart and the safety limits. Monitoring of residuals and parameter change detection techniques are used for monitoring processes with autocorrelated variables. Hotelling's T 2 and residuals of canonical variates techniques are used for monitoring multivariable processes.