Process Monitoring Using Principal Component Analysis in Different Operating Time Processes

Batch processes are very important to the chemical and manufacturing industries. Techniques for monitoring these batch processes to ensure their safe operation and that they produce consistent high quality products are needed. Nomikos and MacGregor (1994) present a multi-way principal component analysis (MPCA) approach for monitoring batch processes, and test results show that the method is simple, powerful, and effective. However MPCA is a linear method, and most batch processes are nonlinear. In this paper a nonlinear principal component analysis (NLPCA) method (Dong-McAvoy, 1993) is used for batch process monitoring. The main focus of this paper is on multi-stage batch process monitoring. The proposed approach and special problems for multi-stage batch processes are illustrated through a detailed simulation study.

On-line batch process monitoring using a consecutively updated multiway principal component analysis model

Dynamic model-based fault diagnosis for (bio)chemical batch processes

The paper describes the application of statistical process control (SPC) methods to supervise the control of a noisy process. SPC charts form a supervisory unit that monitors process outputs and selectively applies terms of a conventional PID controller. The advantages of this method lie in the ability of the control loop to distinguish between process noise and genuine error signals. This stops the controller reacting unnecessarily reducing actuator work. Results are presented for the control of a steam/water heat exchanger rig and show the effectiveness of this supervisory unit to produce quieter control without creating a lag in the control loop. >

Radiation Portal Monitors (RPM) acquire sequential measurements of gamma activity that can be modeled as a sequence of Poisson distributed random numbers. These series can be analyzed with Statistical Process Control (SPC) methods to trigger alarms when the statistical distribution of the ambient dose during a transit changes. Some usual SPC methods have been compared and modified to narrow the time required to bound an alarm. Both the probabilities of detection and the Average Run Length (ARL) are used and fitted to curves to benchmark the SPC methods, which are ranked as: SR, EWMA, CUSUM, SPRT, FLR, and Shewhart.

There has been a lot of research activity in the area of batch process analysis and monitoring for abnormal situation detection since the pioneer work of Nomikos and MacGregor []. However, some of the key ideas and the thought process that led to those first papers have been forgotten. Batch process data are dynamic data. The whole philosophy of looking at batch process data with latent variables was developed because batch process variables are both autocorrelated and cross‐correlated. Statistical process control by definition checks deviations from a nominal behavior (a target). Therefore for statistical process control of batch processes we should look at deviations of process variable trajectories from their nominal trajectories and from their nominal auto/cross‐correlations. An added advantage to modeling the deviations from the target trajectory is that a non‐linear problem is converted to a linear one that it is easy to tackle with linear latent variable methods such as principal component analysis (PCA) and partial least squares (PLS). This paper first takes a critical look at the true nature of batch process data. The general case where variables are not present during the entire duration of the batch is addressed. It is then illustrated how proper centering (by taking the deviations from the target trajectory) can retain valuable information on auto‐ and cross‐correlation of the process variables. This auto‐ and cross‐correlation is only modeled with a certain types of models. Topics such as scaling and trajectory alignment are revisited and issues arising when using the indicator variable approach are addressed. The development of control charts for multiblock, multiway PCA/PLS is discussed. Practical issues related to applications in industry are addressed. Then some of the methods that have appeared in the literature are examined as to their assumptions, their advantages and disadvantages and their range of applicability. Finally the nature of transition data (start‐ups, grade transitions) is discussed and issues related to aligning, centering and scaling such types of data are presented. Copyright © 2003 John Wiley & Sons, Ltd.

Statistical process control (SPC) methods have been extensively applied to monitor the quality performance of manufacturing processes to quickly detect and correct out-of-control conditions. As sensor and measurement technologies advance, there is a continual need to adapt and refine SPC methods to effectively and efficiently use these new data-sets. One of the most state-of-the-art dimensional measurement technologies currently being implemented in industry is the 3D laser scanner, which rapidly provides millions of data points to represent an entire manufactured part's surface. Consequently, this data has a great potential to detect unexpected faults, i.e., faults that are not captured by measuring a small number of predefined dimensions. However, in order for this potential to be realized, SPC methods capable of handling these large data-sets need to be developed. This paper presents an approach to performing SPC using point clouds obtained through a 3D laser scanner. The proposed approach transforms high-dimensional point clouds into linear profiles through the use of Q---Q plots, which can be monitored by well established profile monitoring techniques. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios. In addition, experimental studies were performed to determine the effectiveness of the proposed approach using actual point cloud data. The results of these experiments show that the proposed approach can significantly improve the monitoring capabilities for manufacturing parts that are characterized by complex surface geometries.

Several methods of Statistical Process Control (SPC) are used to analyze process measurements with the purpose to detect faults that affect the process stability. SPC has a major drawback because it indicates the presence of faults without explaining which ones and where are the faults. In practical applications, SPC just analyses univariate signals limiting the study of multiple measures. Nowadays, novel methods have been developed for fault analysis based on pattern recognition in control charts. However, the majority of these studies follow a univariate approach. This article proposes a multivariate pattern recognition approach using machine learning algorithms in conjunction with a scatter diagram as the proposed method. In particular the aim of this approach is to monitor quality characteristics of a product in a multivariate environment considering states in control and out of control without the constraints of statistical conditions with the possibility of its application in real time. Results using Support Vector Machines (SVM) and the FuzzyARTMAP neural network showed that multivariate patterns can be recognized successfully in 81% of the cases.

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.

Batch process monitoring in the original measurement's space

Six sigma process improvement in the emergency department: A pilot study using statistical process control methods to analyze ecg times in the emergency department

Photoluminescence spectroscopic probes offer the potential for differentiation among plant species in real-time. Spectral emission signatures (excitation at 365 nm) from three different pH (2.2, 7.5 and 12.5) phosphate buffered saline (PBS) extracts from two grasses, Sporobolus flexuosus (Thurb. ex Vasey) Rydb., [mesa dropseed], and Pleuraphis mutica Buckley [tobosa], two forbs, Dimorphocarpa wislizenii (Engelm.) Rollins [spectacle pod], and Sphaeralcea incana Torrey [pale globemallow], and leaves and twigs from two shrubs Flourensia cernua DC. [tarbush], and Atriplex canescens (Pursh) Nutt., [fourwing saltbush] were examined. Since pH has been shown to be pivotal in affecting extraction efficiency of other plant compounds pH seemed appropriate as an additional dimension within our multi-way principal component analysis (MPCA) to differentiate among six different plant species. In particular, MPCA allowed differentiation between Sporobolus and Pleuraphis that was not possible using only principal component analysis (PCA). This research suggests MPCA may be a more appropriate tool than PCA when attempting to discriminate among plant species.

Quality is a benchmark to determine the level of good and bad of a product. The level of quality of a product will affect customer satisfaction, hence, to produce high quality products, it is necessary for a company to have a quality control process. Quality control is a process that aims to maintain the quality of products and services that have been promised to consumers. In this study, quality control is carried out in the sugar packaging process. Where in the sugar packaging process found the resulting product has a poor quality, defective and not in accordance with specifications. This study uses the Statistical Process Control (SPC) method which aims to determine the causes of defective products with the intention that the packaging process can further minimize the level of product defects. The SPC method is a statistical analysis technique with seven statistical tools or seven tools. Based on the results of the study it can be seen that the cause of product damage / defects in the product packaging process is caused by three types of damage namely damage due to conveyor (38.17%), damage due to machine pinched (35.82%), and damage due to loose seams (26,00%) This type of damage can be caused by human error and other factors such as engine condition, engine cleanliness and the monitoring process. Proposed improvements recommended for the company are to provide training to employees, make clear work instructions, conduct periodic maintenance for the machines used, supervise all work areas, and carry out quality control for every acceptance of raw materials.

Biodiesel is a promising alternative fuel that can be obtained from vegetable oils or animal fats through transesterification reactions with alcohol. Biodiesel has numerous advantages compared to diesel oil, namely: it is a renewable energy resource, it is not toxic, and it is environmentally friendly because the raw material does not contain sulfur and low emissions. Biodiesel production is inseparable from the availability of raw materials until now the raw materials that meet the needs of production capacity are palm oil (CPO). To overcome this problem, in this study we want to find out how much biodiesel conversion percentage if we use other raw materials such as: CPO oil, corn oil, VCO oil, and waste cooking oil. The analysis used in this study was the analysis of the use of raw materials to the value of biodiesel conversion using the Statistical Process Control (SPC) method. SPC method was used to analyze, manage, control, and improve a product and process using statistics. The objective of this study was to produce high conversion percentage biodiesel and analyze and control the quality of research products. By having this statistical method, it could be found errors in a study or out of control production so that further action can be taken to overcome them. In this study, it was found that the product which was outside of the control limit was biodiesel made from CPO and waste cooking oil. The results of the analysis using cause and effect diagrams could determine the causes of damage in the production process, which come from the factors of workers/humans, production machines, working methods, materials/raw materials and work environment.

Moving towards process-based radiotherapy quality assurance using statistical process control