Today, with the growth of technology, Monitoring Processes by the use of video and satellite sensors have been more expanded, due to their rich and valuable information. Recently, some researchers have used sequential images for defect detection because a single image is not sufficient for Process Monitoring. In this paper, by adding the time dimension to the image-based Process Monitoring problem, we detect Process changes (such as the changes in the size, location, speed, color, etc.). The temporal correlation between the images and the high dimensionality of the data make this a complex problem. To address this, using the sequential images, a Statistical approach with RIDGE regression and a Q control chart is proposed to monitor the Process. This method can be applied to color and gray images. To validate the proposed method, it was applied to a real case study and was compared to the best methods in literature. The obtained results showed that it was more effective in finding the changes.

Introduction: Statistical Process control (SPC) is a handy and powerful tool for Monitoring quality assurance (QA) programs in radiotherapy. This study explains the institutional experience in Monitoring weekly output constancy QA and patient-specific quality assurance (PSQA) using SPC tools. Material and Methods: Prospective Monitoring of output constancy has been demonstrated by the simultaneous usage of Shewhart's I-MR charts and time-weighted control charts. PSQA results were retrospectively analysed in a combined γ,and dose volume histogram (DVH) based analysis using control charts and Process capability indices. A PSQA analysis method has been illustrated in which the site-specific action limits (AL) and control limits (CL) for γ,and DVH based analysis were obtained using SPC. Results: The simultaneous use of different control charts indicated a systematic error in the output constancy of Linac as successive measurement points fell above the CL. The reason for failure was found and Process was monitored further. The obtained AL and CL for γ,and DVH based analysis were used to decide pass or fail criteria in PSQA. Among the analysed treatment plans, fourteen plans of different treatment sites failed the PSQA analysis. Cause-and-effect analysis of these failed treatment plans in PSQA pointed out six primary potential sources of errors in the results. Conclusion: SPC tools can be adopted among institutions for consistent and comparable QA programs. If the QA Process monitored using SPC falls outside the CL, cause-and-effect diagrams can be used to extract all possible contributing factors that lead to such a Process state.

In this paper, first the available single charting methods, which have been proposed to detect simultaneous shifts in a single Process mean and variance, are reviewed. Then, by designing proper simulation studies these methods are evaluated in terms of in-control and out-ofcontrol average run length criteria (ARL). The results of these simulation experiments show that the EWMA and EWMS methods are quite capable to detect large shifts in the means and variances. However, while the two EWMV procedures under study do not perform well, the Max-Min EWMA and Max-EWMA perform very well in all scenarios of mean and variance shifts.

Profile Monitoring is a new research subject in Statistical Process Control which has been recently considered by many researchers. Profile describes the relationship between a response variable and one or more independent variables. This relationship is often modeled using regression which can be simple linear, multiple linear, polynomial or sometimes nonlinear. To monitor polynomial profiles, some methods have been developed, the best of which is the orthogonal polynomial approach. One of the disadvantages of this method is the large number of control charts which are simultaneously used. In this paper, a new approach has been proposed based on orthogonal polynomial approach, in which only two control charts are used to monitor a kth-order polynomial profile. Simulation findings and average run length (ARL) curve analysis imply better performance of the proposed approach compared to the existing approach. Also, using the proposed approach is much easier in practice.

On-line high performance liquid chromatography (HPLC) was used to monitor steady state reactions in three reactors (K=3) over 48.0 h. Different numbers of chromatograms, with J=1981 retention time points, were recorded for each of the three reactors. Peaks for each chromatogram were baseline corrected and aligned using correlation optimized warping (COW). To make a complete three-way data set of I=266 chromatograms a cubic Hermite interpolation was performed. The applied bilinear multivariate Statistical Process control (MSPC) method included the unfolding PCA and the trilinear technique was PARAFAC.Unfolding in reactor (K) mode was the most informative. D-charts and Q-charts were applied to the data to determine samples which were out of control. Confidence limits were then applied to the D and Q-charts and variables with different behaviours from that encapsulated within the reference data set were located. Both bilinear and trilinear methods were found to be useful for Process analysis.

One of the most important goals of organizations and manufacturing companies is to provide suitable products and services to customers, requiring high-quality Processes and keeping them at a desired quality level. In many cases, product quality is low due to equipment deterioration,however, it cannot be , gured out until the equipment breaks down. On the other hand, control charts can be used to identify the condition of the Process, where the out-of-control state for a quality characteristic means deterioration in the equipment, which is used for the manufacturing purpose. Hence, the Statistical Process control and maintenance decisions can be combined to form an integrated model that enjoys higher e, ciency in reducing costs of quality and maintenance. Furthermore, using delayed Monitoring policy to monitor Processes is one of the newest research , elds in this regard. Delayed Monitoring means that Processes are in control at the beginning of the Process and sampling can be delayed until the pre-speci, ed scheduled time. With a delayed Monitoring policy, the total cost of production per unit is expected to be more a, ordable as the sampling rate decreases,however, it may be lead to an increase in the quality and maintenance costs. Therefore, determining e, cient decision variables is important in the model. In this paper, an integrated Statistical Process control and maintenance model based on delayed Monitoring is designed for a two-stage Process. By using this procedure, 28 di, erent scenarios are created in which variations in quality and di, erent breakdown states are considered. , X residual mean control charts have been used for Monitoring purposes. In order to integrate Statistical Process control and maintenance, a model is proposed such that the expected cost per time unit of manufacturing is minimized by using a genetic algorithm. To evaluate the performance of the proposed method, an illustrative example is presented. In addition, sensitivity analysis of some parameters of the proposed model is carried out. The results show the appropriate performance of the proposed model.

There are miscellaneous quality characteristics in healthcare which are interested to be monitored. However, Monitoring each characteristic needs a special Statistical method. There are some characteristics with very small incidence rates that it’s usually considered not to be necessary to Monitoring them, since their incidence rates are so small that p and np charts are not able to monitor them. Such characteristics are called “rare health-related events”. In this paper, while introducing the most common methods for Monitoring the rare events, we are going to compare their performances based on the average run length (ARL).

Wind-induced waves are the most complex waves among sea waves. In special circumstances such as extreme winds and storms however, it is essential to estimate and predict them correctly. In this research, the height and period of waves was investigated during winter Shamal wind using (SPM) method and a numerical simulation employing Coupled Ocean-Atmosphere-Wave Sediment Transport COAWST, in the northwest of the Persian Gulf in January 2015. The beginning of the winter Shamal wind coincides with the passage of a cold front over the Persian Gulf, when the wind speed increases and the direction of wind changes to north and northwest. According to the results simulated wave height and period from COAWST show better agreement with field data compared with those of (SPM) method. This conclusion is also verified with Statistical methods such as RMSE. In both methods, the frequency spectrum of the region is single-peak. Besides, the peak of the spectrum of (SPM) and COAWST, shows under-prediction in comparison with the field data. According to the standard spectrums, the JONSWAP represents the closest wave spectrum of the area under investigation.