جستجوی مقالات مرتبط با کلیدواژه « نمودار کنترل » در نشریات گروه « فنی و مهندسی »

Tracking military structures and equipment is one of the parameters to create superiority in military battles. Camouflage has long been used to reduce the possibility of detection of military structures and equipment. Development of knowledge in the field of tracking and discovering military structures and equipment followed by the necessity of using the concealment in order to reduce vulnerability in war, has enhanced the importance of using new camouflage and radar evasion methods. The use of nanotechnology in the field of radar evasion of military facilities was developed by introducing graphene as a very strong absorber of electromagnetic waves. Graphene coating on the military installations causes the absorption of electromagnetic waves and as a result, these installations are not detected by the enemy's radar.  Referring to the fact that there is a functional relationship between the diameter of the graphene oxide used and the radar evasion of the equipment, an attempt has been made in this article for the first time to find a solution to control and monitor the radar evasion quality using graphene oxide (with the scientific name of RGO/NiFe2O4) in the microwave spectrum of 7GHZ by the profile approach and presentation of a regression relationship. This model can be used to monitor the quality of radar evasion products (cheaper and faster than existing methods). Finally, sensitivity analysis of the model showed that the ability to detect non-conformity in the manufactured products can be detected quickly (between 1 and 20 samples) with the change in the parameters of the regression model.

In shock models, the system will fail as soon as the amount of damages caused by shocks exceeds its failure threshold. In this research, the design of control chart for reliability monitoring of systems with random failure threshold subjected to cumulative random shocks is discussed. In the cumulative shock model, the system fails when the cumulative amount of damage caused by the shocks exceeds the failure threshold of the system. For the analytical solution of the model related to the control chart, there are some complexities such as the existence of unknown distributions, obtaining the convolutions of the distributions and integral calculations. To overcome these problems, phase-type distribution is used for modeling. By presenting a numerical example, the results of the presented analytical model are evaluated and compared with the Monte Carlo simulation method. Also, the performance of the proposed control chart using the average run length and average time to out of control signal criteria is evaluated.

Most Shewhart control charts are designed to monitor changes in the mean or variance of the process. There are some situations when the process mean fluctuates from time to time, but is still considered as in-control and the process standard deviation is a linear function of the process mean. In addition, in some cases, the mean and the variance of the process are actually dependent on each other. Also, in many processes, monitoring the mean or variance of the process is unreasonable due to the nature of the process, and it is recommended that the coefficient of variation be used to monitor the process. Although monitoring multivariate coefficient of variation was studied at both Phases I and II, the design of chart for monitoring multivariate CV considering measurement errors was not thoroughly studied in previous studies; hence, it has been considered in this research. In this paper, a run sum control chart is developed for monitoring multivariate coefficient of variation in the presence of measurement errors at Phase II and the performance of the proposed chart with and without the assumption of measurement errors was compared through Average Run Length (ARL) criterion based on Markov chain approach. The results show that the presence of measurement errors has a negative effect on the performance of the run sum control chart. In other words, ARL of the run sum chart in the presence of measurement errors gets far away from the corresponding value without measurement errors as the magnitude of measurement errors increases. This research considers multiple measurements approach to reduce the effect of measurement errors on the performance of control charts in monitoring the multivariate coefficient of variation at Phase II. The results of the proposed chart's performance show that ARL decreases in the presence of measurement errors due to increasing the effect of measurement errors on the performance of control chart. The results show that by using the multiple measurements approach, the results become closer to the case without measurement errors.

In some applications, performance of a process or quality of a product is characterized by a relationship between a response variable and one or more explanatory variables, referred to as profile in the literature. Certain methods have been developed to monitor various profiles. On the other side, nowadays due to diversity of customer demand and short time for presenting products in market, manufacturing strategy is focused on short run processes characterized by high diversity and low volume. Therefore, statistical process control for such processes, due to inspection restrictions in a short period is a special practice. In such circumstances, control charts in Phase I cannot be performed and correct estimations are not available for estimating process mean and standard deviation. To overcome the situation, self-starting methods are developed to update the parameter estimations along with new observations and simultaneous checks of the out-of-control conditions. Hence, implementing traditional control charts for monitoring short run processes is not practical, and new methods and control charts should be developed to monitor such processes. In this paper with aggregating two above-mentioned subjects, quality characteristics which are pertained to short run processes and which are modeled by simple linear profiles, have been monitored. Suitable methods and new control charts are developed to monitor process effectively. In this paper, we focus on monitoring residuals and propose new control charts to monitor mean and dispersion of residuals simultaneously. In order to monitor residuals in short run processes whose quality characteristics are modeled by simple linear profiles, we propose two control charts for monitoring mean and one control chart for monitoring standard deviation. Then, with combination of these control charts, we develop two distinct control charts named QMCC and TMCC to monitor mean and variance of residuals concurrently. Performance of the proposed control charts have been compared with competitor control chart using simulation studies and average run length (ARL) criterion. The results of simulation studies show that our proposed control charts in some parameters have better performance compared to competitive control chart under moderate and large shifts in terms of out-of-control ARLs.

Statistical process control plays an impressive role in industries due to the growing complicated products and processes. This tool helps practitioners prevent the production of defected products and waste of money and time. Due to the importance of processes and inefficiency of methods based on human inspection, the use of image data for statistical process control has gained great attention among researchers in recent years. Image data have been applied by industries for many years for separating defected products and preventing them to get to customers. In recent years, some methods are proposed by researchers in the area of applying statistical features of image data in statistical process control. Image data analysis is decomposed into two categories: spatial domain and frequency domain. The main concentration of previous research studies in the area of process monitoring using image data is in the area of spatial domain analysis. This study has proposed three methods based on one-dimensional wavelet decomposition for monitoring image data with respect to frequency domain features. At each level of decomposition, wavelet transformation decomposes each signal into two elements including an approximation part (which is similar to the main signal and is performed as a low-pass filter) and a detail element (which is performed as a high-pass filter). The first method proposed in this paper only applies approximation coefficient for process monitoring. The second and third methods consider the detail coefficient by using hard thresholding and soft thresholding, respectively. These methods use a likelihood ratio-based statistic for process monitoring. Hence, they can show an out-of-control status and estimate the change point that is one of the most important diagnostic information. The performance of these methods is evaluated and compared with respect to the average run length and the difference between real and estimated changepoint criteria. Simulation studies are performed by using a textile image. Results showed a suitable degree of accuracy in detecting out of control status and estimating change points.

Process capability indices are widely used in different industries for quality assurance purposes. An appropriate analysis of the process capability index can result in improving quality level and satisfying customer requirements. Recently, some control charts have been developed for continuous evaluation of process capability in producing conforming products. However, these charts are designed to monitor a single index and cannot directly show the relation between parameters of the quality characteristic distribution and the process capability index. In fact, average and dispersion of a process are combined into an index and control charts monitor this individual index. In this research, control charts are developed for monitoring the capability of a process in producing conforming products. In these charts, the capability of a process in producing conforming products is monitored in terms of the average and dispersion of the process. Therefore, interaction between these measures can also be considered into account. Control charts are developed for Normal, Lognormal and Weibull distributions and their performances are evaluated through simulation and Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) measures. The results show that the proposed control charts can lead to an appropriate monitoring of the process capability.

Structural health monitoring is an economical and reliable strategy for infrastructure condition assessment. In recent years, researchers have tried to propose algorithms based on statistical pattern recognition techniques. Studies show these algorithms can be successfully used to detect structural damage. Variability of operational and ambient conditions during data acquisition should be considered as an important factor in applying statistical pattern recognition methods in practical applications. This paper studies the efficiency of statistical pattern recognition methods on the damage detection of structures under various operational and ambient conditions. The data is obtained from an experimental study on an eight degrees of freedom mass spring system. Ambient vibration is applied to the mass spring system using random excitation. In order to simulate various ambient conditions, the amplitude level of the input force has been varied. By applying the statistical pattern recognition methods, the ability of these methods to damage detection under various ambient conditions is discussed. Two common approaches of statistical pattern recognition are considered. These approaches are autoregressive model accompanied with using control chart and Mahalanobis distance for outlier analysis. Results show the importance of considering the statistical pattern recognition methods for structural damage detection under various operational and ambient conditions.

N‌o‌w‌a‌d‌a‌y‌s, i‌n m‌a‌n‌y p‌r‌o‌d‌u‌c‌t‌i‌o‌n a‌n‌d s‌e‌r‌v‌i‌c‌e e‌n‌v‌i‌r‌o‌n‌m‌e‌n‌t, t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f a p‌r‌o‌d‌u‌c‌t o‌r p‌r‌o‌c‌e‌s‌s i‌s r‌e‌p‌r‌e‌s‌e‌n‌t‌e‌d b‌y t‌w‌o o‌r m‌o‌r‌e q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s. T‌h‌e‌r‌e‌f‌o‌r‌e, m‌u‌l‌t‌i‌v‌a‌r‌i‌a‌t‌e a‌n‌d m‌u‌l‌t‌i-a‌t‌t‌r‌i‌b‌u‌t‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s h‌a‌v‌e b‌e‌e‌n w‌i‌d‌e‌l‌y d‌e‌v‌e‌l‌o‌p‌e‌d b‌y m‌a‌n‌y a‌u‌t‌h‌o‌r‌s, s‌e‌p‌a‌r‌a‌t‌e‌l‌y. S‌o‌m‌e‌t‌i‌m‌e‌s, t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f a p‌r‌o‌d‌u‌c‌t o‌r a p‌r‌o‌c‌e‌s‌s i‌s r‌e‌p‌r‌e‌s‌e‌n‌t‌e‌d b‌y c‌o‌r‌r‌e‌l‌a‌t‌e‌d v‌a‌r‌i‌a‌b‌l‌e‌s a‌n‌d a‌t‌t‌r‌i‌b‌u‌t‌e q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s. F‌o‌r e‌x‌a‌m‌p‌l‌e, i‌n p‌l‌a‌s‌t‌i‌c m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌i‌n‌g c‌o‌m‌p‌a‌n‌i‌e‌s, t‌h‌e n‌u‌m‌b‌e‌r o‌f d‌e‌f‌e‌c‌t‌s i‌n o‌n‌e p‌r‌o‌d‌u‌c‌t, a‌s a‌n a‌t‌t‌r‌i‌b‌u‌t‌e, h‌a‌s a c‌o‌r‌r‌e‌l‌a‌t‌i‌o‌n w‌i‌t‌h t‌h‌e w‌e‌i‌g‌h‌t o‌f t‌h‌e p‌r‌o‌d‌u‌c‌t, a‌s a v‌a‌r‌i‌a‌b‌l‌e q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c. T‌o t‌h‌e b‌e‌s‌t o‌f o‌u‌r k‌n‌o‌w‌l‌e‌d‌g‌e, t‌h‌e‌r‌e i‌s n‌o m‌e‌t‌h‌o‌d t‌o m‌o‌n‌i‌t‌o‌r t‌h‌i‌s t‌y‌p‌e o‌f q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c. N‌o‌t‌e t‌h‌a‌t m‌o‌n‌i‌t‌o‌r‌i‌n‌g c‌o‌r‌r‌e‌l‌a‌t‌e‌d v‌a‌r‌i‌a‌b‌l‌e‌s a‌n‌d a‌t‌t‌r‌i‌b‌u‌t‌e q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s s‌e‌p‌a‌r‌a‌t‌e‌l‌y, w‌i‌t‌h‌o‌u‌t c‌o‌n‌s‌i‌d‌e‌r‌i‌n‌g t‌h‌e c‌o‌r‌r‌e‌l‌a‌t‌i‌o‌n s‌t‌r‌u‌c‌t‌u‌r‌e, l‌e‌a‌d‌s t‌o i‌n‌c‌r‌e‌a‌s‌i‌n‌g t‌h‌e o‌v‌e‌r‌a‌l‌l p‌r‌o‌b‌a‌b‌i‌l‌i‌t‌y o‌f T‌y‌p‌e I e‌r‌r‌o‌r i‌n t‌h‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t. A‌n a‌p‌p‌r‌o‌p‌r‌i‌a‌t‌e a‌p‌p‌r‌o‌a‌c‌h i‌n d‌e‌s‌i‌g‌n‌i‌n‌g c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s i‌s d‌e‌f‌i‌n‌i‌n‌g c‌o‌n‌f‌i‌d‌e‌n‌c‌e l‌i‌m‌i‌t‌s. T‌h‌e‌r‌e a‌r‌e s‌o‌m‌e m‌e‌t‌h‌o‌d‌s t‌o ‌e‌t‌e‌r‌m‌i‌n‌e c‌o‌n‌f‌i‌d‌e‌n‌c‌e l‌i‌m‌i‌t‌s f‌o‌r c‌o‌r‌r‌e‌l‌a‌t‌e‌d r‌a‌n‌d‌o‌m d‌a‌t‌a, s‌u‌c‌h a‌s d‌e‌s‌c‌r‌i‌b‌e‌d b‌y B‌o‌n‌f‌e‌r‌o‌n‌i (H‌a‌y‌t‌e‌r a‌n‌d T‌s‌u‌i, 1994) a‌n‌d S‌i‌d‌a‌k (1967). B‌u‌t, t‌h‌e f‌i‌r‌s‌t m‌e‌t‌h‌o‌d r‌e‌q‌u‌i‌r‌e‌s l‌a‌r‌g‌e s‌a‌m‌p‌l‌e‌s, w‌h‌i‌c‌h l‌e‌a‌d t‌o l‌e‌s‌s a‌p‌p‌l‌i‌c‌a‌t‌i‌o‌n, a‌n‌d t‌h‌e s‌e‌c‌o‌n‌d h‌a‌s a w‌e‌a‌k‌n‌e‌s‌s i‌n n‌e‌g‌l‌e‌c‌t‌i‌n‌g t‌h‌e c‌o‌r‌r‌e‌l‌a‌t‌i‌o‌n b‌e‌t‌w‌e‌e‌n q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s. I‌n t‌h‌i‌s p‌a‌p‌e‌r, w‌e p‌r‌o‌p‌o‌s‌e t‌w‌o m‌e‌t‌h‌o‌d‌s t‌o m‌o‌n‌i‌t‌o‌r m‌u‌l‌t‌i‌v‌a‌r‌i‌a‌t‌e-a‌t‌t‌r‌i‌b‌u‌t‌e p‌r‌o‌c‌e‌s‌s‌e‌s, b‌a‌s‌e‌d o‌n t‌h‌e b‌o‌o‌t‌s‌t‌r‌a‌p t‌e‌c‌h‌n‌i‌q‌u‌e (J‌h‌u‌n e‌t a‌l. 2007), w‌h‌i‌c‌h h‌a‌s n‌o‌n‌e o‌f t‌h‌e d‌r‌a‌w‌b‌a‌c‌k‌s o‌f t‌h‌e m‌e‌n‌t‌i‌o‌n‌e‌d m‌e‌t‌h‌o‌d‌s (B‌o‌n‌f‌e‌r‌o‌n‌i a‌n‌d S‌i‌d‌a‌k). I‌n t‌h‌e f‌i‌r‌s‌t m‌e‌t‌h‌o‌d, n‌e‌w c‌o‌n‌f‌i‌d‌e‌n‌c‌e l‌i‌m‌i‌t‌s f‌o‌r m‌u‌l‌t‌i‌v‌a‌r‌i‌a‌t‌e-a‌t‌t‌r‌i‌b‌u‌t‌e q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s a‌r‌e p‌r‌e‌s‌e‌n‌t‌e‌d b‌y u‌s‌i‌n‌g t‌h‌e b‌o‌o‌t‌s‌t‌r‌a‌p t‌e‌c‌h‌n‌i‌q‌u‌e. I‌n t‌h‌e s‌e‌c‌o‌n‌d m‌e‌t‌h‌o‌d, t‌h‌e b‌o‌o‌t‌s‌t‌r‌a‌p t‌e‌c‌h‌n‌i‌q‌u‌e i‌s u‌s‌e‌d t‌o d‌e‌t‌e‌r‌m‌i‌n‌e c‌o‌n‌f‌i‌d‌e‌n‌c‌e l‌i‌m‌i‌t‌s f‌o‌r E‌W‌M‌A c‌o‌n‌t‌r‌o‌l s‌t‌a‌t‌i‌s‌t‌i‌c‌s, w‌h‌i‌c‌h a‌r‌e u‌s‌e‌d f‌o‌r c‌o‌r‌r‌e‌l‌a‌t‌e‌d a‌t‌t‌r‌i‌b‌u‌t‌e a‌n‌d v‌a‌r‌i‌a‌b‌l‌e q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s. F‌i‌n‌a‌l‌l‌y, t‌h‌e‌s‌e c‌o‌n‌f‌i‌d‌e‌n‌c‌e l‌i‌m‌i‌t‌s a‌r‌e u‌s‌e‌d t‌o m‌o‌n‌i‌t‌o‌r t‌h‌e p‌r‌o‌c‌e‌s‌s. B‌a‌s‌e‌d o‌n t‌h‌e s‌i‌g‌n‌a‌l r‌u‌l‌e, w‌h‌e‌n‌e‌v‌e‌r e‌a‌c‌h c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t s‌i‌g‌n‌a‌l‌s, t‌h‌e p‌r‌o‌c‌e‌s‌s w‌i‌l‌l b‌e o‌u‌t-o‌f-c‌o‌n‌t‌r‌o‌l a‌n‌d t‌h‌e c‌o‌r‌r‌e‌s‌p‌o‌n‌d‌i‌n‌g q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c i‌s i‌n‌t‌r‌o‌d‌u‌c‌e‌d a‌s t‌h‌e s‌o‌u‌r‌c‌e o‌f v‌a‌r‌i‌a‌t‌i‌o‌n. T‌h‌i‌s r‌e‌s‌e‌a‌r‌c‌h i‌s p‌e‌r‌f‌o‌r‌m‌e‌d i‌n P‌h‌a‌s‌e I‌I, t‌h‌u‌s, i‌t i‌s a‌s‌s‌u‌m‌e‌d t‌h‌a‌t t‌h‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n‌s o‌f q‌u‌a‌l‌i‌t‌y c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c‌s a‌r‌e k‌n‌o‌w‌n b‌a‌s‌e‌d o‌n P‌h‌a‌s‌e I a‌n‌a‌l‌y‌s‌e‌s. T‌h‌e o‌b‌t‌a‌i‌n‌e‌d r‌e‌s‌u‌l‌t‌s c‌o‌n‌f‌i‌r‌m‌s t‌h‌e e‌f‌f‌i‌c‌i‌e‌n‌c‌y o‌f t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌e‌t‌h‌o‌d c‌o‌m‌p‌a‌r‌e‌d t‌o t‌h‌e t‌r‌a‌d‌i‌t‌i‌o‌n‌a‌l m‌e‌t‌h‌o‌d b‌a‌s‌e‌d o‌n t‌h‌e a‌c‌c‌u‌r‌a‌c‌y o‌f p‌r‌o‌b‌a‌b‌i‌l‌i‌t‌y o‌f T‌y‌p‌e I e‌r‌r‌o‌r, e‌s‌p‌e‌c‌i‌a‌l‌l‌y u‌n‌d‌e‌r s‌m‌a‌l‌l v‌a‌l‌u‌e‌s o‌f T‌y‌p‌e I e‌r‌r‌o‌r p‌r‌o‌b‌a‌b‌i‌l‌i‌t‌y.

When a control chart shows an out-of-control condition, a search begins to identify and eliminate the root cause(s) of the process disturbance. The time when the disturbance has manifested itself to the process is referred to as change point. Identification of the change point is considered as an essential step in analyzing and eliminating the disturbance source(s) effectively. When a process control is based on a quality characteristic vector, identification of the change point alone would not help practitioners to an effective elimination of the source(s) contributing to the out-of-control condition. This paper provides a control scheme based on artificial neural networks to identify the change point in a mean vector when the change type is monotonic and at the same time allows one to perform effective diagnostic analysis to identify the variable(s) responsible for the change in a bivariate process. To the best of our knowledge, this the first time that an artificial neural networks scheme is presented to identify the change point and simultaneously perform diagnostic analysis in a multivariate environment when a monotonic change appears in the mean vector. Average run length criterion is used as a vehicle to investigate the performance of the proposed scheme numerically under different correlation structure. Simulation results indicate effective performance for the proposed scheme.