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Earthquake in recent years has increased tremendously. This paper outlines an evaluation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) charting technique to determine if the frequency of earthquake in the world is unusual. The frequency of earthquake in the world is considered from the period 1973 to 2016. As our data is auto correlated we cannot use the regular control chart like Shewhart control chart to detect unusual earthquake frequency. An approach that has proved useful in dealing with auto correlated data is to directly model time series model such as Autoregressive Integrated Moving Average (ARIMA), and apply control charts to the residuals. The EWMA control chart and the CUSUM control chart have detected unusual frequencies of earthquake in the year 2012 and 2013 which are state of statistically out of control.

When designing control charts the in-control parameters are unknown, so the control limits have to be estimated using a Phase I reference sample. To evaluate the in-control performance of control charts in the monitoring phase (Phase II), two performance indicators are most commonly used: the average run length (ARL) or the false alarm rate (FAR). However, these quantities will vary across practitioners due to the use of different reference samples in Phase I. This variation is small only for very large amounts of Phase I data, even when the actual distribution of the data is known. In practice, we do not know the distribution of the data, and it has to be estimated, along with its parameters. This means that we have to deal with model error when parametric models are used and stochastic error because we have to estimate the parameters. With these issues in mind, choices have to be made in order to control the performance of control charts. In this paper, we discuss some results with respect to the in-control guaranteed conditional performance of control charts with estimated parameters for parametric and nonparametric methods. We focus on Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts for monitoring the mean when parameters are estimated.

In these last few decades, control charts have received a growing interest because of the important role they play by improving the quality of the products and services in industrial and non-industrial environments. Most of the existing control charts are based on the assumption of certainty and accuracy. However, in real-life applications, such as weather forecasting and stock prices, operators are not always certain about the accuracy of an observed data. To efficiently monitor such processes, this paper proposes a new cumulative sum (CUSUM) X¯ chart under the assumption of uncertainty using the neutrosophic statistic (NS). The performance of the new chart is investigated in terms of the neutrosophic run length properties using the Monte Carlo simulations approach. The efficiency of the proposed neutrosophic CUSUM (NCUSUM) X¯ chart is also compared to the one of the classical CUSUM X¯ chart. It is observed that the NCUSUM X¯ chart has very interesting properties compared to the classical CUSUM X¯ chart. The application and implementation of the NCUSUM X¯ chart are provided using simulated, petroleum and meteorological data.

Tukey control chart (TCC) is a popular choice for robust monitoring of process parameters. With the advancement in technology, we develop refined techniques that incorporate multiple aspects in a single structure. This article is a similar effort to design an improved charting structure in the form of mixed Tukey EWMA-CUSUM chart (namely MEC-TCC). We have investigated the performance of the proposed chart using different run length properties. We have observed that the proposed MEC-TCC design serves the dual objectives, namely the efficient detection of shifts and robustness against non-normality. The comparative analysis has revealed that the proposed scheme is an effective competitor to the existing counterparts, including classical Shewhart, EWMA, CUSUM, Tukey and some other variants such as mixed EWMA-CUSUM, Tukey EWMA, Tukey CUSUM. Moreover, the proposed design presents some of the aforementioned charts as special cases. For real life considerations, we have implemented the proposed and exist...

This paper is a study on the multivariate CUSUM control charts using three di er-ent control statistics for monitoring covariance matrix. We get control limits and ARLsof the proposed multivariate CUSUM control charts using three di erent control statis-tics by using computer simulations. The performances of these proposed multivariateCUSUM control charts have been investigated by comparing ARLs. The purpose ofcontrol charts is to detect assignable causes of variation so that these causes can befound and eliminated from process, variability will be reduced and the process willbe improved. We show that the charts based on three di erent control statistics arevery e ective in detecting shifts, especially shifts in covariances when the variables arehighly correlated. When variables are highly correlated, our overall recommendationis to use the multivariate CUSUM control charts using trace for detecting changes incovariance matrix.Keywords: Average run length, covariance matrix, multivariate CUSUM control chart.

Biofouling is an undesirable phenomenon in heat exchangers, which adheres slowly and progressively to the inner surface of the tubes, results in a reduction of the heat transfer, and increases the operating and maintenance costs. Controlling this phenomenon is essential to ensure that the equipment operates under optimal conditions. Different biofouling detection methods are currently available, depending on the properties of the fluid or by means of intrusive elements in the process. In this paper, the validation of the CUSUM control graph as an alternative technique to conventional detection methods is proposed. This type of graph is very efficient in the early detection of slow and progressive changes within a process. In a pilot plant, formed by two tubular heat exchangers, biofouling was allowed to grow until experimental variables indicated that growth had occurred. The biofouling growth was monitored through the evolution of the heat transfer resistance Rf, which is a technique that has been widely validated for this purpose, and through the CUSUM control graphs. The variable Rf, clearly indicates the different phases of growth. However, the evolution of this variable depends on the physical-chemical characteristics and biological activity of water. The CUSUM graphs demonstrated a greater predictive capacity with regard to changes in the biological adherence process. Additionally, their results do not depend on the characteristics of water. This new tool could be simply and economically implemented regardless of the heat exchanger’s location.

The aim of this study was to derive explicit formulas of the average run length (ARL) of a cumulative sum (CUSUM) control chart for seasonal and non-seasonal moving average processes with exogenous variables, and then evaluate it against the numerical integral equation (NIE) method. Both methods had similarly excellent agreement, with an absolute percentage error of less than 0.50%. When compared to other methods, the explicit formula method is extremely useful for finding optimal parameters when other methods cannot. In this work, the procedure for obtaining optimal parameters&mdash, which are the reference value ( a ) and control limit ( h )&mdash, for designing a CUSUM chart with a minimum out-of-control ARL is presented. In addition, the explicit formulas for the CUSUM control chart were applied with the practical data of a stock price from the stock exchange of Thailand, and the resulting performance efficiency is compared with an exponentially weighted moving average (EWMA) control chart. This comparison showed that the CUSUM control chart efficiently detected a small shift size in the process, whereas the EWMA control chart was more efficient for moderate to large shift sizes.

This study is aimed at retrospectively analyzing and monitoring the rate of road traffic crashes using Cumulative Sum (CUSUM) charting technique. The data used in this study is a secondary data from the accident record office of the Federal Road Safety Commission (FRSC), Zone RS8 Command Headquarter, Offa Road, GRA, Ilorin, Kwara State, Nigeria for the period of January, 2000 to December, 2010. Retrospective analysis of the number of road crashes recorded during the years of study was carried out. Also Cumulative Sum (CUSUM) control chart schemes were designed and used to study the rate of road crashes. The result obtained from the Retrospective Analysis revealed that there are three significant segments which results to the following local means: 30.6, 34.10, 22.00, 46.00, 51.00, 35.54 and 54.90.

Made available in DSpace on 2018-12-11T17:31:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-01-01. Added 1 bitstream(s) on 2019-10-09T18:32:49Z : No. of bitstreams: 1 S0100-69162017000100106.pdf: 547946 bytes, checksum: 54a976dbd4a7a5dadbe9058e77d22181 (MD5) The knowledge of the operational quality of soybean harvester provides useful information to management in order to obtain the maximum performance of all available resources, with minimal expenses. The aimed of this study was to evaluate the quality of mechanized soybean harvesting operation in different formats of plots through statistical process control. Treatments were established from the formats of existing plots in the area (irregular, trapezoidal and rectangular). The activities carried out during the harvest were monitored (harvesting, unloading, handling, maneuvering and climate charts) and through these activities were made the calculation of capacity and harvesting operation efficiencies. In the determination of total losses were used 4 circular frames of 0.33 m2 each, the grain losses were considered below and above these frames. The statistical analysis was by means of statistical process control opting for the use of the type CUSUM charts. The rectangular plot showed greater management efficiency and harvesting efficiency. The trapezoidal shape facilitated the maneuvers relative to others. The rectangular plot has better quality of operation in relation to others. The CUSUM control chart showed to be effective in preventing instability and maintenance of process quality. Universidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias Universidade Federal de Viçosa Universidade Estadual Paulista (UNESP) Faculdade de Ciências Agrárias e Veterinárias

In recent years, road accident fatalities in Nigeria have continued to be on the increase. Thus, urgent attention is needed to reduce or eliminate road accidents fatalities. To achieve this goal, the cumulative sum (Cusum) control chart scheme was designed for monitoring the road accident fatalities using the recorded occurrence of road accident fatalities in a state in the western part of Nigeria. The designed Cusum detects the period of the years when the highest occurrence of road accident fatalities occurred. These periods were observed to be festive periods such as Christmas, Easter, Eid-el- Kabir, and Eid-el-Moluod. Therefore, the festive periods of the year should be used as benchmark by road managers as periods where more attention or precaution measure should be put in place on the roads to drastically reduce or eliminate high occurrence of road accident fatalities. The designed Cusum control chart can be adapted for other states in the country and also for the larger society for detecting the periods when the rate of death as a result of road accidents was prevalent.

[This abstract is based on the author's abstract.]Control charts used to monitor for permanent shifts in a process do not indicate the exact time when the shift occurs, making it difficult to detect the assignable cause. A method for determining the tim..

The performance of the CUSUM control chart used to monitor the performance of production processes is usually evaluated with the assumption that the process parameters are know. In practice, however, the parameters are seldom known and are often replace..

Competition between the homoneous companies cause the company have to keep production quality. To cover this problem, the company controls the production with statistical quality control using control chart. Shewhart control chart is used to normal distributed data. The production data is often non-normal distribution and occured small process shift. Grand median control chart is a control chart for non-normal distributed data, while cumulative sum (cusum) control chart is a sensitive control chart to detect small process shift. The purpose of this research is to compare grand median and cusum control charts on shuttlecock weight variable in CV Marjoko Kompas dan Domas by generating data as the actual distribution. The generated data is used to simulate multiplier of standard deviation on grand median and cusum control charts. Simulation is done to get average run lenght (ARL) 370. Grand median control chart detects ten points that out of control, while cusum control chart detects a point out of control. It can be concluded that grand median control chart is better than cusum control chart.

The design of a CUSUM control chart typically involves choosing the subgroup size and sampling interval to achieve specified control chart performance. One can maintain a specified sampling intensity by using small subgroups at frequent intervals or larger subgroups at infrequent intervals. We use the average time to signal to investigate the performance of a CUSUM chart under alternative selections of the subgroup size and sampling interval. Recommendation are provided for both initial-state and steady-state performance.

We will deal with financial proceedings of the company using methods of SPC (Statistical Process Control), specifically through time series control charts. The paper will outline the intersection of two disciplines which are econometrics and statistical process control. The theoretical part will discuss the methodology of time series control charts and in the research part there will be this methodology demonstrated in three case studies. The first study will focus on the regulation of simulated financial flows for a company by CUSUM control chart. The second study will involve the regulation of financial flows for a heteroskedastic financial process by EWMA control chart. The last case study of our paper will be devoted to applications of ARIMA, EWMA and CUSUM control charts in the financial data that are sensitive to the mean shifting while calculating the autocorrelation in the data. In this paper, we highlight the versatility of control charts not only in manufacturing but also in managing the financial stability of cash flows., IGA TBU, Developement in usage of mathematical and statistical methods in quality management [IGA/73/FaME/10/]

One-sided CUSUM control chart have been developed for detecting shifts in the shape parameter of a Pareto distribution. It was realised that the parameters of the CUSUM chart, the lead distance and the mask angle changes considerably for a slight shift in the shape parameter of the Pareto distribution. The ARL also changes considerably for a slight shift in the parameters of the distribution.

We describe the adaptation of the decision limit cumulative sum method (cusum) to internal quality control in clinical chemistry. With the decision limit method, the cusum is interpreted against a numerical limit, rather than by use of a V-mask. The method can be readily implemented in computerized quality-control systems or manually on controls charts. We emphasize the manual application here and demonstrate how the technique can be implemented on existing Shewhart or Levey-Jennings control charts. This permits both cusum and Shewhart control rules to be used simultaneously on a single control chart and also minimizes the data calculations necessary for the cusum method. Computer simulation studies are used to determine the performance characteristics of several different cusum rules, alone and in combination with a Shewhart rule. These studies indicate that improvements in existing quality-control systems should be possible by addition of this simple cusum method and by use of a combined Shewhart-cusum control chart. This should be particularly advantageous when introducing the cusum method in laboratories with manual quality-control systems.

The aim of the present study was to automatically predict the onset of farrowing in crate-confined sows. (1) Background: Automatic tools are appropriate to support animal surveillance under practical farming conditions. (2) Methods: In three batches, sows in one farrowing compartment of the Futterkamp research farm were equipped with an ear sensor to sample acceleration. As a reference video, recordings of the sows were used. A classical CUSUM chart using different acceleration indices of various distribution characteristics with several scenarios were compared. (3) Results: The increase of activity mainly due to nest building behavior before the onset of farrowing could be detected with the sow individual CUSUM chart. The best performance required a statistical distribution characteristic that represented fluctuations in the signal (for example, 1st variation) combined with a transformation of this parameter by cumulating differences in the signal within certain time periods from one day to another. With this transformed signal, farrowing sows could reliably be detected. For 100% or 85% of the sows, an alarm was given within 48 or 12 h before the onset of farrowing. (4) Conclusions: Acceleration measurements in the ear of a sow are suitable for detecting the onset of farrowing in individually housed sows in commercial farrowing crates.

A cumulative sum control chart scheme is designed for the detection of the outbreak of an epidemic to demonstrate the use of CUSUM chart in the non-manufacturing sector. The designed schemes are then applied on data on diabetic disease to illustrate the detection of outbreak of epidemic and also a retrospective analysis is carried out and the local means are computed