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The exponentially weighted moving average (EWMA) control chart is good enough to capture a small/moderate shift in the process-mean as it has the ability to provide good detection of a said shift b...

An Intrabody Nanonetwork (IBNN) is constituted by nanoscale devices that are implanted inside the human body for monitoring of physiological parameters for disease diagnosis and treatment purposes. The extraordinary accuracy and precision of these nanoscale devices in cellular level disease diagnosis and drug delivery are envisioned to advance the traditional healthcare system. However, the feature constraints of these nanoscale devices, such as inadequate energy resources, topology-unawareness, and limited computational power, challenges the development of energy-efficient routing protocol for IBNNs. The presented work concentrates on the primary limitations and responsibilities of IBNNs and designs a routing protocol that incorporates characteristics of Exponential Weighted Moving Average (EWMA) Based Opportunistic Data Transmission (EWMA-ODT) and Artificial Colony Algorithm Based Query Response Transmission (ABC-QRT) approaches for efficiently handling the routing challenges of IBNNs. In EWMA-ODT, the moving Nano Biosensors (NBSs) employ the EWMA method attributes to aggregate detected data by assigning high weightage to the recent detected information. Later, the aggregated data is transmitted to the Nano Router (NR) when the direct data transmission opportunity is available, the reception of aggregated briefs NR about the condition of the network after the last successful interaction with minimum energy consumption. Whereas, the ABC-QRT approach introduces the ABC algorithm for the selection of those optimal NBSs that have maximum fitness value for satisfying the data transmission demand of the external healthcare system with minimal traffic overhead. The simulation results validate that the joint contribution of these approaches enhances IBNNs lifetime and reduces end-to-end delay as compared to the flooding scheme.

In this article, we develop a mixed EWMA-CUSUM control chart for monitoring moderate and/or small shift in the process following the Conway Maxwell Poisson (COM-Poisson) distribution. To determine ...

Random causes of variation are part of every process and are harmless to product quality characteristics, while special cause variations appear due to a fault in the process and need special attention. The adaptive exponential weighted moving average (EWMA) control chart based on the cumulative sum (CUSUM) accumulation error is proposed in this study. The aim of this study is detecting imbalanced mixed range (different sizes) shifts (special cause) in process dispersion. The Huber and Tukey bi-square functions are used to enhance the proposed chart efficiency to simultaneously detect these small to large shifts. To check the sensitivity of the proposed chart, numerical results are provided through Monto Carlo simulations. The average run length (ARL) for single shift performance and extra quadratic loss (EQL), relative average run length (RARL) and performance comparison index (PCI) measures are used for overall performance evaluation of the proposed chart. The proposed scheme is compared to existing charts in the literature to determine superiority. For practical purposes, the guidelines are provided using real-life data for their practical implementation.

This paper addresses the problem of fault detection and isolation (FDI) in wind turbine benchmark model using data driven and multi-class support vector machine (SVM) approach. Since, the fault detection is fundamental for any active system, isolation is similarly vital, and identification is decisive for fault reconfiguration as well as maintenance addition to monitoring purposes. The need for man-made dynamic system to work automatically when sensor, actuator, or system faults occur was constantly developed in order to increase reliability and decrease unavailability and maintenance costs. The key step of our approach based on extraction of mean features from sensors measurements by applying the statistical methods such as moving standard deviation and the exponential weighted moving average (EWMA). The fault detection step is invoked later based on the multi-class SVM classifier that decides the presence or not of the fault. Another important contribution of this paper is the simulation of combined sensor and actuator faults simultaneously for the first time in wind turbine benchmark model. The FDI performances are illustrated through simulation study for seven different scenario tests. The results demonstrate clearly the effectiveness of statistical and SVM approach to detect and isolate single, multiple sensor and actuator faults and outperforms many results reported in the literature for solving this problem.

A type of hybrid exponential weighted moving average (HEWMA) control chart is presented by using two-parametric ratio estimator to strengthen the performance of control chart for detecting shift in process mean at phase-II. Auxiliary information is incorporated by assuming the bivariate normal distribution for study variable Y and auxiliary variable X. Monte Carlo simulation method is used to calculate the average run length of the proposed control chart. In order to detect the small and moderate shift, the proposed control chart performed better than existing HEWMA control chart.

Fouling is a principal constraint of membrane bioreactors (MBRs). It blocks the wide use of MBRs and aggravates the ability of MBRs. Trans-membrane pressure (TMP) is measured simply from MBRs and is a useful factor for evaluating fouling phenomena such as fouling mechanisms. Fouling mechanism diagnosis based on a measured TMP was used to evaluate MBRs operation conditions. However, diagnosis of MBR conditions is difficult due to the dynamic conditions of MBRs. Therefore, we used differential calculus, exponential weighted moving average (EWMA) and fast Fourier transform (FFT) to determine a periodic pattern for diagnosing fouling mechanisms in the dynamic operating conditions of MBRs. The periodic pattern was reflected in the operating conditions of MBRs, based on the fouling mechanism using TMP. We used two data sets obtained from pilot-scale MBR to suggest a periodic pattern and validated the proposed method using a lab-scale MBR experiment. Consequently, the suggested periodic pattern can diagnose fouling mechanisms using the proposed method, because the methods can be adjusted under the dynamic conditions of MBRs.

Various control charts have been proposed to monitor generalized linear profiles in Phase II. However, robustness of the proposed methods in detecting different types and especially different directions of changes is not well-studied in the literature. In real-world applications different kinds of changes such as drift and multiple change are likely to happen which can be isotonic (increasing) or antitonic (decreasing). This paper studies the robustness of Rao Score Test (RST) method, T2, and multivariate exponential weighted moving average (MEWMA) in different types, drift and multiple, and directions of changes. Rao Score Test method also benefits from a change-point detection approach whose performance is studied as well. According to the results, generally RST method shows a better performance in detecting different types of changes. Moreover, the performance of the RST method is robust to direction of the change, while T2 and MEWMA are not ARL-unbiased and show different performances under isotonic and antitonic changes. Therefore, to address this issue, we proposed a bias-reduced estimator to be used in T2. Our results demonstrate that the proposed control chart outperforms T2 and is less biased than T2. Finally, a real-world problem is presented in which aforementioned methods are applied to real data.

When the classical H∞ algorithm (HIF) is applied to estimate the state of charge (SOC) of a lithium battery, the influence of historical data is usually ignored, resulting in an increase in the estimation error. In order to improve the accuracy of SOC estimation, this paper proposes an extended exponential weighted moving average H∞ algorithm (EE-HIF) in view of the influence of historical data. By designing the Gaussian function, the weighted distribution of the data at different times can effectively reduce the estimation error caused by the inaccuracy of the lithium battery model. In addition, when the system contains Gaussian white noise and alternating current input, the proposed method can achieve a faster convergence speed and better robustness. Simulation results show the advantages of the proposed algorithm, as compared to an HIF filtering algorithm and an exponentially weighted moving average H∞ algorithm (EWMA).

In this article, we discuss finding the optimal k of (i) kth simple moving average, (ii) kth weighted moving average, and (iii) kth exponential weighted moving average based on simulated MA(q) model. We run a simulation using the three above examining methods under specific conditions. The main finding is that, 5th Exponential Weighted Moving Average (5-th EWMA) Autoregressive Integrated Moving Average (ARIMA) model is the best forecasting model among others, which means the optimal k = 5. For Turkish Telecommunications (TTKOM), stock market real data reveals the similar results of the simulation study. © 2017 Taylor & Francis Group, LLC.

A single control chart is very famous to control assignable causes that shift the process because of variations in parameters (e.g., location and dispersion). Simultaneous monitoring of processes is another popular approach used for the bilateral processes. In this study, we have proposed the mixed control charts for simultaneously monitoring of process location and dispersion parameters. We have used the idea of mixed exponential weighted moving average and cumulative sum charts and designed the charting structures for simultaneous monitoring. The proposals are compared with several existing counterparts. The comparisons reveal numerous advantages of the proposed charts over the other existing scheme. The practical application of the proposed charts is also highlighted using an illustrative example based on a real dataset. Copyright © 2016 John Wiley & Sons, Ltd.

The authors use a logistic regression model, a type of generalized linear model (GLM), to represent the functional relationship between the binary response and explanatory variables, which are assumed random with specified in-control distributions.

With the advancement in the science and technology, cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources. Load balancing of cloud computing environments is an important matter of concern. The migration of the overloaded virtual machines (VMs) to the underloaded VM with optimized resource utilization is the effective way of the load balancing. In this paper, a new VM migration algorithm for the load balancing in the cloud is proposed. The migration algorithm proposed (EGSA-VMM) is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory. In our approach, the migration is done based on the migration cost and QoS. The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA. The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization, which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.

Control schemes that are simple enough for operators to implement and that are robust with respect to the underlying model assumptions are usually sought in the area of process adjustment. In the l...