Explore the vast range of titles available.

Control charts are extensively used to monitor the production process. When there is more than one variable process are considered, the multivariate control charts are typically employed to monitor the mean vector and the variability process separately. In recent years, control charts have been developed for monitoring mean process and variability process simultaneously in a chart. A Maximum multivariate control chart (Max-Mchart) is one of the simultaneous multivariate control charts relying on exact distribution control limit. The objective of this paper is to evolve Max-Mchart based on Bootstrap control limits. This paper also compares Max-Mchart over the Hotelling T2 and Generalized Variance (GV) control chart. The interpretive examples are implemented to demonstrate the applications of the ZA fertilizer production dataset in carbonation step.

In this article, we consider the problem of monitoring Poisson rates when the population sizes are time-varying and the nominal value of the process parameter is unavailable. Almost all previous control schemes for the detection of increases in the Poisson rate in Phase II are constructed based on assumed knowledge of the process parameters, for example, the expectation of the count of a rare event when the process of interest is in control. In practice, however, this parameter is usually unknown and not able to be estimated with a sufficiently large number of reference samples. A self-starting exponentially weighted moving average (EWMA) control scheme based on a parametric bootstrap method is proposed. The success of the proposed method lies in the use of probability control limits, which are determined based on the observations during rather than before monitoring. Simulation studies show that our proposed scheme has good in-control and out-of-control performance under various situations. In particular, our proposed scheme is useful in rare event studies during the start-up stage of a monitoring process. Supplementary materials for this article are available online.

Shewhart charts are the most commonly utilised control charts for process monitoring in industries with the assumption that the underlying distribution of the quality characteristic is normal. However, this assumption may not always hold true in practice. In this paper, the weighted-variance mean charts are developed and their population standard deviation is estimated using the three subgroup scale estimators, namely the standard deviation, median absolute deviation and standard deviation of trimmed mean for monitoring Weibull distributed data with different coefficients of skewness. This study aims to compare the out-of-control average run length of these charts with the pre-determined fixed value of the in-control ARL in terms of different scale estimators, coefficients of skewness and sample sizes via extensive simulation studies. The results indicate that as the coefficients of skewness increase, the charts tend to detect the out-of-control signal more rapidly under identical magnitude of shift. Meanwhile, as the size of the shift increases under the same coefficient of skewness, the proposed charts are able to locate the shifts quicker and the similar scenarios arise as a sample size raised from 5 to 10. A real data set from survival analysis domain which, possessing Weibull distribution, was to demonstrate the usefulness and applicability of the proposed chart in practice.

For the grid-connected doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS), many improved control algorithms have been developed for the rotor-side converter (RSC) to suppress the overcurrents in the rotor-side under voltage dips. However, such objective can hardly be achieved under severe grid fault conditions because of the limitation of RSCs output voltage. An analysis tool is proposed to estimate the the theoretical control limit of the RSC in suppressing the short-circuit rotor currents during grid faults in this study. The tool is based on the optimisation theory and takes the practical constraints of the RSC into account. To execute the analysis, a simplified DFIG model with decoupled stator and rotor fluxes is presented, and the low-voltage ride through (LVRT) problem can be formulated as an optimisation problem, which intends to suppress the rotor winding currents with voltage constraints. The Pontryagin's minimum principle is employed to solve the optimisation problem and the results can identify the control limit of the RSC. A case study based on a typical 1.5 MW DFIG-based WECS under various grid voltage dips is carried out to validate the analytical method. The proposed method is also further verified by experimental tests on a scaled 3 KW DFIG system. The results are expected to help the manufacturers to assess and improve their RSC controllers or LVRT measures.

This paper presents an approach for improving the control limits of X ¯ control charts when the parameters of the process are estimated and the control chart is in operation. In these conditions, the observed average run length (ARL) may be very different from the planned ARL since the parameter estimates may have a larger error. To minimize this problem, the data collected in effective control (phase 2) will be used to re-estimate the parameters with a precision greater than that obtained in phase 1. Thus, we defined a minimum sample size of observations of phase 2, which is constituted of a mixture of two normal distributions that should be used to re-estimate the process parameters. The proposal is illustrated with numerical example.

Differential dynamic programming (DDP) is a direct single shooting method for trajectory optimization. Its efficiency derives from the exploitation of temporal structure (inherent to optimal control problems) and explicit roll-out/integration of the system dynamics. However, it suffers from numerical instability and, when compared to direct multiple shooting methods, it has limited initialization options (allows initialization of controls, but not of states) and lacks proper handling of control constraints. In this work, we tackle these issues with a feasibility-driven approach that regulates the dynamic feasibility during the numerical optimization and ensures control limits. Our feasibility search emulates the numerical resolution of a direct multiple shooting problem with only dynamics constraints. We show that our approach (named Box-FDDP) has better numerical convergence than Box-DDP+ (a single shooting method), and that its convergence rate and runtime performance are competitive with state-of-the-art direct transcription formulations solved using the interior point and active set algorithms available in Knitro. We further show that Box-FDDP decreases the dynamic feasibility error monotonically—as in state-of-the-art nonlinear programming algorithms. We demonstrate the benefits of our approach by generating complex and athletic motions for quadruped and humanoid robots. Finally, we highlight that Box-FDDP is suitable for model predictive control in legged robots.

Over the last two years, global regulatory authorities have raised safety concerns on nitrosamine contamination in several drug classes, including angiotensin II receptor antagonists, histamine-2 receptor antagonists, antimicrobial agents, and antidiabetic drugs. To avoid carcinogenic and mutagenic effects in patients relying on these medications, authorities have established specific guidelines in risk assessment scenarios and proposed control limits for nitrosamine impurities in pharmaceuticals. In this review, nitrosation pathways and possible root causes of nitrosamine formation in pharmaceuticals are discussed. The control limits of nitrosamine impurities in pharmaceuticals proposed by national regulatory authorities are presented. Additionally, a practical and science-based strategy for implementing the well-established control limits is notably reviewed in terms of an alternative approach for drug product N-nitrosamines without published AI information from animal carcinogenicity testing. Finally, a novel risk evaluation strategy for predicting and investigating the possible nitrosation of amine precursors and amine pharmaceuticals as powerful prevention of nitrosamine contamination is addressed.

Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and may result in process downtime and damaged components. With this in mind, it has long been desired to monitor tool wear/tool condition. Kernel principal component analysis (KPCA) is proposed as an effective and efficient method for monitoring the tool condition in a machining process. The KPCA-based method may be used to identify faults (abnormalities) in a process through the fusion of multi-sensor signals. The method employs a control chart monitoring approach that uses Hotelling’s T2-statistic and Q-statistic to identify the faults in conjunction with control limits, which are computed by kernel density estimation (KDE). KDE is a non-parametric technique to approximate a probability density function. Four performance metrics, abnormality detection rate, false detection rate, detection delay, and prediction accuracy, are employed to test the reliability of the monitoring system and are used to compare the KPCA-based method with PCA-based method. Application of the proposed monitoring system to experimental data shows that the KPCA based method can effectively monitor the tool wear.

Purpose – Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular, when the data illustrates uncertainty, inconsistency and is incomplete which is often the. case of real data. Traditionally, we use variable control chart to detect the process shift with real value. However, when the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional statistical process control (SPC) to monitor the fuzzy control chart. The purpose of this paper is to propose the designed standardized fuzzy control chart for interval-valued fuzzy data set. Design/methodology/approach – The general statistical principles used on the standardized control chart are applied to fuzzy control chart for interval-valued fuzzy data. Findings – When the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional SPC to monitor the fuzzy control chart. This study proposes the designed standardized fuzzy control chart for interval-valued fuzzy data set of vegetable price from January 2009 to September 2010 in Taiwan obtained from Council of Agriculture, Executive Yuan. Empirical studies are used to illustrate the application for designing standardized fuzzy control chart. More related practical phenomena can be explained by this appropriate definition of fuzzy control chart. Originality/value – This paper uses a simpler approach to construct the standardized interval-valued chart for fuzzy data based on traditional standardized control chart which is easy and straightforward. Moreover, the control limit of the designed standardized fuzzy control chart is an interval with (LCL, UCL), which consists of the conventional range of classical standardized control chart.

Control charts are essential tools for monitoring the stability of manufacturing processes. However, measurement error can reduce their effectiveness by weakening their ability to detect process shifts. This study introduces an improved version of the Generally Weighted Moving Average (GWMA) chart, called the Auxiliary Information Based GWMA with Measurement Error (AIB-GWMA-ME) chart. This new chart combines auxiliary information with a measurement error adjustment mechanism to improve monitoring accuracy. Three types of measurement error models are considered – namely, the covariate model, multiple measurements model, and linearly increasing variance model. For each model, the statistic of the AIB-GWMA-ME chart is developed, and the corresponding control limits are determined. Monte Carlo simulations are used to assess the chart’s performance based on Average Run Length (ARL). Results show that the AIB-GWMA-ME chart improves sensitivity to small shifts and performs better than existing GWMA and EWMA charts in the presence of measurement error.