Nonparametric control charts for bivariate high-quality processes

For attribute data with (very) low rates of defectives, attractive control charts can be based on the maximum of subsequent groups of r failure times, for some suitable r≥1, like r=5. Such charts combine good performance with often highly needed robustness, as they allow a nonparametric adaptation already for Phase I samples of ordinary size. In the present paper we address the problem of extending this approach to the situation where two characteristics have to be monitored simultaneously. Generalization to the multivariate case is straightforward

A Time Truncated Moving Average Chart for the Weibull Distribution

A control chart of monitoring the number of failures is proposed with a moving average scheme, when the life of an item follows a Weibull distribution. A specified number of items are put on a time truncated life test and the number of failures is observed. The proposed control chart has been evaluated by the average run lengths (ARLs) under different parameter settings. The control constant and the test time multiplier are to be determined by considering the in-control ARL. It is observed that the proposed control chart is more efficient in detecting a shift in the process as compared with the existing time truncated control chart. ? 2013 IEEE.11Ysciescopu

A Binary Control Chart to Detect Small Jumps

The classic N p chart gives a signal if the number of successes in a sequence of inde- pendent binary variables exceeds a control limit. Motivated by engineering applications in industrial image processing and, to some extent, financial statistics, we study a simple modification of this chart, which uses only the most recent observations. Our aim is to construct a control chart for detecting a shift of an unknown size, allowing for an unknown distribution of the error terms. Simulation studies indicate that the proposed chart is su- perior in terms of out-of-control average run length, when one is interest in the detection of very small shifts. We provide a (functional) central limit theorem under a change-point model with local alternatives which explains that unexpected and interesting behavior. Since real observations are often not independent, the question arises whether these re- sults still hold true for the dependent case. Indeed, our asymptotic results work under the fairly general condition that the observations form a martingale difference array. This enlarges the applicability of our results considerably, firstly, to a large class time series models, and, secondly, to locally dependent image data, as we demonstrate by an example

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Leachate treatment by conventional coagulation, electrocoagulation and two-stage coagulation (conventional coagulation and electrocoagulation)

Leachate is widely explored and investigated due to highly polluted and difficult to treat. Leachate treatment commonly involves advanced, complicated and high cost activities. Conventional coagulation is widely used in the treatment of wastewater but the sludge production becomes the biggest constraint in this treatment. Electrocoagulation is an alternative to conventional method because it has the same application but produce less sludge and requires simple equipment. Thus, combination of conventional coagulation and electrocoagulation can improve the efficiency of coagulation process in leachate treatment. This article is focusing on the efficiency of single and combined treatment as well as the improvement made by combined treatment. Based on review, the percentage reduction of current density and dose of coagulant was perceptible. As much 50% reduction of current density, duration of treatment, and dose of coagulant able to be obtained by using combined treatment. This combined treatment is able to reduce the cost and at the same time reduce the duration of treatment. Hence, the combined treatment offers an alternative technique for landfill leachate treatment on the removal of pollutants

A New S-2 Control Chart Using Multiple Dependent State Repetitive Sampling

The combined application of multiple dependent state sampling and the repetitive group sampling (RGS) is an efficient sampling scheme for industrial process monitoring as it combines the advantages of both the sampling schemes. In this paper, a new variance control chart has been proposed, when the interesting quality characteristic follows the normal distribution using the combination of these two efficient sampling schemes called multiple dependent state repetitive sampling. The control chart coefficients and parameters have been estimated through simulation for the in-control process by considering the target in-control average run lengths under different process settings. The efficiency of the proposed chart has been determined by computing the out-of-control ARL for different shift levels. The advantages of the proposed monitoring scheme have been discussed and compared with the existing RGS scheme and the single sampling scheme. A simulated example and a real industrial data have been included to demonstrate the application of the proposed monitoring scheme. It has been observed that the proposed chart is a valuable addition to the toolkit of the quality monitoring personnel.11Ysciescopu

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo