Design and Application of Risk Adjusted Cumulative Sum (RACUSUM) for Online Strength Monitoring of Ready Mixed Concrete

The Cumulative Sum (CUSUM) procedure is an effective statistical process control tool that can be used to monitor quality of ready mixed concrete (RMC) during its production process. Online quality monitoring refers to monitoring of the concrete quality at the RMC plant during its production process. In this paper, we attempt to design and apply a new CUSUM procedure for RMC industry which takes care of the risks involved and associated with the production of RMC. This new procedure can be termed as Risk Adjusted CUSUM (RACUSUM). The 28 days characteristic cube compressive strengths of the various grades of concrete and detailed information regarding the production process and the risks associated with the production of RMC were collected from the operational RMC plants in and around Ahmedabad and Delhi (India). The risks are quantified using a likelihood based scoring method. Finally a Risk Adjusted CUSUM model is developed by imposing the weighted score of the estimated risks on the conventional CUSUM plot. This model is a more effective and realistic tool for monitoring the strength of RMC.

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

Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation

The development of egg hatching and storage machines equipped with cooling and heating systems and iot

The development of egg hatching and storage machines equipped with cooling and heating systems and IoT was for helping chicken breeders to address the issue of chicken production shortages. To produce large numbers of poultry production, eggs hatching is one of the major step that needs to pay attention to. There are several reasons why egg hatching process fails, such as lack of care by hen, eaten by rooster, and unsuitable hatching environment and temperature. In addition, if the eggs are not incubated within 1 week, the eggs will be damaged having producing a hatching machine and egg storage can help the chicken breeders to produce a better amount of chicken production. Internet of Things (IoT) elements such as the Arduino and Blynk are also used to make this egg hatching and storage machine attractive and to meet the needs and requirements of users. The objectives of this study were to design, develop and evaluate the functionality of egg hatching and storage machines in combination with cooling and heating systems along with IoT. Methodology is a technique and method that incorporates methods and approaches used to achieve the objectives and objectives of the study. The model used is the ADDIE model which consists of 5 phases namely Analysis, Design, Development, Implementation, and Evaluation. This product has received expert confirmation in terms of design and functionality. The results show that the egg hatching and storage machine is well developed and can attract users when using this hatching and storage machine

Characterization of Model-Based Detectors for CPS Sensor Faults/Attacks

A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, we derive tools for tuning the CUSUM procedure in the fault/attack free case to fulfill a desired detection performance (in terms of false alarm rate). We use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, we find the upper bound of state degradation that is possible by an undetected attacker. We quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations of a chemical reactor with heat exchanger are presented to illustrate the performance of our tools.Comment: Submitted to IEEE Transactions on Control Systems Technolog

Developing Quality Control Charts for the Control Points of a Food Product

Monitoring the production process is a critical issue for improving the quality of product and for reducing the costs regarding external failures. Quality control charts are often used to visualize measurements on the process during the monitoring activities. This paper presents a case study based on the use of advanced charts, Cumulative Summation (CUSUM) and Estimated Weighted Moving Average (EWMA) charts, for visualizing the control points of a particular chicken product in fast-food industry. Furthermore, GM (1,1) and GM (1,1) Markov models were built to generate predictions to see the trends and future values to maintain a follow-up procedure for the fluctuations in the process performance. In this context, three control points are considered that are weight of the chicken wings, sterilizer temperature, and grid-pan temperature. The findings provide a significant feedback for the efficiency of the corresponding processes. Results show that the methodology selected to develop these charts has an important impact on creating an effective quality control process

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