A Process Capability Analysis Method Using Adjusted Modified Sample Entropy

Citation: Koppel, S., & Chang, S. I. (2016). A Process Capability Analysis Method Using Adjusted Modified Sample Entropy. Procedia Manufacturing, 5, 122-131. doi:10.1016/j.promfg.2016.08.012The evolution of sensors and data storage possibilities has created possibilities for more precise data collection in processes. However, process capability analysis has become more difficult. Traditional methods, such as process capability ratios, cannot handle large volumes of process data over time because these methods assume normal process distribution that is not changing. Entropy methods have been proposed for process capability studies because entropy is not dependent on distribution and can therefore provide accurate readings in changing distribution environments. The goal of this paper is to explore the use of entropy-based methods, specifically modified Sample Entropy to identify process variations over time. A study based on simulated data sets showed that the proposed method provides process capability information. © 2016 The Author

A research on the use of greywater in aquaponic systems

Magistritöö Vesiehituse ja veekaitse õppekavalPuhta joogivee nappuses kannatavad paljud Euroopa riigid, kaasa-arvatud Eesti mõningad piirkonnad suvekuudel. Magistritöö eesmärk on projekteerida Interreg Transfarmi projekti (TRANSborder cooperation for circular soil-less FARMing) tarbeks Eesti Maaülikooli spordihoonesse katseseade, mille abil saadakse hallveest taasksutusvesi, uuritakse ja katsetatakse hallvee taaskasutamise võimalusi akvapoonilise süsteemi veega varustamisel. Magistritöö koostamise käigus mõõdistati katseseadme asukohaks valitud ruum ning koguti projekteerimiseks vajalikud lähteandmed. Saadud andmete põhjal projekteeriti hallvee kogumissüsteem ja valiti hallveepuhastusseade ning projekteeriti akvapooniline süsteem. Projekteeritud akvapoonililises süsteemis on kavandatud kasutada hallvee puhastamisel saadud taaskasutusvett. Magistritöö koostamise käigus ehitati ja testiti esimest osa katseseadmest, et veenduda hallvee puhastamise tulemusel saadava taaskasutusvee sobivuses kasutamiseks akvapoonilises süsteemis. Tulemustest selgus, et puhastatud hallvesi sobib kasutamiseks akvapoonilises süsteemis.Many European countries, including some parts of Estonia, suffer from a shortage of clean drinking water especially during summer months. The aim of the master´s thesis is to design a pilot plant for the Interreg Transfarm project (TRANSborder cooperation for circular soil-less FARMing) in the sports building of the Estonian University of Life Sciences, which will be used to produce greywater reuse water, to study and test the possibilities of greywater reuse in aquaponic systems. During the preparation of the thesis, the space chosen for the test facility was surveyed and the basic design data were collected. On the basis of the data obtained, a greywater collection system was designed, a greywater treatment unit was selected and an aquaponic system was designed. The designed aquaponic system is designed to use the recycled water from greywater treatment. During the preparation of the thesis, the first part of the pilot plant was built and tested to verify the suitability of the recycled water from greywater treatment for use in the aquaponic system. The results showed that the purified greywater is suitable for use in an aquaponic system

A big data computational framework for enterprise level statistical process monitoring

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringShing I. ChangThe emergence of big data storage together with the evolution of sensor technologies has expanded the amount of data that complex manufacturing facilities can produce. Almost all process variables in the factory can be measured and the data can be stored in data lakes in cloud servers. This big data phenomenon has presented challenges and opportunities for quality improvement teams. While the traditional control charts are still widely used, they are often isolated tools for monitoring product quality characteristics scattered in a manufacturing system. The need to monitor full systems becomes even more pressing with the emergence of smart factories in the next industrial revolution called Industry 4.0. The goal of this research is to develop a big data computational framework for enterprise-level process monitoring that tracks different variables simultaneously and provides near-time system status updates. To achieve this goal, a novel methodology called Technique of Uniformally Formatted Frequencies (TUFF) is developed that standardizes continuous, discrete and profile variables into comparable statistics, classifies these statistics into four colors using ideas from pre-control charts and summarizes these colors to a single frequency table. This table is used to compare the current situation to historic data and to decide if the performance of the system has changed. A higher resolution of the results identifies the temporal and spatial location of possible change. The comprehensive monitoring method uses all the available data and monitors both quality characteristics as well as process parameters near-time. Additionally, the method is easily scalable to handle big data level datasets. Extensive simulation studies identify the sensitivity and other characteristics of the TUFF method. This dissertation also redefines one of the more popular Six Sigma continuous improvement methods of DMAIC (Define, Measure, Analyze, Improve, and Control) for the manufacturing environment. The redefined method is Measure, Define, Analyze, Improve and Control (MDAIC), where the unit in need of improvement is identified automatically by the data. The research integrates the TUFF statistical system monitoring method to the MDAIC framework and provides a solution for the implementation of the method in a big data environment based on the MapReduce algorith

Retrospective analysis for phase I statistical process control and process capability study using revised sample entropy

[[abstract]]This study explored a new nonparametric analytical method for identifying heterogeneous segments in time-series data for data-abundant processes. A sample entropy (SampEn) algorithm often used in signal processing and information theory can also be used in a time series or a signal stream, but the original SampEn is only capable of quantifying process variation changes. The proposed algorithm, the adjusted sample entropy (AdSEn), is capable of identifying process mean shifts, variance changes, or mixture of both. A simulation study showed that the proposed method is capable of identifying heterogeneous segments in a time series. Once segments of change points are identified, any existing change-point algorithms can be used to precisely identify exact locations of potential change points. The proposed method is especially applicable for long time series with many change points. Properties of the proposed AdSEn are provided to demonstrate the algorithm’s multi-scale capability. A table of critical values is also provided to help users accurately interpret entropy results.[[notice]]補正完