Applied Mechanics and Materials Vol. 693 (2014) pp 123-128 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.693.123
The Options of Using Data Mining Methods in Process Control Alena Kopcekova1, a, Michal Kopcek2,b, Pavol Tanuska3,c 1,2,3
Institute of Applied Informatics, Automation and Mathematics, Faculty of Materials Science and Technology in Trnava, Slovak University of Technology in Bratislava, Paulínska 16, 917 24 Trnava, Slovak Repulic a
[email protected],
[email protected],
[email protected]
Keywords: business intelligence, data mining, process control level
Abstract. The term business intelligence (BI) represents the tools and systems that play a key role in the strategic planning process of the corporation. These systems allow a company to gather, store, access and analyze corporate data to aid in decision-making. Necessary fundamental definitions are offered and explained to better understand the basic principles and the role of this technology for a company management. The proposed article is logically divided into more sections, where the stages of basic research in the field of data mining are described gradually. This involves the definition of the technology and the list of main advantages and analytical methods incorporated in online analytical processing. Also some typical applications of above mentioned particular methods are introduced. The focus of this paper is to introduce the options of using the data mining methods on the control systems level within the hierarchical control systems model. Introduction The current distributed control systems are built as many processor control systems with horizontal and vertical communication. These systems contain elements of physical and logical distribution, which is a direct hierarchical relationships are changing the network. The emergent trends are strongly manifested, which means that combining previously independent systems may lead to the formation of new properties as a whole. [1] Therefore, the process control is currently realized by using control systems with hierarchical structure. Generally accepted model for complex control of processes represents a so called pyramidal model, which is shown in Fig. 1
Fig. 1: The pyramidal model of the distributed control [2] The process level of control forms the basic interface with the production. These include production lines, machines and equipment in which the sensors, contactors, actuators are integrated
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that communicate using industrial networks with control systems, programmable logic controllers PLC, programmable industrial controllers PPR and other. The production of control is the higher level of control which integrates the acquisition and integration of process data, processes measurements, signalizes commands and alarm messages, monitoring and visualization of real-time information, evaluation and direct operational interference into processes. Information systems at the production level of control are working in real time, but with a lower sampling period than systems at the process level. Application from the MES level has to provide the aggregation of data arriving in real time from the process level into form and time resolution corresponding with ERP system, as well as data storing and the transaction with ERP. The management level of control covers the previous layers. At this level the data are archived and processed through the data pump and they are prepared for applying business intelligence systems. Long-term strategic decisions for production are made through these systems. [3] Communication between process level and production level is realized in a real time and the individual response times are in range from milliseconds to seconds. This type of communication is two-way. The information flow to the next hierarchical level of control is formed from analogue inputs and outputs, digital inputs and outputs, alarms and indirect measurements. The control parameters, set-points, configuration parameters of the PLC and PPR are transmitted in the opposite direction. Data are collected in real time (there are different sampling periods), which is an enormous amount of data to be processed and possibly stored for the potential need for further use. Communication between production and management levels takes place mostly between MES and ERP systems (including systems CRM or SCM) as shown in the figure 2.
Fig. 2: The content of the data interface between MES and ERP [4] The data from the real-time information system are transmitted to the ERP systems in the transaction mode. Due to the huge and the surplus amount of data arising at all levels of the control system, it is necessary to find new effective ways to store extreme volumes of data and then extract useful information for the process control. Using the conventional approach to the processing of data in the pyramidal model of the process control is not possible to solve these problems. The real solutions are offered in the use of the special data storages and in the application of modern methods and approaches to the processing of data in these storage sites. [2]
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Business Intelligence (BI) is the term for information technologies, applications and methods for the collection, normalization, analysis, presentation and interpretation of the data. The objective of BI is to support decision-making and planning in various areas of management. Deploying BI in enterprises and organizations helps to direct the organization towards its main objectives, enhances decision-making ability of analysts and managers, ensures efficient acquisition and distribution of essential data and statistics, finds hidden problems using information that is not visible, provides immediate answers to the questions that arise during the data study. BI uses the principle of multidimensionality. This means that the data are transformed from a relational to multidimensional database, where they are then processed by the information technology called Online Analytical Processing (OLAP). [5] The results of the analysis are summaries and reports used as a basis for decision-making and processes management. The tools of OLAP, machine learning and methods of statistics, mathematics and artificial intelligence uses data mining. The main objective of this process is to gain knowledge from existing data files and their transformation into structures understandable to humans. The data mining recognizes two fundamental tasks - predictive and descriptive as shown in Fig. 3. The aim of predictive tasks is to find relations between the data in order to forecast the future. The descriptive tasks characterize the general characteristics of the data in order to categorize them into homogeneous groups.
Fig. 3: Tasks and methods of data mining The BI technology is evolving rapidly. The latest innovation in this area is e.g. big data technology that can process extreme amounts of data in a reasonable time. The Big data, as opposed to the data warehouses, works with the unstructured data on the volume of the order of petabytes. Petabyte is 1 000 000 000 000 000 bytes. That's about 3 orders more than in data warehouses. [6] The Big Data is not just about a simple increase of claims for processing large amounts of data. It is also about other data characteristics. The term 3V is used in the available literature, which means volume, velocity and variety. Some sources also added in more V for veracity, viscosity, virality. [7] By using BI technologies the results are visible almost immediately and from different perspectives. Using Big Data platform takes more time to execute queries. Therefore the data processing often runs in the background. Then the results can be viewed using the available BI tools. The Big Data priority is not speed but the processing of large amounts of data in reasonable time.
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The enormous benefit of the Big Data is scalability. Large amounts of data are normally discarded (e.g. outputs of camera systems, sensor data etc.), because in the relational databases is no place for them and they don't have added value in an unprocessed form. Big Data technologies are able to use also these data. Within the BI this is about exact numbers, e.g. number of orders, the value of goods sold, the average profit per customer. With the Big data approach are important patterns, new discoveries and relations. [8] Summary of the Analysis. Many authors in the world, but also here in Slovakia, deal with technologies, which in its activities use database systems and data warehouses, either the traditional way or for the knowledge discovery. However, most applications are in the field of management and banking, which represents the highest control level. In this section a short list of several authors and areas arising from analyzes is presented with the special focus on the overview of publications about the use of knowledge on the lower levels of control. Let's mention these Slovak authors J. Horvath, who deals with segmentation of image processing, E. Ocelíková and M. Štofko with knowledge discovery in the visualization of the information and control systems, M. Horvath uses data mining for the purposes of quality management and P. Bubeník who works with BI technology on higher levels of control. World’s authors deal mainly with solutions for the financial and telecommunications segment. Further e.g. Beranova and Köksala use knowledge discovery for quality assurance and production quality control. Authors Morello, Zhang Tong and Huang use KDD for flexible manufacturing systems and Duebel for increasing the performance of selected industrial processes. Many authors such as Fayyad, Giudici, Figini, Vercelli are focused on the DM and KDD in general. It is obvious that in general there is a desire to use the advantages offered by BI technology not only at the highest level, but also at other levels of the hierarchical control systems. The application of BI technology at lower levels of the hierarchical control systems is still very little explored area, which offers a very wide space for research. Materials and Methods In the introduction the technology (process) level of information and control systems of technological processes and production technologies was defined. As mentioned, the primary data acquisition from the controlled process and the realization of control actions with a period of the order of tens to hundreds of milliseconds take place at this level. There arises a large amount of unstructured data in a real time, which can be a potential source of knowledge. The conventional analytical optimization methods of control and economic processes normally available at the production level do not offer the necessary capacity and performance needed for the processing of such amounts of data. New opportunities are brought by the technology Business Intelligence and especially Big Data, which were explained in more detail above. These technologies, however, are working at the highest (managament) level of the hierarchical model, where the period of information processing and realization of control actions is of the order of hours to days. Based on the facts mentioned above, the main problem of the research could be defined as follows: "Within the whole hierarchy of control systems an extremely large amount of data containing potential knowledge is produced. However, this knowledge is not used for the needs of the hierarchical process control." Objectives of the Research. Based on the formulated problem, the fundamental objective of the research could be defined: "The usage of the process of the knowledge discovery from specific data stores of heterogeneous data in a real time and subsequent use of this knowledge in the hierarchical process control."
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This fundamental objective can be further developed into the following sub-objectives: • Analysis of the data generated during the operation of the technological and production processes in order to identify problems and dependencies arising at lower levels of control. • Identification of the application possibilities of methods and techniques of data-mining for the need of the control systems on the lower levels of technological process control. • Design of a data mining model using appropriate methods and techniques of data mining. • Verification and validation of the designed data mining model. • Generalization of the draft to a methodology to improve the selected attributes of control. Fundamental solution requirements: • Integrate the heterogeneous data of the hierarchical control system for further processing. • Maintain the data integrity within the extraction and processing of data from production databases. • Design the data storage while taking into account the specifics of the hierarchical control system. • Appropriately and correctly interpret the acquired knowledge. • Generalization of the draft to a methodology for the purpose of possible implementation into the real production process. The information flow from the level of knowledge discovery to the process level may include: • control algorithms parameters, • balance calculations values, • static and dynamic model parameters, • parameters useful for the equipment diagnostics – the maintenance support, • records for ensuring the product quality etc. Predicted Benefits of the Research. Solution of these objectives forms the basis for the development of a complex standalone system of knowledge discovery for the hierarchical process control, which role is to assist in solving the following problems: • Prediction of failure states of the controlled process based on the principle of finding an analogous situation by processing of large amounts of data in a real time. • Prediction of preventive checks of the production equipment, which is associated with maintenance. • Identification of the influence of process parameters on the production process. • Diagnosis of manufacturing systems with respect to the life cycle of these systems. • Identification and optimization of relevant control parameters which have an impact on increasing the safety of the industrial processes control. • Incorrect actions of actuators, such as lack of accomplishment of the calculated control action. • Improve specification of the nonlinear dynamic models of control processes to optimize theirs parameters. [9] • Continuous monitoring of the process control quality based on the quality evaluation from the on line obtained data. • The error detection of the production machines as well as the individual products - detecting the occurrence of rejects. • Identification of the various non-standard conditions that have an impact on the production process and which have to be solved by the production operator often by an unplanned shutdown of a machine or a part of technology. • Solve problems using knowledge gained without pre-specified aim. • Effective implementation and innovation of the control systems at all levels.
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Results and Discusion The Business Intelligence represents the top of the hierarchical model of business management and information systems and it is a powerful tool that helps to enhance the quality, reliability, safety and efficiency of business processes. The Business Intelligence is used to discover knowledge through processing of large amounts of data. Thanks to these knowledge, analysts and managers have important information about the activities of the company and views into the processes within the company. The hierarchical control systems of technological processes and production technologies represent a source of huge amounts of data at all levels. These data are the source of a number of different knowledge. Given the nature and the diversity of the BI technology tools has the potential to discover, interpret and implement knowledge also at lower levels of the pyramidal model of process control systems. The BI tools could find application, e.g. in optimizing the control parameters and the control of complex nonlinear technological processes, in early warning systems or other systems using mass storage of the historical data. Further work will be focused at the gradual achievement of the goals listed above. References [1] D. Mudrončík, Softvér riadiacich systémov, STU Bratislava, 2000. [2] P.Tanuška, Získavanie znalostí z databáz v hierarchickom riadení technologických a výrobných procesov, Tézy k inauguračnej prednáške, 2013 [3] J. Jadlovský a kol., Návrh distribuovaného systému riadenia pružnej výrobnej linky, In: International Conference Cybernetics and Informatics , Vyšná Boca, 2010. [4] H. Meyer, F. Fuchs, K. Thiel, Manufacturing Execution Systems: Optimal Design, Planning, and Deployment. McGraw-Hill Professional, ISBN: 0071623833, 2009. [5] M. Liška, Dobívaní znalostí z databází, Ostrava,2008. [6] O. Dolák, Big data, Nové způsoby zpracování a analýzy velkých objemů dat 2011. http://www.systemonline.cz/clanky/big-data.htm [7] I. Claverie-Berge, Isabelle, Solutions Big Data IBM 2012. http://www-05.ibm.com/fr/events/netezzaDM_2012/Solutions_Big_Data.pdf [8] T. Kuzár, Tradičný Business Intelligence vs. Big data. Blog, DWH/BI 2012. http://robime.it/tradicny-business-intelligence-vs-bigdata/#more-2253 [9] R. Vrábeľ, M. Abas, Frequency control of singularly perturbed forced duffing´s oscillator. registrovaný: Web of Science, Master Journal List, Scopus. In: Journal of dynamical and control systems. - ISSN 1079-2724. - Vol. 17, Iss. 3 ,s. 451-467, 2011.
