USAGE OF DATAMINING METHODS FOR CONTROL SUPPORT OF CRYSTALIZER S LIFETIME. Martin ZUSKÁČ, Jiří DAVID, Lucie FRISCHEROVÁ, Romana GARZINOVÁ

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1 USAGE OF DATAMINING METHODS FOR CONTROL SUPPORT OF CRYSTALIZER S LIFETIME Martin ZUSKÁČ, Jiří DAVID, Lucie FRISCHEROVÁ, Romana GARZINOVÁ VŠB Technical University Ostrava, Faculty of Metallurgy and Materials Engineering, 17. listopadu 15, Ostrava-Poruba, Czech republic martin.zuskac@vsb.cz, j.david@vsb.cz, lucie.frischerova@vsb.cz, romana.garzinova@vsb.cz Abstract In the paper is described the method for determining an actual state of the crystallizer when continuous steel casting based on data mining methods. The signification of proposed method lies on making graphical display of the crystallizer s state in any time and without usage instruments of technical diagnostics. Keywords: Crystallizer, Lifetime, Software, Probability 1. APPROACH TO SERVICE LIFE OF TECHNICAL OBJECTS The service life of technical objects and so also industrial objects can be determined by three ways. In standard progress is start from the material features in initial state and all operation history is take into account. Whole lifetime is determined by calculation and residual lifetime will appear by subtraction the operational time from the whole lifetime. In the cases, when the operational history is unknown (for example operational temperature, number of operation hours, pressure, number of leakage tests, number of pressure tests and other), is necessary to determine residual lifetime by other way.in this case we are coming out from actual value of diagnostic quantities, which are determined by the diagnostics tests. It is bound to diagnostic measurement way, which can lie only in measurement diagnostic s quantity or will be performed by sample extraction and with relatively longtime experimental program of diagnostic test of the material. Recently when determining residual lifetime get used the same principle like in dimensioning process. It is obvious, that the residual service life is overvalued. The third approach, which is in present time spreading, is the approach, when evaluation of service life is based on connection on probabilistic approach and with usage of failure physics and study of mechanisms of failures. This approach means a complex solution, enabling include into the solution classic probabilistic approach, results of diagnostic measurement and also the essence of the lowering service life process degraded mechanism. In the phase of device usage until its outage of service, every device and its elements coming through various states. The important are these states, if the maximum level is reached, change material character - limiting state. The limiting states could be very different, from the point of its character, and also for next usage of the system. The limiting states are these ones, which can cause failure and outage. In case of unrenewed objects, the object reach the limiting state when the failure occur, or in reaching previously defined usage time, or whole operation time (for example with regard to fatigue of material). Admissible time of usage and operational time is necessary to according to branch for safety in context with decreasing of basic parameters under the certain level. In renewable objects rise a limiting state in case of:

2 - Is not possible to maintain the safety, reliability or operational efficiency at minimum level, which is previously defined, - As a result of aging or change of geometrical shapes the object reach that stat, that the eventually repair cost disproportionate costs. Definitely the limiting state means necessity to interrupt other object usage for irremovable violation of safety demands, irremovable exceeding of defined boundaries of given parameters, irremovable lowering operational efficiency under acceptable level and necessity to make object s reparations. This moment is in term of solution defined by reliability model and quantified by quantity of probability of reach of limiting state. 2. CRYSTALLIZER'S SERVICE LIFE The main elements which is a base material concerning crystallizers is copper (electrolytically clean with addition of phosphorus) and this is for its low acquisition costs and good heat dissipation. Recently was found, that more suitable is to use copper alloys with another metals, as chromium or zirconium Cu-Cr (0.5 to 0.5%) or Cu-Cr-Zr (0.7% Cr, 0.6% Zr). By this can be lowered heat dissipation Crystallizer s service life is generally expressed by length of casted steel in given crystallizer, casted steel weight or number of casted melts. Big difference is given by different volume of pans and blank profile. In the length is is about 10 to 15 thousand meters of casted blanks under normal operation. In presented solution is used the diagnostic signal of casted steel weight. From the operational practice we can defined following influences which has impact to crystallizer s service life and affection in process of casting: - Features and quality of crystallizer s material these features are explicitly given by manufacturers of crystallizer s enclosure and so it can t be affect through the casting. - The shape and size of the blanks these parameters has meaning for calculation of casted steel weight, parameters are given by crystallizer s type, and it can t be affect through the casting, - Way of cooling can be affected, but the way of cooling is given by technological parameters and is further controlled with regard to technological efficiency of the casting process, no from the point of view of crystallizer s wear. Casting speed can be affected during the casting, but must be met the same condition as previously, - Operational influences (inaccurate centring of immersion spouts regarding to metallurgical axis, casting with turbulent flow, casting with low temperature or with to high share of aluminum in steel) some of the mentioned influences can be affected, but from the general point o view these are also preferred by technological aspects, no by maintenance aspects. From the mentioned text is obvious, that maximization of crystallizer s service life is given by mutual combination of constructional technical, operational and organization parameters, which are always in concrete object. 2.1 Reliability model In the term of solution is developed a reliability model, which is expected in the shape F( t) 1 e t, (1)

3 Where F(t) is probability of reaching the limiting state when presumption, that random quantity t expressing time to limiting state will has a Weibull probability distribution; is shape parameter, and is scale parameter. Parameterization of that model then means numeration of shape parameter and value of scale parameter. Expression of the shape parameter is based on processing input data about degraded mechanisms. Which cause reaching of limiting state of the object. By - If is defined one dominant degraded mechanism, which cause limiting state on the object, then the shape parameter is the value, which is attached to given degraded mechanism. - If there are two mechanisms, respectively three of them, which has the share on limiting state occurring, then the shape parameter will be defined according formula: i w i m i, (2) where mi wi is resulting shape parameter is shape parameter for given degraded mechanism, which has share on limiting state occurring, is weight determining the mean of given degraded mechanism on limiting state reaching. This value is from interval (0;1) and is given like expert estimation for given object. Solution of determination of resulting shape parameter in this situation comes from analogy of artificial neuron (Figure 1.), when each input has assigned the weight, which carried information either amplify or reduce. It means that resulting information can be obtained from the expression (2). Fig. 1 Determination of shape parameter of Weibull probability distribution by neuron analogy. - The third situation describe the state, when are defined more than three degraded mechanisms, from which one of them is dominant. After that, the shape parameter will obtain value 1.1 and Weibull probability distribution proceed to exponential distribution. Scale parameter is then calculated according following formula 1 ln(1 F) t (3) Where is resulting shape parameter

4 F t is a value of probability of reaching limiting state in the time of last known technical object state (S2), is instant of time of last known technical object s state (Figure 2), expressed in form of number of operation hours from the beginning of object running (t3-t1) or from the last repair, when the value of probability was lowered (t2-t1). Picture explanation: Fig. 2 Graphical interpretation of each time and state values. S0 initial technical state, S1 technical state after reparation (general), S2 last known technical state, Sa actual technical state (unknown), Sp - predicate technical state (unknown), t1 operational time from the last unknown state, t2 operational time since repair, t3 operational time since beginning, t1p operational time since last known state to predicate state, t2p operational time since repair to predicate state, t3p operational time since beginning to predicate state. Consequently is able to numbering reaching probability of limiting state on the reliable model bases in each defined time moments, respectively to predicate according to defined operational hours value. 3. BASIC SYSTEM MODULES DEVELOPMENT Above described crystallizer reliability model was integrated in to the software system ARPO-AMO, its main screen is presented on figure 3. and its basic modules are: Evidence and monitoring State and prediction evaluation Localization Blanks

5 Fig. 3 Screen of the interconnection software and its modules System will be connected to TQ model (production and quality data from the process and maintenance system. The thoughts exist about usage this system especially for maintenance staff from the point of verification, ordering and cycling of crystallizer s desks. Evaluation state model and prediction perform basic function of evaluation desk s state from the point of reaching limiting state and prediction to period defined by user. This module using data mining methods connecting analytic approach to solution of given problem, when the probability distribution function of limiting state is created when assuming Weibull probability distribution and choosing degraded mechanisms which has influence on crystallizer s desks, time data regarding to operation and crystallizer s desks maintenance and data from analytics and technical diagnostics of single crystallizer s desks. On figure 4 is presented the screen of this module. Fig. 4 Screen of interconnecting software of each modules

6 4. CONCLUSION Technical systems service life solution represents difficult multiparameters task with items of indeterminateness and its character limit usage of classic mathematical analytics statistics tools. In terms of this article is presented developed and created methods of determining actual device state and its parts and prediction of the state based on analytics probability approach to description of weariness of single solved parts, which can lead to limiting state which is further improve by usage of measured diagnostics data with evaluation with usage of tools of artificial intelligence. This approach is in harmony with present modern trends in the field of technical reliability systems. Acknowledgments The work was supported by the specific university research of Ministry of Education, Youth and Sports of the Czech Republic No. SP2013/49 and project of Ministry of Industry and Trade of the Czech Republic No. FR-TI 1/319. BIBLIOGRAPHY [1] DAVID, J. Umělá inteligence v predikci životnosti, organizace a řízení údržby a obnov výrobních celků. VŠB-TU Ostrava, Ostrava, ISBN [2] DAVID, J., HEGER, M., VROŽINA, M., VÁLEK, L. Visualisation of data fields. Archives of Metallurgy and Materials. 2010, Volume: 55, Issue: 3, p ISSN: [3] DAVID J., VROŽINA M., JANČÍKOVÁ Z.: Determination of Crystallizer Service Life on Continuous Steel Casting by Means of the Knowledge System. WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS. Issue 10, Volume 10, n , Print ISSN: E-ISSN: X In < [4] DAVID, J. a kol. Automatizace v metalurgii. VŠB-TU Ostrava, Ostrava, ISBN