Operating Characteristics Analysis of Rotor Systems Using MCDM Methods
Audrius ČEREŠKA1, Valentinas PODVEZKO2, Edmundas Kazimieras ZAVADSKAS3*
1 Department of Mechanical Engineering,
Vilnius Gediminas Technical University,
Basanavičiaus str. 28, 03224, Vilnius, Lithuania,
2 Department of Mathematical Statistics,
Vilnius Gediminas Technical University,
Saulėtekio al. 11, 10223, Vilnius, Lithuania,
3 Research Institute of Smart Building Technologies,
Vilnius Gediminas Technical University,
Saulėtekio al. 11, 10223, Vilnius, Lithuania,
* Corresponding author
Abstract: The paper presents multi-criteria analysis of operating characteristics of rotor systems with tilting pad bearings. Special stand with measuring equipment was used for experimental researches. Three types of bearings were tested while changing the speed of rotor rotation and the clearance between the rotor and the bearing pad. Results of 27 measurements have been processed using multi-criteria analysis. Three methods have been used for estimating criteria weights: entropy and new methods CILOS (Criterion Impact LOS) and Objective criteria of weight determination IDOCRIW integrated (Integrated Determination of Objective Criteria Weights). For the selection of priority several well-known and widely used MCDM methods such as COPRAS, SAW and TOPSIS have been used.
Keywords: rotor system, bearing, characteristics, MCDM, objective weight, entropy method, CILOS method, IDOCRIW method.
CITE THIS PAPER AS: Audrius ČEREŠKA, Valentinas PODVEZKO, Edmundas Kazimieras ZAVADSKAS, Operating Characteristics Analysis of Rotor Systems Using MCDM Methods, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(1), pp. 59-68, 2016. https://doi.org/10.24846/v25i1y201607
Hydrodynamic bearings are one of the most important components in rotary systems. They are used in various technological machines, turbo generators, turbo compressors, steam turbines, pumps, grinding machine spindles, generators, gas turbines, fans, propulsion machinery, and a number of other mechanisms, but it is designed to significantly less work for systems with hydrodynamic bearings diagnosis methods and analysis than for systems with rolling bearings [1, 2, 3].
When the temperature of the bearing in the operating zone has reached critical values , the oil viscosity and the clearance between the rotor and the bearing segment are decreased. Then bearing is operating in a semi-fluid lubrication mode. As a result, operating time of rotor systems is shortened and it can cause failures. Such phenomena could disturb the work process and cause large losses.
Dynamic parameters of the system “rotor – oil – bearing” and parameters of the oil taken together define the stability of the rotor system, expressed by the speed of rotor rotation.
When this rate of rotation of the rotor system is reached and exceeded, occur automatic transverse rotor vibrations, caused by turbulence in the oil bearing clearances [5, 6, 7]. Stability may be achieved through the design of a hydrodynamic bearing using dampening elements [8, 9].
In order to increase stiffness of the hydrodynamic bearing and stability of rotor rotation in a wider rotation frequencies range, together with the sliding sleeve bearings were designed bearings with various structural features: sleeve, sleeve with the ring, elliptical, tilting pad, etc., etc. [10, 11]. Hydrodynamic bearings with tilting pad demonstrated good performance on adaptation options, but in order to improve rotational stability of the rotor a variety of bearing structures with additional segments spanning elastic elements have been used. These elements are regulating distribution of the loads between pad. This ensures a uniform thickness of oil hydrodynamic film as well as increased stability of the rotor rotation. It also increases the stiffness of the rotor system.
Having information about the performance characteristics of the rotor system one can determine the current status of the system and to choose the most optimal variant during design process. For latter a MCDM (Multiple Criteria Decision Making) techniques can be applied, which are successfully utilized for the optimization of technical solutions in laser technologies  and other technical solutions [13, 14, 15].
MCDM methods are based on decisions matrix R = ‖rij‖, criterion statistics (experimental criterion values) and criteria weights (weights) vector Ω = (ωi), where i = 1, 2, …, n; j = 1, 2, …, m – the number of criteria; n – compared the number of options .
For the comparison of 27 variants customized MCDM (Multiple Criteria Decision Making) methods were utilized: COPRAS (Complex Proportional Assessment) [17, 18], SAW (Simple Additive Weighting) [18-21], TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) [19 – 21].
Subjective criteria weighting methods have been used the most in practice [19, 22-27].
Data structure can be evaluated and degree of dominance (or objective weights of criteria) of each criteria can be estimated. Objective weights compared with subjective are applied in practice much less frequently [19, 28]. Combination weighting is based on the integration of subjective weighting and weighting objective [29-32].
Doing analysis of operating performance of rotor systems it is not possible to value the importance of the criteria for significance quantitatively, that is to estimate subjective weights of criteria. Therefore there are used effective setting methods of criteria weights in this work: entropy, criterion impact loss CILOS (Criterion Impact LOS) and aggregate objective weights IDOCRIW (Integrated Determination of Objective Criteria Weights).
- ALLMAIER, H., C. PRIESTNER, C. SIX, H. H. PRIEBSCH, C. FORSTNER, F. NOVOTNY-FARKAS, Predicting Friction Reliably and Accurately in Journal Bearings – A Systematic Validation of Simulation Results with Experimental Measurements, Tribology International, vol. 44, no 10, 2011, pp. 1151-1160.
- BRITO, F. P., A. S. MIRANDA, J. C. P., CLARO, M. FILLON, Experimental Comparison of the Performance of Journal Bearing with a Single and Twin Axial Groove Configuration, Tribology International, vol. 54, 2012, pp. 1–8.
- DIMOND, T., A. YOUNAN, P. ALLAIRE, A Review of Tilting Pad Bearing Theory, International Journal of Rotating Machinery, vol. 2011, 2011, p. 23.
- DANIEL, G. B., K. L. CAVALCA, Evaluation of the Thermal Effects in Tilting Pad Bearing, International Journal of Rotating Machinery, 2013, 2013, p. 17.
- CHASALEVRIS, A., D. SFYRIS, Evaluation of the Finite Journal Bearing Characteristics, using the Exact Analytical Solution of the Reynolds Equation, Tribology International, 57, 2013, pp. 216-234.
- LIU, H., H. XU, P. J. ELLISON, Z. JIN, Application of Computational Fluid Dynamics and Fluid-structure Interaction Method to the Lubrication Study of a Rotor-bearing System, Tribology Letters, vol. 38(3), 2010, pp. 325-336.
- VIGNOLO, G. G., D. O. L. BARILA, M. QUINZANI, Approximate Analytical Solution to Reynolds Equation for Finite Length Journal Bearings, Tribology International, vol. 44, issue 10), 2011, pp. 1089-1099.
- MARCINKEVIČIUS, A. H., Automatic Regulation of Clearance in a Tilting Pad Journal Bearing, Mechanika, vol. 18, no. 2, 2012, pp. 5-9.
- MARCINKEVIČIUS, A. H., M. JUREVIČIUS, Automatic Control of Loading Forces in a Tilting Pad Journal Bearing, Advances in Mechanical Engineering, vol. 2014, 2014, p. 9.
- CARBONARA, D., J. R. DUARTE, M. L. BITTENCOURT, Comparison of Journal Orbits under Hydrodynamic Lubrication Regime for Traditional and Newton-Euler Loads in Combustion Engines, Latin American Journal of Solids and Structures, vol. 6, issue 1, 2009, pp. 13-33.
- STRZELECKI, S., L. KUŚMIERZ, G. PONIEWAŻ, Thermal Deformation of Pads in Tilting 5-pad Journal Bearing, Eksploatacja i niezawodność, vol. 38, no. 2, 2008, pp. 12-16.
- MADIĆ, M., M. RADOVANOVIĆ, D. PETKOVIĆ, B. NEDIĆ, Multi-Criteria Analysis of Laser Cut Surface Characteristics in CO2 Laser Cutting of Stainless Steel, Tribology in Industry, 37, no. 2, 2015, pp. 236-243.
- CHAKRABORTY, S., E. K. ZAVADSKAS, Applications of WASPAS Method in Manufacturing Decision Making, Informatica, vol. 25, no. 1, 2014, pp. 1-20.
- CHAKRABORTY, S., E. K. ZAVADSKAS, J. ANTUCHEVICIENE, Applications of WASPAS Method as a Multi-criteria Decision Making Tool, Economic Computation and Economic Cybernetics, vol. 49, no. 1, 2015, pp. 5-22.
- RIKHTEGAR, N., N. MANSOURT, A. A. OROUMIEH, A. YAZDANI-CHAMZINI, E. K. ZAVADSKAS, S. KILDIENE, Environmental Impact Assessment based Group Decision Making Methods in Mining Projects, Economic Research – ekonomska istrazivanja. vol. 27, no.1, 2014, pp. 378-392.
- BRAUERS, W. K., R. GINEVIČIUS, A. PODVIEZKO, Development of a Methodology of Evaluation of Financial Stability of Commercial Banks, Panoeconomicus, vol. 61, no.3, 2014, pp. 349-367.
- ZAVADSKAS, E. K., A. KAKLAUSKAS, V. ŠARKA, The New Method of Multi-Criteria Complex Proportional Assessment of Projects, Technological and Economic Development of Economy, vol. 1, no. 3, 1994, pp.131-139.
- PODVEZKO, V., The Comparative Analysis of MCDM Methods SAW and COPRAS, Inžinerinė Ekonomika–Engineering Economics, 22, no. 2, 2011, pp 134-146.
- HWANG, C. L., K. YOON, Multiple Attribute Decision Making – Methods and Applications, Springer, New York, 1981.
- PODVIEZKO, A., V. PODVEZKO, Absolute and Relative Evaluation of Socio-Economic Objects Based on Multiple Criteria Decision Making Methods, Inzinerine Ekonomika – Engineering Economics, vol. 25, no. 5, 2014, pp. 522-529.
- GINEVIČIUS, R., A. PODVIEZKO, The Evaluation of Financial Stability and Soundness of Lithuanian Banks, Ekonomska istraživanja – Economic Research, 26, no. 2, 2013, pp. 191-208.
- SAATY, T. L., The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, New York: McGrawHill, 1980.
- PODVEZKO, V., H. SIVILEVICIUS, The Use of AHP and Rank Correlation Methods for Determining the Significance of the Interaction Between the Elements of a Transport System Having a Strong Influence on Traffic Safety, Transport, 28, no.4, 2013, pp. 389-403.
- KERSULIENE, V., E. K. ZAVADSKAS, Z. TURSKIS, Selection of Rational Dispute Resolution Method by Applying New Step–wise Weight Assessment Ratio Analysis (SWARA), Journal of Business Economics and Management 11, no. 2, 2010, pp. 243-258.
- KRYLOVAS, A., E. K. ZAVADSKAS, N. KOSAREVA, S. DADELO, New KEMIRA Method for Determining Criteria Priority and Weights in Solving MCDM Problem, International Journal of Information Technology & Decision Making, vol. 13, no.6, 2014, pp. 1-15.
- YAZDANI-CHAMZINI, A., A Integrated Fuzzy Multi Criteria Group Decision Making Model for Handling Equipment Selection, Journal of Civil Engineering and Management, vol. 20, no.5, 2014, pp. 660-673.
- HASHEMKHANI ZOLFANI, S., M. BAHRAMI, Investment Prioritizing in High Tech Industries based on SWARA-COPRAS Approach, Technological and Economic Development of Economy, Vol. 20, no. 3, 2014, pp. 534-553.
- CHENG, Q., Structure Entropy Weight Method to Confirm the Weight of Evaluating Index, Systems Engineering – Theory & Practice, 30, no. 7, 2010, pp. 1225-1228.
- USTINOVICHIUS, L., E. K. ZAVADSKAS, V. PODVEZKO, Application of a Quantitative Multiple Criteria Decision Making (MCDM-1) Approach to the Analysis of Investments in Construction, Control and Cybernetics, vol. 36, no. 1, 2007, pp. 251-268.
- MA, J. Z., P. FAN, L. H. HUANG, A Subjective and Objective Integrated Approach to Determine Attribute Weights, European Journal of Operational Research, 112, no. 2, 1999, pp. 397-404.
- SAAD, R., M. Z. AHMAD, M. S. ABU, M. S., JUSOH, Hamming Distance Method with Subjective and Objective Weights for Personnel Selection. The Scientific World Journal Article, ID 865495, 2014, pp. 1-9.
- WANG, T. C., H. D. LEE, Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights, Expert Systems with Applications, vol. 36, issue 5, 2009, pp. 8980-8985.
- SHANNON, C. E., The Mathematical Theory of Communication, Bell System Technical Journal, vol. 27, 1948, pp. 379-423.
- MIRKIN, B. G., Problema grupovogo vibora, Moskva, Nauka, 1974, p. 256 (Russian).
- ZAVADSKAS, E. K., V. PODVEZKO, Integrated Determination of Objective Criteria Weights in MCDM, International Journal of Information Technology & Decision Making, Vol. 15, No. 2, 2016, pp. 267-284, DOI:10.1142/S0219622016500036.