MULTI ATTRIBUTE DECISION MAKING IN SELECTION OF THE MOST SIGNIFICANT CONDITION MONITORING METHODOLOGY FOR ROTATING MACHINERY

Transcription

1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 3, March 2017, pp Article ID: IJMET_08_03_028 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed MULTI ATTRIBUTE DECISION MAKING IN SELECTION OF THE MOST SIGNIFICANT CONDITION MONITORING METHODOLOGY FOR ROTATING MACHINERY Arka Sen Research Scholar, Mechanical Engineering Department, National Institute of Technology, Durgapur, West Bengal, India Manik Chandra Majumder Professor, Mechanical Engineering Department, National Institute of Technology, Durgapur, West Bengal, India Sumit Mukhopadhyay Associate Professor, Mechanical Engineering Department, National Institute of Technology, Durgapur, West Bengal, India Robin Kumar Biswas Chief Scientist, Condition Monitoring and Structural Analysis Group, Central Mechanical Engineering Research Institute, Durgapur, India ABSTRACT This paper deals with various condition monitoring methodology, detailed analysis and evaluation of the accurate methodology through a data based analytical approach of quantitative decision making, which is used for evaluating the most appropriate condition monitoring methodology using Analytical Hierarchy Process (AHP) for a 525MW Turbo generator set. Key words: Condition Monitoring (CM), Analytical Hierarchy Process (AHP), Turbo Generator Set (TG set), Saaty`s table. Cite this Article: Arka Sen, Manik Chandra Majumder, Sumit Mukhopadhyay and Robin Kumar Biswas, Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery. International Journal of Mechanical Engineering and Technology, 8(3), 2017, pp INTRODUCTION Machine faults can be defined as any change in a machinery part or component which makes it unable to perform its function properly. Due to these faults in machine components, they editor@iaeme.com

2 Arka Sen, Manik Chandra Majumder, Sumit Mukhopadhyay and Robin Kumar Biswas undergo component damage, energy loss and also work efficiency is reduced to a great extent. Classification of failure causes are as follows: Inherent weakness in material, design, and manufacturing Misuse or applying stress in undesired direction Gradual deterioration due to wear, tear, stress fatigue, corrosion, and so forth. The common types of machine faults are: (i) Unbalance (ii) Shaft misalignment or bent shaft (iii) Damaged or loose bearings (iv) Damaged gears (v) Faulty of misaligned belt drive (vi) Mechanical looseness (vii) Increased turbulence (viii) Electrical induced vibration. In machine fault diagnosis various approaches are being used for finding the fault of the machine. All the methods and techniques are not effective and sometimes become confusing and no solution is obtained. Thus, selection of the right condition monitoring methods is the first criterion for fault identification in a machine. The cost and time also have a crucial role in the commercial aspect of condition monitoring where both are factors are expected to be low for an efficient condition monitoring methodology. P. Soderholm & B. Nystrom has used AHP for decision making and expert judgement in railway infrastructure maintenance [6]. Kamal Al-Subhi Al Harbi has discussed the application of AHP for potential decision making for project management [4]. Odzen Bayazit has applied AHP in the tractor manufacturing plant. [5]. Most important factors, relative importance and its influence were found in this tractor manufacturing system. J.A. Alanso & M. Teresalamata has discussed the AHP and statistical criteria have been used for accepting / rejecting the pair wise reciprocal comparison matrices in AHP [3]. D. Dalalah, Farris Al Oqla & M. Hayajneh has used AHP for the selection of crane [1]. The process of crane selection is a multi-criteria decision making problem with conflicting and diversifying objectives. Author has presented a systematic methodology under consideration of multiple factors and objectives that are faced to be crucial to construction process. 2. VARIOUS CONDITIONS MONITORING METHODOLOGY Various Condition Monitoring Techniques that are being used in recent times for plant machinery monitoring are shown in the Figure 1.1 and discussed below in brief. Vibration Monitoring: In this monitoring method, machine vibration parameter is monitored. Any machine problem is exhibited in change in vibration pattern. Which is monitored with the help of various portable instruments or permanent sensors fitted in the machine for on line measurements? Oil Analysis: The oil analysis can be performed on different types of oil, such as lubrication, hydraulic or insulation oil. It can indicate problems such as machine degradation (wear), oil contamination, improper oil consistency (e.g. incorrect or improper amount of additives) and oil deterioration. Displacement Measurement: The displacement measurement in relation to the TG set is the axial shift measurement of the turbine shaft both backward and forward or the differential expansion measurement between inner and outer casing of turbine. This is a vital parameter and its monitoring gives immediate decision of turbine tripping. Wear Debris Analysis: Small particles called wear debris are generated when machine degrades. This comes out from the machine with its lubricating oil used. This wear particles are studied in detail in a bichromatic microscope (called Ferroscope), after making slide out of this lubricating oil. The colour, shape, size and density of the particle deposited on the slide, determines the severity of the machine editor@iaeme.com

3 Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery Pressure Monitoring: Oil or fluid pressure is the indicative parameter of machine malfunction. Particularly bearing input and output pressure gives indication of oil chocking or filter not working etc. In fluid handling system like pump, can be monitored with pressure readings. It is not a very powerful technique of monitoring but with the addition of this information the monitoring becomes more powerful. Temperature Measurements: Temperature a measurement (e.g. temperature indicating paints) helps to detect potential failure related to temperature change of the equipment. Measured temperature changes can indicate problems such as excessive mechanical friction (e.g. faulty bearing, inadequate lubrication), degraded heat transfer (e,g. fouling in a heat exchanger) and poor electrical connections (e.g. loose, corroded or oxidized connections) Thermography: Since infrared radiation is emitted by all objects above absolute zero according to the black body radiation law, thermography makes it possible to see one s environment with or without visible illumination. The amount of radiations emitted by an object increases with temperature. Thermal imaging cameras detect radiation in the infrared range of the electromagnetic spectrum and produce images of the radiation called thermograms. Figure 1 Selection criteria for CM technique 3. ANALYTICAL HIERARCHY PROCESS FOR THE SELECTION OF RELIABLE CONDITION MONITORING METHODOLOGY (FIRST TIER) Figure 2.1 shows the various CM methodologies and its relation with cost, time, accuracy of measurement and early detection editor@iaeme.com

4 Arka Sen, Manik Chandra Majumder, Sumit Mukhopadhyay and Robin Kumar Biswas Figure 2 Selection of CM methodology By identifying the attribute by A, B, C, D, E, F and G, the initial matrix has been established in Table 2.2, 2.4, 2.6, 2.8 and 3.10 by following the scales for relative importance of each parameter over the other as given in Table 2.1. Table 1 Relative Importance of each parameter Intensity of Importance Definition Explanation 1 Equal importance Two activities contribute equally to the objective. 3 Moderate importance Experience and judgment slightly favour one activity over another. 5 Strong importance Experience and judgment strongly favour one activity over another. 7 Very strong or demonstrated importance An activity is favored very strongly over another, its dominance demonstrated in practice. 9 Extreme importance The evidence favouring one activity over another is of the highest possible order of affirmation. 2,4,6,8 For compromise between the above values Sometimes one needs to interpolate a compromise judgment numerically because there is no good Reciprocals of above If activity i has one of the above nonzero numbers assigned to it when compared with activity j then j has the reciprocal value when compared with i word to describe it. A comparison mandated by choosing the smaller element as the unit to estimate the larger one as a multiple of that unit. Rationals Ratios arising from the scale If consistency were to be forced by obtaining n numerical values to span the matrix For tied activities When elements are close and nearly indistinguishable; moderate is 1.3 and extreme is editor@iaeme.com

5 Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery 4. PROCEDURE FOR DETERMINING THE SCORE FOR ATTRIBUTES IN THE TWO TIER ANALYTICAL HIERARCHICAL PROCESS (AHP) From field inputs as given by engineers, the initial pair wise matrix has been developed using relative importance of each parameter (using Table 2.1) Summation of each column is done. In the priority matrix, each of the column elements is divided by their respective column sum. Row sum is calculated and then divided by the number of attributes to get the Geometric Mean (GM) of each row. The process mentioned in (iv) has been repeated for all the seven attributes (in first tier) and four objectives (in second tier). From Table 2.3, 2.5, 2.7 and 2.9 the ranking of the attributes are obtained. Similarly from Table 3.11, the rankings of the objectives are obtained. Matrix Y 1 (7 X 4) for relative worth index of attribute and Matrix Z 1 (4 X 1) for relative worth index of objective is obtained from Table 2.3, 2.5, 2.7, 2.9 and 3.11 respectively. By multiplying matrix Y 1 with Matrix Z 1, we get the Overall Relative Worth Index Matrix X 1 which determines the final ranking of the attributes. The attribute with Ranking I is the most reliable and efficient methodology for condition monitoring of a rotating machinery (current case -TG set) with minimum condition monitoring cost, minimum time required for data collection and processing, high rate of precision and accuracy and very high possibility of early detection of fault. Table 2 Initial Matrix (Pair -wise Comparison of Cost) (Minimum Condition Monitoring cost is preferable) A B C D E F G A 1 1/5 3 1/ /9 B / C 1/3 1/7 1 1/ /7 D E 1/4 1/8 1 1/ /9 F 1/7 1/4 1/2 1/6 1/3 1 1/7 G 9 1/7 7 1/ SUM Table 3 Priority Matrix for Comparison of Cost A B C D E F G ROW SUM GM A 11/228 23/904 6/47 84/949 6/41 1/4 7/ B 241/999 79/621 14/47 70/949 12/41 1/7 441/ C 11/684 1/55 2/47 105/949 3/82 1/14 9/ D 10/ /997 8/47 289/653 15/82 3/14 189/ E 10/829 9/566 2/47 84/949 3/82 3/28 7/ F 1/145 9/283 1/47 70/949 1/82 1/28 9/ G 241/555 1/55 14/47 140/949 27/82 1/4 63/ editor@iaeme.com

6 Arka Sen, Manik Chandra Majumder, Sumit Mukhopadhyay and Robin Kumar Biswas Table 4 Initial Matrix (Pair wise Comparison of Time)(Minimum Condition Monitoring Time is preferable) A B C D E F G A 1 1/4 2 1/ B C 1/2 1/6 1 1/ /2 D 5 1/ E 1/4 1/7 1/2 1/ /3 F 1/4 1/7 1/2 1/ /3 G 1/2 1/5 2 1/ SUM Table 5 Priority Matrix for Comparison of Time A B C D E F G ROW SUM GM A 2/23 35/313 2/17 2/51 1/6 4/23 25/ B 8/23 140/313 6/17 10/17 7/24 7/23 125/ C 1/23 70/939 1/17 2/51 1/12 2/23 25/ D 10/23 140/939 5/17 10/51 1/4 5/23 75/ E 1/46 20/313 1/34 5/153 1/24 1/23 25/ F 1/46 20/313 1/34 2/51 1/24 1/23 25/ G 1/23 28/313 2/17 10/153 1/8 3/23 25/ Table 6 Initial Matrix (Pair wise Comparison of Precision and Accuracy of Monitoring) A B C D E F G A B 1/ / C 1/6 1/4 1 1/ /2 D 1/ E 1/8 1/6 1/4 1/4 1 1/3 1/3 F 1/7 1/5 1/3 1/ /3 G 1/5 1/3 2 1/ SUM Table 7 Priority matrix for comparison of Precision and Accuracy of Monitoring A B C D E F G ROW SUM GM A 280/691 20/53 72/211 8/17 1/4 21/76 6/ B 57/422 20/159 48/211 4/51 3/16 15/76 18/ C 57/844 5/159 12/211 1/17 1/8 9/76 3/ D 140/691 20/53 48/211 4/17 7/32 9/38 24/ E 35/691 10/477 3/211 1/17 1/32 1/76 2/ F 40/691 4/159 4/211 2/51 3/32 3/76 2/ G 56/691 20/477 24/211 1/17 3/32 9/76 6/ Table 8 Initial Matrix (Pair wise Comparison of Early Detection) A B C D E F G A / B 1/5 1 1/2 1/ C 1/ / D E 1/7 1/5 1/5 1/6 1 1/2 1/4 F 1/6 1/4 1/4 1/ /3 G 1/5 1/3 1/3 1/ SUM editor@iaeme.com

7 Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery Table 9 Priority matrix for comparison of Early Detection A B C D E F G ROW SUM GM A 70/ / /497 5/29 9/32 12/47 84/ B 14/283 60/827 30/497 2/29 5/32 8/47 36/ C 70/ /827 60/497 5/29 5/32 8/47 36/ D 140/ / /497 10/29 3/16 10/47 36/ E 10/283 12/827 12/497 5/87 1/32 1/47 3/ F 35/849 15/827 15/497 2/29 1/16 2/47 4/ G 14/283 20/827 20/497 10/87 1/8 6/47 12/ Legend: A Vibration Monitoring, B Oil Analysis, C Displacement Measurement Wear Debris Analysis, E Pressure Measurement, F Temperature Measurement, G Thermography, GM Geometric Mean 5. ANALYTICAL HIERARCHY PROCESS FOR THE SELECTION OF RELIABLE CONDITION MONITORING METHODOLOGY (SECOND TIER) To determine the significance of each one of the objectives over the other, analytic hierarchy process has been used and shown in Table The priority matrix for the objective has been shown in Table Table 10 Pair wise Comparison of Objectives (in second tier) COST OF MONITORING TIME OF MONITORING ACCURACY AND PRECISION EARLY DETECTION COST OF MONITORING TIME OF MONITORING ACCURACY AND PRECISION EARLY DETECTION SUM Table 11 Priority Matrix for comparison of objectives (in second tier) COST OF MONITORING TIME OF MONITORING ACCURACY AND PRECISION EARLY DETECTION ROW SUM GM COST OF MONITORING TIME OF MONITORING ACCURACY AND PRECISION EARLY DETECTION Relative Worth Matrix for Attribute: Y 1 = editor@iaeme.com

8 Arka Sen, Manik Chandra Majumder, Sumit Mukhopadhyay and Robin Kumar Biswas Relative Worth Matrix for Objective: Z 1 = By matrix multiplication method, the matrix Y 1 and matrix Z 1 is multiplied to get the overall Relative Worth Index of Attributes. Overall Relative Worth Index: X 1 = Y 1 * Z 1 = Final ranking of attributes in condition monitoring methodology as per Analytical Hierarchy process is shown in Table Table 12 Ranking of Attributes after AHP Condition monitoring methodology Ranking Overall Relative worth Index Vibration Monitoring (A) I Oil Analysis (B) III Displacement Measurement (C) IV Wear Debris Analysis (D) II Pressure Measurement (E) VII Temperature Measurement (F) VI Thermography (G) V QUANTITATIVE DECISION MAKING RULE From the above selection of condition monitoring methodology, it can be easily concluded that the condition monitoring of the rotating machine (current case- TG set) depends upon the factors (attributes) like cost, time, accuracy and early detection. This shall again vary from machine to machine depending upon the mode of operation, machine size and functional capabilities. In our present discussion a typical case of Turbo Generator set of 525MW at Maithon Power Limited has been taken into consideration. The same methodology can be applied to other complex operating machinery with inputs from field engineers and likewise correct decision can be taken from the calculation of the Overall Relative Worth Index matrix which is a quantitative representation of the importance of the attributes and objectives editor@iaeme.com

9 Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery 7. RESULT AND DISCUSSION The following conclusions may be derived: Quantitative decision making of condition monitoring methodology has been established by ranking the attributes & finding the overall relative worth index, using Analytical Hierarchy Process, where Vibration Analysis has been Ranked I which clearly indicates for being the best condition monitoring methodology in the current case study of 525MW Turbo Generator set. This can be extended for other complex machines depending on their functional capability and size. The result may also vary depending on the inputs from the field engineers. Wear debris analysis with relative weightage (Rank II) is also equally powerful to monitor the TG set. In absence of vibration analysis ( Rank -I), wear debris analysis may also be adopted for monitoring the TG set with good result. It is also concluded that with Vibration analysis supplemented by wear debris analysis by Ferrography is the most effective monitoring methodology of Turbo Generator set. The supply of more cost-effective monitoring tools has reduced the errors and accumulation of junk data in condition monitoring by technical advances such as: (i) reduced costs of instrumentation (ii) increased capability of instrumentation such as data preacquisition, data storage (iii) radio transmission direct by the sensors with integrated electronically circuits, (iv) improved data storage media in combination with low cost computation (v) faster and more effective data analysis using specialist software tools. 8. ACKNOWLEDGEMENT The author is thankful to the Department of Mechanical Engineering, National Institute of Technology Durgapur and Condition Monitoring and Structural Analysis Group of Central Mechanical Engineering Research Institute located at Durgapur for helping me to carry out this research work on Multi Attribute Decision Making in Selection of the Most Significant Condition Monitoring Methodology for Rotating Machinery REFERENCES [1] D. Dalalah, Farris Al Oqla, M. Hayajneh Application of the Analytic Hierarchy Process (AHP) in Multi Criteria Analysis of the selection of cranes. [2] E. Triantaphyllou, S.H. Mann Using the Analytical Hierarchy Process for decision making in Engineering Applications: Some Challenges. International Journal of Industrial Engineering Application and Practice, Vol-2, No. 1 pp35-44 (1995). [3] J.A. Alanso, M. Teresalamata Consistency in the analytic Hierarchy process: a new approach, International Journal of Uncertainty Fuzziness and Knowledge based system., Vol-14, No.4 (2006), p [4] Kamal Al-Subhi Al Harbi Application of AHP in Project Management International Journal of Project Management, 19 (2001), 19 to 27 [5] OdzenBayazit Use of AHP in decision making for flexible manufacturing system Journal of manufacturing technology Management, Vol.16, No.7, 2005 p [6] P Soderholm and B. Nystrom The analytic Hierarchy Process (AHP) for decision making and expert judgment in railway infrastructure maintenance IRSC 2009, Bastad, Sweden. [7] R. K. Biswas, M. C. Majumdar, S. K. Basu, Vibration and Oil Analysis by Ferrography for Condition Monitoring, J. Inst. Eng. India Ser. C (July September 2013) 94(3): editor@iaeme.com

10 Arka Sen, Manik Chandra Majumder, Sumit Mukhopadhyay and Robin Kumar Biswas [8] Kuldeep R. Sanap, S.R. Paraskar and S.S. Jadhao. Broken Rotor Bar Fault Diagnosis of Induction Motor by Signal Processing Techniques. International Journal of Electrical Engineering & Technology, 8(1), 2017, pp [9] Satya Mandal and Dr. Seema Sarkar Mondal. Analytic Hierarchy Process (AHP) Approach f or Selection of Open Cast Coal Mine Project, International Journal of Industrial Engineering Research and Development, 7 (2), 2016, pp editor@iaeme.com