A Methodology for Selection of Condition Monitoring Techniques for Rotating Machinery
Gurmeet Singh Vallayil Narayana Achutha Naikan
Rotating machinery generally consist of a driver machine such as a motor and a driven machine or load such as a compressor or pump. Several condition monitoring (CM) techniques have been developed over the years for the predictive maintenance of rotating machinery. An appropriate selection of these techniques needs to be established for maximizing the ROI (Return on investment) of such systems. This paper proposes a methodology for the proper selection of CM techniques based on factors such as fault detectability, fault severity, cost, ease of data collection, noise, and system criticality. Effective techniques are recommended based on applicability in the industrial scenario and research done till now. A careful scoring system was adopted and weightage was given to each factor by expert opinion depending on its importance in the industrial environment. Multi-criteria decision-making (MCDM) was used to obtain comparable technique combination scores. The effectiveness of a single technique was found limited in rotating machinery, effective combinations were made and scored according to important factors. Final scores were obtained and top combinations were chosen for non-critical, sub-critical, and critical systems. A possible way of implementation is also shown for remote monitoring through literature.
industrial asset condition monitoring, fault diagnosis, induction motor, condition monitoring techniques, industrial downtime, remote monitoring
Al-Najjar, B. (2000). Impact of real-time measurements of operating conditions on effectiveness and accuracy of vibration-based maintenance policy a case study in paper mill. Journal of Quality in Maintenance Engineering, 6(4). https://doi.org/10.1108/13552510010346815
Al-Najjar, B. (2007). The lack of maintenance and not maintenance which costs: A model to describe and quantify the impact of vibration-based maintenance on company’s business. International Journal of Production Economics, 107(1), 260–273. https://doi.org/10.1016/j.ijpe.2006.09.005
Al-Najjar, B. (2012). On establishing cost-effective condition-based maintenance: Exemplified for vibration-based maintenance in case companies. Journal of Quality in Maintenance Engineering, 18(4), 401–416. https://doi.org/10.1108/13552511211281561
Al-Najjar, B., & Alsyouf, I. (2003). Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making. In Int. J. Production Economics (Vol. 84).
Barbour, A., & Thomson, W. T. (1997). Finite element analysis and on-line current monitoring to diagnose airgap eccentricity in 3-phase induction motors. IEE Conference Publication, 444. https://doi.org/10.1049/cp:19971057
Bellini, A., Immovilli, F., Rubini, R., & Tassoni, C. (2008). Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. Conference Record - IAS Annual Meeting (IEEE Industry Applications Society). https://doi.org/10.1109/08IAS.2008.26
Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47(5), 984–993. https://doi.org/10.1109/41.873206
Buckley, J. J. (1987). THE FUZZY MATHEMATICS OF FINANCE. In Fuzzy Sets and Systems (Vol. 21).
Carnero, M. C. (2009). Selection of condition monitoring techniques using discrete probability distributions: A case study. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 223(1), 99–117. https://doi.org/10.1243/1748006XJRR186
Ch, T., Kumar, A., Singh, G., & Naikan, V. N. A. (2015). Effectiveness of vibration monitoring in the health assessment of induction motor. In International Journal of Prognostics and Health Management.
Chatterjee, P., & Chakraborty, S. (2013). Gear Material Selection using Complex Proportional Assessment and Additive Ratio Assessment-based Approaches: A Comparative Study. International Journal of Materials Science and Engineering, 104–111. https://doi.org/10.12720/ijmse.1.2.104-111
Choudhary, A., Shimi, S. L., & Akula, A. (2019). Bearing fault diagnosis of induction motor using thermal imaging. 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018. https://doi.org/10.1109/GUCON.2018.8674889
Davis, P., Sullivan, E., Marlow, D., & Marney, D. (2013). A selection framework for infrastructure condition monitoring technologies in water and wastewater networks. Expert Systems with Applications, 40(6), 1947–1958. https://doi.org/10.1016/j.eswa.2012.10.004
Djeddi, M., Granjon, P., & Leprettre, B. (2007). Bearing Fault Diagnosis in Induction Machine Based on Current Analysis Using High-Resolution Technique. 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 23–28. https://doi.org/10.1109/DEMPED.2007.4393066
Duan, Z., Wu, T., Guo, S., Shao, T., Malekian, R., & Li, Z. (2018). Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. International Journal of Advanced Manufacturing Technology, 96(1–4), 803–819. https://doi.org/10.1007/s00170-017-1474-8
Dutta, N., Umashankar, S., Arun Shankar, V. K., Padmanaban, S., Leonowicz, Z., & Wheeler, P. (2018). Centrifugal Pump Cavitation Detection Using Machine Learning Algorithm Technique. Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. https://doi.org/10.1109/EEEIC.2018.8494594
Elliot, S. (2015). DevOps and the Cost of Downtime: Fortune 1000 Best Practice Metrics Quantified. IDC Insight, December.
Emovon, I., & Oghenenyerovwho, O. S. (2020). Application of MCDM method in material selection for optimal design: A review. Results in Materials, 7. https://doi.org/10.1016/j.rinma.2020.100115
Gangsar, P., & Tiwari, R. (2020). Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. In Mechanical Systems and Signal Processing (Vol. 144). Academic Press. https://doi.org/10.1016/j.ymssp.2020.106908
Glowacz, A. (2018). Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137, 82–89. https://doi.org/10.1016/j.apacoust.2018.03.010
Glowacz, A., & Glowacz, Z. (2017). Diagnosis of the three-phase induction motor using thermal imaging. Infrared Physics and Technology, 81, 7–16. https://doi.org/10.1016/j.infrared.2016.12.003
Goktas, T., Arkan, M., Salih Mamis, M., & Akin, B. (2017, August 3). Broken rotor bar fault monitoring based on fluxgate sensor measurement of leakage flux. 2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017. https://doi.org/10.1109/IEMDC.2017.8002342
Goman, V., Oshurbekov, S., Kazakbaev, V., Prakht, V., & Dmitrievskii, V. (2019). Energy efficiency analysis of fixed-speed pump drives with various types of motors. Applied Sciences (Switzerland), 9(24). https://doi.org/10.3390/app9245295
Gugaliya, A., Singh, G., & Naikan, V. N. A. (2018). Effective combination of motor fault diagnosis techniques. Proceedings of 2018 IEEE International Conference on Power, Instrumentation, Control and Computing, PICC 2018, 1–5. https://doi.org/10.1109/PICC.2018.8384812
Gundewar, S. K., & Kane, P. v. (2021). Condition Monitoring and Fault Diagnosis of Induction Motor. In Journal of Vibration Engineering and Technologies (Vol. 9, Issue 4, pp. 643–674). Springer. https://doi.org/10.1007/s42417-020-00253-y
Guoji, S., McLaughlin, S., Yongcheng, X., & White, P. (2014). Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears. Mechanical Systems and Signal Processing, 43(1–2), 76–89. https://doi.org/10.1016/j.ymssp.2013.08.023
Hamid A. Toliyat, G. B. K. (2004). Handbook of Electrical Motors. In Electronic Product Design.
Han, Y., & Song, Y. H. (2003). Condition monitoring techniques for electrical equipment - A literature survey. In IEEE Transactions on Power Delivery (Vol. 18, Issue 1, pp. 4–13). https://doi.org/10.1109/TPWRD.2002.801425
Henriquez, P., Alonso, J. B., Ferrer, M. A., & Travieso, C. M. (2014). Review of automatic fault diagnosis systems using audio and vibration signals. In IEEE Transactions on Systems, Man, and Cybernetics: Systems (Vol. 44, Issue 5, pp. 642–652). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/TSMCC.2013.2257752
Ichwana, Nasution, I. S., Sundari, S., & Rifky, N. (2020). Data Acquisition of Multiple Sensors in Greenhouse Using Arduino Platform. IOP Conference Series: Earth and Environmental Science, 515(1). https://doi.org/10.1088/1755-1315/515/1/012011
International Organization for Standardization (ISO) 10816-6:1995, Mechanical vibration - Evaluation of machine vibration by measurements on non-rotating parts - Part 6: Reciprocating machines with power ratings above 100 KW. (n.d.).
Jin, X., Cheng, F., Peng, Y., Qiao, W., & Qu, L. (2016, November 2). A comparative study on Vibration- and current-based approaches for drivetrain gearbox fault diagnosis. IEEE Industry Application Society, 52nd Annual Meeting: IAS 2016. https://doi.org/10.1109/IAS.2016.7731964
Kabir, G., Sadiq, R., & Tesfamariam, S. (2014). A review of multi-criteria decision-making methods for infrastructure management. Structure and Infrastructure Engineering, 10(9), 1176–1210. https://doi.org/10.1080/15732479.2013.795978
Kanovic, Z., Matic, D., Jelicic, Z., Rapaic, M., Jakovljevic, B., & Kapetina, M. (2013). Induction motor broken rotor bar detection using vibration analysis - A case study. Proceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013, 64–68. https://doi.org/10.1109/DEMPED.2013.6645698
Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017a). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. In Renewable and Sustainable Energy Reviews (Vol. 69, pp. 596–609). Elsevier Ltd. https://doi.org/10.1016/j.rser.2016.11.191
Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017b). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. In Renewable and Sustainable Energy Reviews (Vol. 69, pp. 596–609). Elsevier Ltd. https://doi.org/10.1016/j.rser.2016.11.191
Kumar, S., Mukherjee, D., Guchhait, P. K., Banerjee, R., Srivastava, A. K., Vishwakarma, D. N., & Saket, R. K. (2019). A Comprehensive Review of Condition Based Prognostic Maintenance (CBPM) for Induction Motor. In IEEE Access (Vol. 7, pp. 90690–90704). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2019.2926527
Kumar Verma, A., Sarangi, S., & Kolekar, M. H. (2013). Misalignment fault detection in induction motor using rotor shaft vibration and stator current signature analysis. In Int. J. Mechatronics and Manufacturing Systems (Vol. 6, Issue 6).
Kuphaldt, T. R., & Haughery, J. R. (2000). APPLIED INDUSTRIAL ELECTRICITY Theory and Application. https://www.gnu.org/licenses/dsl.html
Laws, W. C., & Muszynska, A. (1987). Periodic and continuous vibration monitoring for preventive/predictive maintenance of rotating machinery. Journal of Engineering for Gas Turbines and Power, 109(2). https://doi.org/10.1115/1.3240019
Liu, L., Liu, D., Zhang, Y., & Peng, Y. (2016). Effective sensor selection and data anomaly detection for condition monitoring of aircraft engines. Sensors (Switzerland), 16(5). https://doi.org/10.3390/s16050623
Loiselle, R., Xu, Z., & Voloh, I. (2018). Essential motor health monitoring: Making informed decisions about motor maintenance before a failure occurs. IEEE Industry Applications Magazine, 24(6), 8–13. https://doi.org/10.1109/MIAS.2017.2740465
Maletič, D., Lovrenčič, V., Maletič, M., Al-Najjar, B., & Gomišček, B. (2015). Maintenance solutions for cost-effective production: A case study in a paper mill. Lecture Notes in Mechanical Engineering, 19. https://doi.org/10.1007/978-3-319-09507-3_33
Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials and Design, 31(4), 1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020
Mechefske, C. K., & Wang, Z. (2001). Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies. Mechanical Systems and Signal Processing, 15(6), 1129–1140. https://doi.org/10.1006/mssp.2000.1395
Mehala, N., & Dahiya, R. (2007). Motor Current Signature Analysis and its Applications in Induction. International Journal, 2(1).
Moore, J. R., & Baker, N. R. (1969). Computational Analysis of Scoring Models for R and D Project Selection. In Application Series (Vol. 16, Issue 4). https://www.jstor.org/stable/2628799?seq=1&cid=pdf-
Muthanandan, S., & Nor, K. A. B. M. (2019). Condition monitoring and assessment for rotating machinery. In SpringerBriefs in Applied Sciences and Technology (pp. 1–22). Springer Verlag. https://doi.org/10.1007/978-981-13-2357-7_1
Negrea, M. D. (2006). Electromagnetic Flux Monitoring for Detecting Faults in Electrical Machines. In Ph.D. Thesis.
Parekh, R. (2003). AC Induction Motor Fundamentals. Microchip Technology Inc.
Parsaei, H. R., & Wilhelm, M. R. (1989). A JUSTIFICATION METHODOLOGY FOR AUTOMATED MANUFACTURING TECHNOLOGIES. In Computers md Engng (Vol. 16, Issue 3).
Peng, Y., Qiao, W., Qu, L., & Wang, J. (2018). Sensor Fault Detection and Isolation for a Wireless Sensor Network-Based Remote Wind Turbine Condition Monitoring System. IEEE Transactions on Industry Applications, 54(2), 1072–1079. https://doi.org/10.1109/TIA.2017.2777925
Petkov, N., Wu, H., & Powell, R. (2020). Cost-benefit analysis of condition monitoring on DEMO remote maintenance system. Fusion Engineering and Design, 160. https://doi.org/10.1016/j.fusengdes.2020.112022
Prasanna, J. L., Lavanya, D., & Kumar, T. A. (2017). Condition monitoring of a virtual solar system using IoT. 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 286–290. https://doi.org/10.1109/CESYS.2017.8321282
Rahman, M. M., & Uddin, M. N. (2017). Online Unbalanced Rotor Fault Detection of an IM Drive Based on Both Time and Frequency Domain Analyses. IEEE Transactions on Industry Applications, 53(4), 4087–4096. https://doi.org/10.1109/TIA.2017.2691736
Reljić, D., Jerkan, D., Marčetić, D., & Oros, D. (2016). Broken bar fault detection in IM operating under no-load condition. Advances in Electrical and Computer Engineering, 16(4), 63–70. https://doi.org/10.4316/AECE.2016.04010
Resa, J., Cortes, D., Marquez-Rubio, J. F., & Navarro, D. (2019). Reduction of induction motor energy consumption via variable velocity and flux references. Electronics (Switzerland), 8(7). https://doi.org/10.3390/electronics8070740
Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). A review of multi-criteria decision making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30–35. https://doi.org/10.1016/j.procir.2015.08.086
Sayadi, M. K., Heydari, M., & Shahanaghi, K. (2009). Extension of VIKOR method for decision making problem with interval numbers. Applied Mathematical Modelling, 33(5), 2257–2262. https://doi.org/10.1016/j.apm.2008.06.002
Schütze, A., Helwig, N., & Schneider, T. (2018). Sensors 4.0 - Smart sensors and measurement technology enable Industry 4.0. Journal of Sensors and Sensor Systems, 7(1), 359–371. https://doi.org/10.5194/jsss-7-359-2018
Shin, K., & Lee, S. H. (2015). Machinery Fault Diagnosis Using Two-Channel Analysis Method Based on Fictitious System Frequency Response Function. Shock and Vibration, 2015. https://doi.org/10.1155/2015/561238
Shyamala, D., Swathi, D., Prasanna, J. L., & Ajitha, A. (2017). IoT platform for condition monitoring of industrial motors. 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 260–265. https://doi.org/10.1109/CESYS.2017.8321278
Singh, A., Grant, B., Defour, R., Sharma, C., & Bahadoorsingh, S. (2016). A review of induction motor fault modeling. In Electric Power Systems Research (Vol. 133, pp. 191–197). Elsevier Ltd. https://doi.org/10.1016/j.epsr.2015.12.017
Singh, G., Anil Kumar, T. C., & Naikan, V. N. A. (2016). Induction motor inter turn fault detection using infrared thermographic analysis. Infrared Physics and Technology, 77, 277–282. https://doi.org/10.1016/j.infrared.2016.06.010
Soother, D. K., & Daudpoto, J. (2019). A brief review of condition monitoring techniques for the induction motor. Transactions of the Canadian Society for Mechanical Engineering, 43(4), 499–508. https://doi.org/10.1139/tcsme-2018-0234
Stopa, M. M., Cardoso Filho, B. J., & Martinez, C. B. (2014). Incipient detection of cavitation phenomenon in centrifugal pumps. IEEE Transactions on Industry Applications, 50(1), 120–126. https://doi.org/10.1109/TIA.2013.2267709
Tabikh, M. (n.d.). Downtime cost and Reduction analysis: Survey results.
Terron-Santiago, C., Martinez-Roman, J., Puche-Panadero, R., & Sapena-Bano, A. (2021). A review of techniques used for induction machine fault modelling. In Sensors (Vol. 21, Issue 14). MDPI AG. https://doi.org/10.3390/s21144855
Thorsen, O. v., & Dalva, M. (1998). Methods of condition monitoring and fault diagnosis for induction motors. European Transactions on Electrical Power, 8(5). https://doi.org/10.1002/etep.4450080510
Trajin, B., Regnier, J., & Faucher, J. (2010). Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives. IET Electric Power Applications, 4(2), 90–100. https://doi.org/10.1049/iet-epa.2009.0040
Uddin, J., Kang, M., Nguyen, D. v., & Kim, J. M. (2014). Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/814593
Utne, I. B., Brurok, T., & Rødseth, H. (2012). A structured approach to improved condition monitoring. Journal of Loss Prevention in the Process Industries, 25(3), 478–488. https://doi.org/10.1016/j.jlp.2011.12.004
Velasquez, M., & Hester, P. T. (2013). An Analysis of Multi-Criteria Decision Making Methods. In International Journal of Operations Research (Vol. 10, Issue 2).
Vitek, O., Janda, M., Hajek, V., & Bauer, P. (2011). Detection of eccentricity and bearings fault using stray flux monitoring. SDEMPED 2011 - 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, 456–461. https://doi.org/10.1109/DEMPED.2011.6063663
Wang, Y., & Wang, P. (2013, April 8). Cost Benefit Analysis of Condition Monitoring Systems for Optimal Maintenance Decision Making. 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. https://doi.org/10.2514/6.2013-1942
Yaseen, Md., Swathi, D., & Kumar, T. A. (2017). IoT based condition monitoring of generators and predictive maintenance. 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 725–729. https://doi.org/10.1109/CESYS.2017.8321176
Ye, Z., & Wu, B. (2000). A review on induction motor online fault diagnosis. Proceedings - IPEMC 2000: 3rd International Power Electronics and Motion Control Conference, 3, 1353–1358. https://doi.org/10.1109/IPEMC.2000.883050
Yu, G. (2020). A Concentrated Time-Frequency Analysis Tool for Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 69(2), 371–381. https://doi.org/10.1109/TIM.2019.2901514
Zhang, P. L., Li, B., Mi, S. S., Zhang, Y. T., & Liu, D. S. (2012). Bearing fault detection using multi-scale fractal dimensions based on morphological covers. Shock and Vibration, 19(6), 1373–1383. https://doi.org/10.3233/SAV-2012-0679