Dinesh Kumar Kushwaha, Dilbagh Panchal, Anish Sachdeva

DOI Number
First page
Last page


The proposed work presents a novel integrated decision framework, based on Intuitionistic Fuzzy (IF)- Failure Mode & Effect Analysis (IF-FMEA), and IF-Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS) approaches for analysing the failure risk issues of Turbine and Alternator Unit (TAU) in a chemical treatment-based sugar process industry. The proposed novel IF-FMEA approach-based modelling overcomes the various demerits of traditional FMEA approaches which are faced during the identification of critical failure causes based on Risk Priority Number (RPN) outputs. On the basis of detailed qualitative information related to plant operation, FMEA sheet was developed and linguistic ratings were collected against three risk factors such as probability of Occurrence (O), Severity (S), and Detection (D). IF- Hybrid Weighted Euclidean Distance (IFHWED) score has been computed to rank all listed failure causes under three risk factors. The ranking results based on IF-FMEA approach has been compared with the well existed IF-TOPSIS approach for evaluating the accuracy of proposed modelling results. Sensitivity analysis has been also done for checking the robustness of the framework. The analysis results were provided to maintenance executives of the TAU unit to frame optimum maintenance plan for overcoming the problems of sudden breakdown. The analysis results are also applicable to TAU systems which are installed in other chemical process industries globally. 


Sugar process industry, IF-FMEA, IF-TOPSIS, Failure causes, Sudden breakdown, Maintenance schedule

Full Text:



Sharma, R.K., Sharma, P., 2012, Integrated framework to optimize RAM and cost decisions in a process plant, Journal of Loss Prevention in the Process Industries, 25(6), pp. 883-904.

Panchal, D., Chatterjee, P., Sharma, R., Garg, R.K., 2021, Sustainable oil selection for cleaner production in Indian foundry industries: A three phase integrated decision-making framework, Journal of Cleaner Production, 313, 127827.

Mccloskey, T.H., 1995, Troubleshooting Bearing and Lube Oil System Problems, In Proceedings of the 24th Turbomachinery Symposium. Texas A&M University, Turbomachinery Laboratories, pp. 147-166.

Prabhakaran, A., Jagga, C.R., 1999, Condition monitoring of steam turbine-generator through contamination analysis of used lubricating oil, Tribology International, 32(3), pp. 145-152.

Ahn, H.S., Yoon, E.S., Sohn, D.G., Kwon, O.K., Shin, K.S., Nam, C.H., 1996, Practical contaminant analysis of lubricating oil in a steam turbine-generator, Tribology International, 29(2), pp. 161-168.

Feili, H.R., Akar, N., Lotfizadeh, H., Bairampour, M., Nasiri, S., 2013, Risk analysis of geothermal power plants using Failure Modes and Effects Analysis (FMEA) technique, Energy Conversion and Management, 72, pp. 69-76.

Liu, H.C., Liu, L., Lin, Q.L., 2013, Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology, IEEE Transactions on Reliability, 62(1), pp. 23-36.

Liu, H.C., You, J.X., Ding, X.F., Su, Q., 2015, Improving risk evaluation in FMEA with a hybrid multiple criteria decision making method, International Journal of Quality and Reliability Management, 32(7), pp. 763-782.

Rah, J.E., Manger, R.P., Yock, A.D., Kim, G.Y., 2016, A comparison of two prospective risk analysis methods: Traditional FMEA and a modified healthcare FMEA, Medical Physics, 43(12), pp. 6347-6353.

Hosseini, N., Givehchi, S., Maknoon, R., 2020, Cost-based fire risk assessment in natural gas industry by means of fuzzy FTA and ETA, Journal of Loss Prevention in the Process Industries, 63, 104025.

Xu, B., Chen, D., Li, H., Zhuang, K., Hu, X., Li, J., Skjelbred, H.I., 2019, Priority analysis for risk factors of equipment in a hydraulic turbine generator unit, Journal of Loss Prevention in the Process Industries, 58, pp. 1–7.

Sarkar, S., Quddus, N., Mannan, M.S., El-Halwagi, M.M., 2021, Integrating flare gas with cogeneration systems: Operational risk assessment, Journal of Loss Prevention in the Process Industries, 72, 104571.

Liu, H.C., You, J.X., You, X.Y., Shan, M.M., 2015, A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method, Applied Soft Computing Journal, 28, pp. 579–588.

Panchal, D., Kumar, D., 2016, Stochastic behaviour analysis of power generating unit in thermal power plant using fuzzy methodology, Opsearch, 53(1), pp. 16-40.

Adar, E., Ince, M., Karatop, B., Bilgili, M.S., 2017, The risk analysis by failure mode and effect analysis (FMEA) and fuzzy-FMEA of supercritical water gasification system used in the sewage sludge treatment, Journal of Environmental Chemical Engineering, 5(1), pp. 1261-1268.

Ahn, J., Noh, Y., Park, S.H., Choi, B. I., Chang, D., 2017, Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion, Journal of Power Sources, 364, pp. 226-233.

Panchal, D., Jamwal, U., Srivastava, P., Kamboj, K., Sharma, R., 2018, Fuzzy methodology application for failure analysis of transmission system, International Journal of Mathematics in Operational Research, 12(2), pp. 220–237.

Fattahi, R., Khalilzadeh, M., 2018, Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment, Safety Science, 102, pp. 290-300.

Panchal, D., Singh, A.K., Chatterjee, P., Zavadskas, E.K., Keshavarz-Ghorabaee, M., 2019, A new fuzzy methodology-based structured framework for RAM and risk analysis, Applied Soft Computing Journal, 74, pp. 242–254.

Boral, S., Howard, I., Chaturvedi, S.K., McKee, K., Naikan, V.N.A., 2020, A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An essential requirement for sustainable manufacturing, Sustainable Production and Consumption, 21, pp. 14-32.

Gopal, N., Panchal, D., 2021, A structured framework for reliability and risk evaluation in the milk process industry under fuzzy environment, Facta Universitatis-Series Mechanical Engineering, 19(2), pp. 307–333.

Wang, L.E., Liu, H.C., Quan, M.Y., 2016, Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments, Computers and Industrial Engineering, 102, pp. 175-185.

Baig, M.M.U., Ali, Y., Rehman, O.U., 2022, Enhancing Resilience of Oil Supply Chains in Context of Developing Countries, Operational Research in Engineering Sciences: Theory and Applications, 5(1), pp. 69-89.

Muhammad, L.J., Badi, I., Haruna, A.A., Mohammed, I.A., 2021, Selecting the best municipal solid waste management techniques in Nigeria using multi criteria decision making techniques, Reports in Mechanical Engineering, 2(1), pp.180-189.

Efe, B., 2019, Analysis of operational safety risks in shipbuilding using failure mode and effect analysis approach, Ocean Engineering, 187, 106214.

Kushwaha, D.K., Panchal, D., Sachdeva, A., 2020, Risk analysis of cutting system under intuitionistic fuzzy environment, Reports in Mechanical Engineering, 1(1), pp. 162-173.

Ilbahar, E., Kahraman, C., Cebi, S., 2021, Risk Assessment of Renewable Energy Investments: A Modified Failure Mode and Effect Analysis Based on Prospect Theory and Intuitionistic Fuzzy AHP, Energy, 239, 121907.

Garg, H., Rani, M., Sharma, S.P., 2013, Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda-Tau methodology, Applied Soft Computing Journal, 13(4), pp. 1869-1881.

Garg, H., 2014, Performance and behavior analysis of repairable industrial systems using Vague Lambda-Tau methodology, Applied Soft Computing Journal, 22, pp. 323-338.

Chen, Z., Wu, X., Qin, J., 2014, Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree, Journal of Loss Prevention in the Process Industries, 32, pp. 349-357.

Panchal, D., Kumar, D., 2016, Integrated framework for behaviour analysis in a process plant, Journal of Loss Prevention in the Process Industries, 40, pp. 147-161.

George, J.J., Renjith, V.R., George, P., George, A.S., 2019, Application of fuzzy failure mode effect and criticality analysis on unloading facility of LNG terminal, Journal of Loss Prevention in the Process Industries, 61, pp. 104-113.

Sangode, P.B., Metre, S.G., 2020, Power distribution operational risk model driven by FMEA and ISM approach, International Journal of Quality and Reliability Management, 38(7), pp. 1445-1465.

Das, I., Panchal, D., Tyagi, M., 2021, A novel PFMEA-Doubly TOPSIS approach-based decision support system for risk analysis in milk process industry, International Journal of Quality & Reliability Management, 39(1), doi:10.1108/ijqrm-10-2019-0320.

Mardani Shahri, M., Eshraghniaye Jahromi, A., Houshmand, M., 2021, Failure Mode and Effect Analysis using an integrated approach of clustering and MCDM under Pythagorean fuzzy environment, Journal of Loss Prevention in the Process Industries, 72, 104591.

Widjajanto, S., Rimawan, E., 2021, Modified failure mode and effect analysis approaching to improve organization performance based on Baldrige criteria-A case study of an electro-medic industry, Operational Research in Engineering Sciences: Theory and Applications, 4(3), pp. 39-58.

Xu, Z., 2005, An overview of methods for determining OWA weights, International Journal of Intelligent Systems, 20(8), pp. 843-865.

Hwang, C.L., Yoon, K., 1981, Multiple Attribute Decision Making Methods and Applications A State-of.the-Art Survey, Springer, Berlin, Heidelberg, New York.

Kumar, S., Maity, S.R. Patnaik, L., 2022, Optimization of Wear Parameters for Duplex-TiAlN Coated MDC-K Tool Steel Using Fuzzy MCDM Techniques, Operational Research in Engineering Sciences: Theory and Applications, 5(3), pp. 40-67.

Bozanic, D., Tešić, D., Marinković, D., Milić, A., 2021, Modeling of neuro-fuzzy system as a support in decision-making processes, Reports in Mechanical Engineering, 2(1), pp. 222-234.

Kushwaha, D.K., Panchal, D., Sachdeva, A., 2022, Intuitionistic fuzzy modelling-based integrated framework for performance analysis of juice clarification unit, Applied Soft Computing, 124, 109056.

Arora, H.D., Naithani, A., 2022, Significance of TOPSIS approach to MADM in computing exponential divergence measures for Pythagorean fuzzy sets, Decision Making: Applications in Management and Engineering, 5(1), pp. 246-263.

Tutak, M., Brodny, J., 2022, Evaluating differences in the level of working conditions between the European Union Member States using TOPSIS method, Decision Making: Applications in Management and Engineering, 5(2), pp. 1-29.


  • There are currently no refbacks.

ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4