A Structured Analytical Framework for Failure Prioritization Using FMEA in Mechanical Manufacturing Systems
Keywords:
Failure Mode and Effects Analysis (FMEA), Failure Prioritization, Mechanical Manufacturing Systems, Risk Priority Number (RPN), Predictive Maintenance, Reliability Engineering.Abstract
This paper presents a structured review and analytical framework for failure prioritization using Failure Mode and Effects Analysis (FMEA) in mechanical manufacturing systems, supported by real-world industrial case studies. The study combines subsystem-level analysis with documented applications from automotive, aerospace, steel, and heavy equipment manufacturing, including systems comparable to those used by Toyota, Boeing, Tata Steel, Siemens, Caterpillar, and Mahindra. Key mechanical subsystems such as bearing assemblies, spindle systems, hydraulic units, and gear trains are evaluated using Severity, Occurrence, and Detection parameters to calculate Risk Priority Numbers (RPN). The case study analysis highlights consistent failure patterns across industries, with fatigue, wear, and thermal stress emerging as dominant contributors to high-priority failures. The results demonstrate that structured implementation of FMEA leads to measurable improvements, including reductions in unscheduled downtime, enhancement in product quality, and optimization of maintenance practices. These improvements are consistently observed across different industrial domains, confirming the applicability of FMEA as a generalized failure prioritization tool. The study further discusses the limitations of conventional RPN-based approaches and highlights the need for integration with predictive maintenance, real-time monitoring, and data-driven technologies. The findings establish FMEA as a practical and scalable methodology for failure prioritization in modern manufacturing environments.
Downloads
References
[1] U.S. Department of Defense, MIL-STD-1629A: Procedures for Performing a Failure Mode, Effects and Criticality Analysis, Washington, DC, 1980.
[2] International Electrotechnical Commission (IEC), IEC 60812: Failure Modes and Effects Analysis (FMEA and FMECA), Geneva, Switzerland, 2018.
[3] AIAG & VDA, Failure Mode and Effects Analysis (FMEA) Handbook, Automotive Industry Action Group & Verband der Automobilindustrie, 2019.
[4] International Organization for Standardization (ISO), ISO 31000: Risk Management – Guidelines, Geneva, Switzerland, 2018.
[5] D. H. Stamatis, Failure Mode and Effect Analysis: FMEA from Theory to Execution, 2nd ed., ASQ Quality Press, 2003.
[6] J. B. Bowles and C. E. Pelaez, “Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis,” Reliability Engineering & System Safety, vol. 50, no. 2, pp. 203–213, 1995.
[7] A. Pillay and J. Wang, “Modified failure mode and effects analysis using approximate reasoning,” Reliability Engineering & System Safety, vol. 79, no. 1, pp. 69–85, 2003.
[8] H.-C. Liu, L. Liu, and N. Liu, “Risk evaluation approaches in failure mode and effects analysis: A literature review,” Expert Systems with Applications, vol. 40, no. 2, pp. 828–838, 2013.
[9] S. Carpitella, M. Certa, J. Benítez, and D. Izquierdo, “Managing human factors in the reliability of production systems,” Computers & Industrial Engineering, vol. 119, pp. 438–451, 2018.
[10] R. Kumar, D. Kumar, and P. Kumar, “FMEA-based risk assessment in manufacturing industry,” International Journal of Engineering Research, vol. 9, no. 5, pp. 234–240, 2020.
[11] F. Tao, Q. Qi, L. Wang, and A. Y. C. Nee, “Digital twins and cyber–physical systems toward smart manufacturing,” Engineering, vol. 5, no. 4, pp. 653–661, 2019.
[12] G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, “Machine learning for predictive maintenance: A multiple classifier approach,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812–820, 2015.
[13] G. W. Vogl, B. A. Weiss, and M. Donmez, “Standards for predictive maintenance,” Procedia CIRP, vol. 56, pp. 54–59, 2016.
[14] R. K. Mobley, An Introduction to Predictive Maintenance, 2nd ed., Butterworth-Heinemann, 2002.
[15] E. Zio, The Monte Carlo Simulation Method for System Reliability and Risk Analysis, Springer, 2013.
[16] A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006.
[17] J. Lee, B. Bagheri, and H. A. Kao, “A cyber-physical systems architecture for Industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015.
[18] S. Mhatre and A. Somatkar, “Risk Analysis and Process Optimization in Toy Manufacturing Using FMEA, Six Sigma, and Statistical Process Control,” International Research Journal of Innovation in Science and Technology (IRJIST), vol. 1, no. 2, 2026.
[19] K. S. Chaudhari, N. B. More, and A. Somatkar, “Risk Assessment in Automated Manufacturing Systems: A Hybrid Framework for Industry 4.0 and Beyond,” International Research Journal of Innovation in Science and Technology (IRJIST), vol. 1, no. 2, 2026.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yash Amol Pawar, Abhijeet Pramod Desai, Prajwal Hanumant Shivtare (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in the International Research Journal of Innovation in Science and Technology (IRJIST) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors retain the copyright of their work and grant the journal the right of first publication. Proper attribution to the authors and the journal is required for any reuse of the published content.
