Mechanical Process Control and Statistical Process Control for Reducing Butter-Oil Defects in Industrial Production

Authors

  • Suresh Kumar Sahani Faculty of Science, Technology, and Engineering, Rajarshi Janak University, Janakpurdham, Nepal
  • Ravi Kumar Raj Department of Management, Rajarshi Janak University, Janakpurdham, Nepal
  • K. Sathishkumar Assistant Professor, PG and Research Department of Computer Science, Erode Arts and Science College (Autonomous), Erode – 638009, Tamil Nadu, India
  • G N Keshava Murthy Electronics and Instrumentation Engineering Department, Siddaganga Institute of Technology, Tumakuru
  • Manojkumar S B Department of ECE, BGS Institute of Technology, Adichunchanagiri University, B G Nagara, Mandya, Karnataka, India
  • Naveen K B Department of ECE, BGS Institute of Technology, Adichunchanagiri University, B G Nagara, Mandya, Karnataka, India
  • Binod Kumar Sah R.R.M.C., T.U., Nepal
  • A. Jayanthiladevi Department of ECE, BGS Institute of Technology, Adichunchanagiri University, B G Nagara, Mandya, Karnataka, India
  • Pintu Mandal Faculty of Science, Technology, and Engineering, Rajarshi Janak University, Janakpurdham, Nepal
  • Kameshwar Sahani Department of Civil Engineering, School of Engineering, Kathmandu University, Dhulikhel, Nepal

DOI:

https://doi.org/10.31181/rme575

Keywords:

Reliability, Availability, Butter-Oil Processing, Serial Process, Markov Analysis, Mean Time between Failures (MTBF), Mean Time to Repair (MTTR)

Abstract

This study provides a quantitative assessment of the reliability and availability of a serial butter-oil manufacturing line from a mechanical engineering perspective. The line comprises critical rotating and fluid-handling subsystems raw-milk receiving pumps, cream separators, heat exchangers, mechanical agitators, homogenizers, gearboxes, and conveying components whose mechanical integrity profoundly impacts throughput and quality. We characterize the production train using continuous-time Markov chains (CTMCs) based on a reliability block diagram (RBD) that depicts series dependencies and maintenance plans. Component-level failure (λ) and repair (μ) rates, derived from realistic duty cycles and mechanical failure modes (bearing wear, seal leakage, misalignment, lubrication starvation, cavitation, fouling, and thermomechanical fatigue), are conveyed to system-level metrics via state-transition analysis. Numerical simulations yield mean time between failures (MTBF), mean time to repair (MTTR), and steady-state availability for the entire system and for mechanically essential subsystems (pump-valve assemblies and homogenization units). Sensitivity analyses identify availability limitations and priorities mechanical factors bearing L10 lifespan, seal mean time between failures, lubricant replacement interval, alignment tolerance, and cooling-water temperature differential according to their impact on throughput decrease. The results suggest that minor improvements in repair logistics for high-criticality assets (such as the use of cartridge seals and quick-release couplings on feed pumps) might exceed substantial reductions in MTBF for low-criticality components. We demonstrate that the integration of mechanical condition monitoring (vibration and temperature trending) with SPC-based run charts and control limits stabilizes essential mechanical variables (overall vibration, RMS acceleration, and discharge pressure ripple), thereby reducing special-cause variation and unanticipated downtime. The study concludes with maintenance strategies for mechanical assets, encompassing spares pooling for prevalent failure categories, precision alignment, optimized lubrication practices, and threshold-based, condition-directed overhauls, yielding measurable improvements in line availability and energy efficiency while upholding butter-oil quality standards.

References

Baskan, O. (2013). Determining optimal link capacity expansions in road networks using cuckoo search algorithm with lévy flights. Journal of Applied Mathematics, 2013(1), 718015. https://doi.org/10.1155/2013/718015

Buaklee, W., & Hongesombut, K. (2013). Optimal DG Allocation in a Smart Distribution Grid Using Cuckoo Search Algorithm. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 11(2), 16-22. https://doi.org/10.1109/ECTICon.2013.6559624

dos Santos Coelho, L. (2009). An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications. Reliability Engineering & System Safety, 94(4), 830-837. https://doi.org/10.1016/j.ress.2008.09.001

Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the sixth international symposium on micro machine and human science (pp. 39-43). Ieee. https://doi.org/10.1109/MHS.1995.494215

Fouad, M. M., Hafez, A. I., Hassanien, A. E., & Snasel, V. (2015). Grey wolves optimizer-based localization approach in WSNs. In 2015 11th International Computer Engineering Conference (ICENCO) (pp. 256-260). IEEE. https://doi.org/10.1109/ICENCO.2015.7416358

Garg, H., & Sharma, S. (2012). Behavior analysis of synthesis unit in fertilizer plant. International Journal of Quality & Reliability Management, 29(2), 217-232. https://doi.org/10.1108/02656711211199928

Gupta, E., & Saxena, A. (2016). Grey wolf optimizer based regulator design for automatic generation control of interconnected power system. Cogent Engineering, 3(1), 1151612. https://doi.org/10.1080/23311916.2016.1151612

Jayabarathi, T., Raghunathan, T., Adarsh, B., & Suganthan, P. N. (2016). Economic dispatch using hybrid grey wolf optimizer. Energy, 111, 630-641. https://doi.org/10.1016/j.energy.2016.05.105

Kamboj, V. K., Bath, S., & Dhillon, J. (2016). Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer. Neural computing and Applications, 27(5), 1301-1316. https://doi.org/10.1007/s00521-015-1934-8

Kohli, M., & Arora, S. (2018). Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 5(4), 458-472. https://doi.org/10.1016/j.jcde.2017.02.005

Kumar, A., Sharma, S., & Kumar, D. (2013). Performance analysis of repairable system using GA and fuzzy Lambda-Tau methodology. International Journal of Quality & Reliability Management, 30(9), 1017-1032. https://doi.org/10.1108/IJQRM-06-2013-0104

Li, D., & Haimes, Y. Y. (1992). A decomposition method for optimization of large-system reliability. IEEE Transactions on Reliability, 41(2), 183-188. https://doi.org/10.1109/24.257778

Li, L., Sun, L., Kang, W., Guo, J., Han, C., & Li, S. (2016). Fuzzy Multilevel Image Thresholding Based on Modified Discrete Grey Wolf Optimizer and Local Information Aggregation. IEEE Access, 4, 6438-6450. https://doi.org/10.1109/ACCESS.2016.2613940

Long, W., Wu, T., Cai, S., Liang, X., Jiao, J., & Xu, M. (2019). A Novel Grey Wolf Optimizer Algorithm With Refraction Learning. IEEE Access, 7, 57805-57819. https://doi.org/10.1109/ACCESS.2019.2910813

Manikandan, S., Manimegalai, R., & Hariharan, M. (2016). Gene Selection from microarray data using binary grey wolf algorithm for classifying acute leukemia. Current Signal Transduction Therapy, 11(2), 76-83. https://doi.org/10.2174/1574362411666160607084415

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. d. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119. https://doi.org/10.1016/j.eswa.2015.10.039

Mosavi, M. R., Khishe, M., & Ghamgosar, A. (2016). Classification of sonar data set using neural network trained by gray wolf optimization. Neural Network World, 26(4), 393. https://doi.org/10.14311/NNW.2016.26.023

Negi, G., Kumar, A., Pant, S., & Ram, M. (2021). Optimization of complex system reliability using hybrid grey wolf optimizer. Decision Making: Applications in Management and Engineering, 4(2), 241-256. https://doi.org/10.31181/dmame210402241n

Ramírez-Rosado, I. J., & Bernal-Agustín, J. L. (2001). Reliability and costs optimization for distribution networks expansion using an evolutionary algorithm. IEEE Transactions on Power Systems, 16(1), 111-118. https://doi.org/10.1109/59.910788

Uprety, I. (2012). Stochastic analysis of a reheating-furnace system subject to preventive maintenance and repair. International Journal of Operational Research, 13(3), 256-280. https://doi.org/10.1504/IJOR.2012.045664

Verma, S. M., & Chari, A. (1991). Availability and frequency of failures of a system in the presence of chance common-cause shock failures. Microelectronics Reliability, 31(2-3), 265-269. https://doi.org/10.1016/0026-2714(91)90211-O

Wolpert, D. H., & Macready, W. G. (1997). No Free Lunch Theorems for Optimization. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 1(1), 67. https://doi.org/10.1109/4235.585893

Zhang, S., Zhou, Y., Li, Z., & Pan, W. (2016). Grey wolf optimizer for unmanned combat aerial vehicle path planning. Advances in engineering software, 99, 121-136. https://doi.org/10.1016/j.advengsoft.2016.05.015

Downloads

Published

2026-04-22

Issue

Vol. 7 No. 1 (2026): Reports in Mechanical Engineering

Section

Articles

License

Copyright (c) 2026 Reports in Mechanical Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mechanical Process Control and Statistical Process Control for Reducing Butter-Oil Defects in Industrial Production. (2026). Reports in Mechanical Engineering, 7(1), 169-184. https://doi.org/10.31181/rme575