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AUTHOR(S): 

Dayo S. Ogunyale, Rene V. Mayorga

 

TITLE

A Fuzzy Inference Systems Approach for Risk Evaluation of Dairy Products Manufacturing Systems

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ABSTRACT

The objective of this Paper is to analyse the risk level of dairy products manufacturing systems at different categories (Physical, Biological, Chemical, and Environmental) of the operation, and the final risk evaluation of the manufacturing system. Five Mamdani Fuzzy Inference System (FIS) models are proposed to solve this problem. Mamdani FIS has been proven to be a great tool to assess risk at different levels. The world is evolving and growing every day and the need for dairy products are becoming more evident and essential to human. Furthermore, the higher consumption rate of dairy products by people of different ages has attracted investors because of its economic values. Considering this growth and its economic benefits, the understanding of the risk involved in dairy products manufacturing processes is highly required. The model provides a deep insight on how to mitigate the risks involved in dairy products manufacturing systems. Models were experimented using experimental data to validate the model.

 

KEYWORDS

Fuzzy, Mamdani Fuzzy Inference System, Linguistic terms, Risk Evaluation, Dairy Products, Failure Mode and Effects Analysis

 

REFERENCES

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[2] Hassan, M. N., Osborn, A. M., & Hafeez, F. Y. (2010). Molecular and biochemical characterization of surfactin producing Bacillus species antagonistic to Colletotrichum falcatum Went causing sugarcane red rot. African Journal of Microbiology Research, 4(20), 2137-2142

[3] Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence.

[4] Gargama, H., & Chaturvedi, S. K. (2011). Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60(1), 102-110.

[5] Yang, Z., Bonsall, S., & Wang, J. (2008). Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA. IEEE Transactions on Reliability, 57(3), 517-528.

[6] Amendola, A. (1986). Uncertainties in systems reliability modeling: Insight gained through European Benchmark exercises. Nuclear Engineering and Design, 93(2-3), 215-225

Cite this paper

Dayo S. Ogunyale, Rene V. Mayorga. (2017) A Fuzzy Inference Systems Approach for Risk Evaluation of Dairy Products Manufacturing Systems. International Journal of Control Systems and Robotics, 2, 86-91

 

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