A Fuzzy Logic-Based Automobile Fault Detection System Using Mamdani Algorithm
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Due to advancement and complexity of modern automobiles, fault detection has gone beyond manual or trial by error methods. The fault detection technologies in automotive industry is used to identify any potential or already existing fault in automobiles. Faults in automobiles are usually mechanical or electrical faults that may include airbag control unit, radiator, gearbox, transmission control unit, tyre pressure, brakes, air conditioner, cylinder casket, alternator, hubs malfunctions etc. Each fault has a specific or related sign and symptoms. There are several methods of fault detections in automobiles like the binary logic technique, the fuzzy logic method technique and artificial intelligence technique with different algorithms. In this research work, we employed a fuzzy logic based technique that uses a Mamdani Algorithm which presented a better fault detection mechanism. Mamdani’s algorithm was proposed by Ebrahim Mamdani as a fuzzy inference method which has a rule-bases that are more intuitive and easier to analyse and implement. Mamdani’s algorithm produces fuzzy sets that originate from fuzzy inference system’s output membership function for decision making. This research work is a web-based technology that was implemented using JavaScript, JQuery and SQL server, ASP.Net, Bootstrap 3.5 and CSS. The output of the system showed a greater improvement from other existing methods of fault detections in automobiles.
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Johnson, A. (2019). The collaboration of Public School and Teachertsin the Development of a StudentsM NursClin N Am. 2019;40(4):771–8.
Igbak, H.S, (2022). AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems, Swinburne University of Technology, Melbourne, VIC 3122 Australia, National Medical Library.
Jovetic, A. (2019). Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms. Claribis Prints. Settle USA. Pp. 67
Race, B. (2020). Machine Learning Applications in Automotive Fault Detection: Challenges and Opportunities. International Journal of Vehicle Systems Modelling and Testing, Volume 5. Pp 67.
Mohammed, O. M. (2015). On Fault Detection, Diagnosis and Monitoring for Induction Motors Department of Computer Science and Electrical Engineering, Luleå University of Technology SE- 97 187 Luleå, Sweden, PhD Thesis
Boum, A. Maurice, N. Y. J. Nneme, L. N. Mbumda, L. M. (2018). Fault Diagnosis of an Induction Motor based on Fuzzy Logic, Artificial Neural Network and Hybrid System, International Journal of Control Science and Engineering 2018, 8(2): 42-51 DOI: 10.5923/j.control.20180802.03
Cessna, K. (2010). Software Collaboration: Prevention and Control of Bugs in Software Development. Josh Prints, New York City, USA. 1996;13(2):120–7.
Lucy, U. (2020). Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing Approaches. Josh Prints, New York City, USA. Pp. 56
Cao, S. G., Rees, N. W., & Feng, G. (2001). Mamdani-Type Fuzzy Controllers are Universal Fuzzy Controllers. Fuzzy Sets and Systems, 123(3), 359–367.
Pal, S. K., & Mandal, D. P. (2015). Fuzzy Logic and Approximate Reasoning: An Overview. IETE Journal of Research, 37(5–6), 548–560.
Qodar, G. L. (2020). The Application of Mamdani Method for Predicting The Best Portable Computer Based on Hardware and Price. Journal of Informatics and Telecommunication Engineering, 4(1), 33-47. doi:10.31289/jite.v4i1.3770
Filljov, K., Lucky. O. and Petrovic, I (2018). Intelligent Systems for Automotive Fault Detection: A Comprehensive Review. Journal of Intelligent Transportation Systems, Volume 3, Pp. 34.
Breman, L., Canon, O., James, P. and Lovelyn, L. (2015). Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing Approaches. TechPublish Co. Publishers, CA, USA.
Angles, M. G. C.; Rosas, P. K. Zúñiga, C. M. Sulla-Torres, J. (2022). Diagnostics in Tire Retreading Based on Classification with Fuzzy Inference System. Appl. Sci., 12, 9955. https://doi.org/ 10.3390/app12199955
Yadav, A, and Swetapadma A. (2015). A Novel Transmission Line Relaying Scheme for Fault Detection and Classification Using Wavelet Transform and Linear Discriminant Analysis. Ain Shams Eng J. 2015;6(1):199–209.
Saradarzadeh M, Sanaye-Pasand M (2014). An accurate fuzzy logic-based fault classification algorithm using voltage and current phase sequence components. Int Trans Electr Energy Syst 25(10):2275–2288
Jain, A. (2013). Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines. Adv Artif Intell.; 2013:1–12.
Dash, P.K. Pradhan, A.K. and Panda, G. (2000). A Novel Fuzzy Neural Network Based Distance Relaying Scheme. IEEE Trans Power Deliv.; 15(3):902–907.
Andani, S. R. (2013). Fuzzy Mamdani Dalammenentukan Tingkat Keberhasilan Dosen Mengajar, Seminar Nasiona
Kulkarni, M.; Abou, S.C.; Stachowicz, M. (2009). Fault detection in hydraulic system using fuzzy logic. In Proceedings of the World.
Panoiu, M.; Panoiu, C.; Lihaciu, I. (2018). Adaptive Neuro Fuzzy System for Modelling and Prediction of Distance Pantograph Catenary in Railway Transportation. In Proceedings of the Materials Science and Engineering; IOP Conference Series; IOP Publishing: Bristol, UK, Volume 294, p. 012073
Samavat, T. Mostafa, N. Mohsen, G. Morteza, A.N. Mohammad, Z. Padmanaban, S. and Baseem, K. (2023). A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System International Journal of Energy Research.
Herpratiwi, H. Maftuh, M. Winci, F. Ahmad, T. Musnar, I. D. Robbi, R (2022). Implementation and Analysis of Fuzzy Mamdani Logic Algorithm from Digital Platform and Electronic Resource. TEM Journal – Volume 11 / Number 3
Cao, S. G., Rees, N. W., & Feng, G. (2001). Mamdani-Type Fuzzy Controllers are Universal Fuzzy Controllers. Fuzzy Sets and Systems, 123(3), 359–367.
Almohammadi, K. (2020). Conceptual Framework Based On Type-2 Fuzzy Logic Theory for Predicting Childhood Obesity Risk. International Journal of Online & Biomedical Engineering, 16(3) doi:10.3991/IJOE.V16I03.12701
Li, S. Frey, M. Gauterin, F. (2023). Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems, https://www.mdpi.com/journal/machines
Li, J. (2022). Implementation of AI Methods in a Holistic Fault Diagnosis System for an Electric and Automated Vehicle. Master’s Thesis, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Pham, D. T. and Castellani, M. (2002). Action Aggregation and Defuzzification in Mamdani-Type Fuzzy Systems, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Sage Publication.
Zaghba, L. Khennane, M. Borni, A. and Fezzani, A. (2021). Intelligent PSO-Fuzzy MPPT Approach for Standalone PV System Under Real Outdoor Weather Conditions,” Algerian Journal of Renewable Energy and and Sustainable Development, vol. 3, no. 1, pp. 1–12.
Shiqing, L. Michael, F. and Frank, G. (2023). Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems, https://www.mdpi.com/journal/machines
Meskin, N. Khorasani, K. (2011). Fault Detection and Isolation: Multi-Vehicle Unmanned Systems; Springer Science & Business Media, Berlin/Heidelberg, Germany.
Shuma, A. Nidul, S. and Thingam, D. (2016). Fuzzy Logic Based On‑line Fault Detection and Classification in Transmission Line, SpringerPlus Publication
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