Abstract
Connected vehicle fleets are deployed worldwide in several industrial internet of things scenarios. With the gradual increase of machines being controlled and managed through networked smart devices, the predictive maintenance potential grows rapidly. Predictive maintenance has the potential of optimizing uptime as well as performance such that time and labor associated with inspections and preventive maintenance are reduced. It provides better cost benefit ratios in terms of business profits. In order to understand the trends of vehicle faults with respect to important vehicle attributes viz mileage, age, vehicle type etc. this problem is addressed through hierarchical modified fuzzy support vector machine which acts as predictive analytics engine for this problem. The proposed method is compared with other commonly used approaches like logistic regression, random forests and support vector machines. This helps better implementation of telematics data to ensure preventative management as part of the desired solution. The superiority of the proposed method is highlighted through several experimental results.
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References
McKinsey & Company: Connected car, automotive value chain unbound, Advanced Industries. McKinsey & Company (2014)
Intel: Connected and immersive vehicle systems go from development to production faster. Intel (2016)
Intel: Designing next-generation telematics solutions, White Paper In-Vehicle Telematics. Intel (2016)
Abel, R.: Uber, Intel and IoT firms join coalition to secure connected cars (2017)
Vogt, A.: Industrie 4.0/IoT vendor benchmark 2017: An Analysis by Experton Group AG, An ISG Business, Munich, Germany (2017)
Predictive maintenance. https://en.wikipedia.org/wiki/Predictive_maintenance
Chowdhury, M., Apon, A., Dey, K. (eds.): Data Analytics for Intelligent Transportation Systems. Elsevier, New York (2017)
Chaudhuri, A.: Some investigations in predictive maintenance for industrial IoT solutions. Technical report, Samsung R & D Institute, Delhi, India (2021)
Chaudhuri, A.: Modified fuzzy support vector machine for credit approval classification. AI Commun. 27(2), 189–211 (2014)
Chaudhuri, A., De, K.: Fuzzy support vector machine for bankruptcy prediction. Appl. Soft Comput. 11(2), 2472–2486 (2011)
Chaudhuri, A.: Studies in applications of soft computing to some optimization problems, Ph.D. Thesis, Netaji Subhas University, Kolkata, India (2010)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)
Cortes, C., Vapnik, V.N.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
Preventive maintenance. https://en.wikipedia.org/wiki/Preventive_maintenance
Lathrop, A.: Preventing Failures with Predictive Maintenance: High-Performance Solutions Using the Microsoft Data Platform. BlueGranite, Portage (2017)
Rao, S.S.: Engineering Optimization: Theory and Practice, 4th edn. Wiley, New York (2013)
Boixader, D., Jacas, J., Recasens, J.: Upper and lower approximations of fuzzy sets. Int. J. Gen Syst 29(4), 555–568 (2000)
Lin, H.T., Lin, C.J.: A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO type methods. Technical report, Department of Computer Science and Information Engineering, National Taiwan University (2003)
Tang, Y., Guo, W., Gao, J.: Efficient model selection for support vector machine with Gaussian kernel function. In Proceedings of IEEE Symposium on Computational Intelligence and Data Mining, pp. 40–45 (2009)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer Verlag, New York (1995)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Schölkopf, B.: Support vector learning. Ph.D. dissertation, Technische Universität Berlin, Germany (1997)
Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers, Boston, MA (2001)
Chaudhuri, A., Ghosh, S.K.: Hierarchical rough fuzzy self organizing map for weblog prediction. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) CSOC 2017. AISC, vol. 573, pp. 188–199. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57261-1_19
Batuwita, R., Palade, V.: Class imbalance learning methods for support vector machines. In: He, H., Ma, Y. (eds.) Imbalanced learning: foundations, algorithms, and applications, pp. 83–101. John Wiley and Sons Inc., Hoboken, New Jersey (2012)
Veropoulos, K., Campbell, C., Cristianini, N.: Controlling the sensitivity of support vector machines, In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 55–60 (1999)
Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_7
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Chaudhuri, A., Ghosh, S.K. (2021). Predictive Maintenance of Vehicle Fleets Using Hierarchical Modified Fuzzy Support Vector Machine for Industrial IoT Datasets. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_28
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DOI: https://doi.org/10.1007/978-3-030-86271-8_28
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