Abstract
To promote the efficiency of fault diagnosis and improve user experience, we proposed an aided vehicle fault diagnosis method based on user’s fault description. According to the description of faults phenomenon, we combined both natural language processing technologies and vehicle structure knowledge to diagnose to locate the fault. First, we collected descriptions of failure from users and made a standardized processing of vehicle fault to build the vehicle fault database and summarized a vehicle proper noun dictionary. Second, according to the design of vehicle, we create a vehicle structure tree and combined both distribution statistics and topic semantic which to build a semantic vector model of vehicle fault description. Finally, we use cosine distance to evaluate the semantic similarity of fault description, based on the result, we can conclude the reason of fault and location. The experiment shows that the precision of fault diagnosis has reached 86.7% and the precision of fault locating has also reached 81.8%. The vehicle diagnosis method will help providers of vehicle maintenance to build a self-service fault inquiry system based on the database from internet. It can collect user’s maintenance requirements ahead, optimizing the service progress and improving the service efficiency.
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References
Shumate, D.M.: Vehicle diagnosis system and method. US Patent 7,590,476, 15 September 2009
Xue, M.: The research on intelligent vehicle fault diagnosis platform. North China University of Science and Technology (2019)
Xia, S.: The research of vehicle fault diagnosis method based on fuzzy fault tree. Hubei University of Technology (2017)
Narasimhan, S., Biswas, G.: Model-based diagnosis of hybrid systems. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 37(3), 348–361 (2007)
Sun, J.: Jieba’ Chinese word segmentation tool (2012)
Li, Z., Sun, M.: Punctuation as implicit annotations for Chinese word segmentation. Comput. Linguist. 35(4), 505–512 (2009)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150. Association for Computational Linguistics (2011)
Erk, K.: Vector space models of word meaning and phrase meaning: a survey. Lang. Linguist. Compass 6(10), 635–653 (2012)
Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, Piscataway, New Jersey, vol. 242, pp. 133–142 (2003)
Zhang, W., Yoshida, T., Tang, X.: A comparative study of TF* IDF, lSI and multiwords for text classification. Expert Syst. Appl. 38(3), 2758–2765 (2011)
Dumais, S.T., et al.: Latent semantic indexing (lSI) and trec-2, p. 105. Nist Special Publication Sp (1994)
Hofmann, T.: Probabilistic latent semantic indexing. ACM SIGIR Forum 51(2), 211–218 (2017)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(2–3), 259–284 (1998)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)
Tata, S., Patel, J.M.: Estimating the selectivity of TF-IDF based cosine similarity predicates. ACM Sigmod Rec. 36(2), 7–12 (2007)
Dehak, N., Dehak, R., Glass, J.R., Reynolds, D.A., Kenny, P.: Cosine similarity scoring without score normalization techniques. In: Odyssey, p. 15 (2010)
Roy, R., Whelan, R.: Successful recycling through value-chain collaboration. Long Range Plan. 25(4), 62–71 (1992)
Normann, R., Ramirez, R.: From value chain to value constellation: designing interactive strategy. Harvar. Bus. Rev. 71(4), 65–77 (1993)
Dania, W.A.P., Xing, K., Amer, Y.: Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review. J. Clean. Prod. 186, 851–864 (2018)
Acknowledgement
The authors would like to express their thanks to the editors and experts who participated in the review of the paper for their valuable suggestions and comments. This research was supported by National Key Research and Development Project--Multi core value chain collaborative business technology resources and service integration technology (2017YFB1400902).
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Cai, X., Wu, W., Bo, W., Zhang, C. (2021). An Aided Vehicle Diagnosis Method Based on the Fault Description. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_21
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DOI: https://doi.org/10.1007/978-981-16-2540-4_21
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