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http://dx.doi.org/10.5762/KAIS.2021.22.3.20

Deep Learning-Based Vehicle Anomaly Detection by Combining Vehicle Sensor Data  

Kim, Songhee (Department of Industrial & Systems Enginnering, Dongguk University)
Kim, Sunhye (Department of Industrial & Systems Enginnering, Dongguk University)
Yoon, Byungun (Department of Industrial & Systems Enginnering, Dongguk University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.3, 2021 , pp. 20-29 More about this Journal
Abstract
In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.
Keywords
Anomaly detection; CNN; LSTM; Vehicle; Deep learning;
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1 S.G. Kim, T.I. Oh, "Real-time PM10 Concentration Prediction LSTM Model Based on IoT Streaming Sensor Data" Korea Journal of the Korean Society of Science and Technology, Vol. 19, No. 11, pp. 310-318, 2018. DOI: https://doi.org/10.5762/KAIS.2018.19.11.310   DOI
2 B. Zong, Q. Song, M.R. Min, W. Cheng, C, Lumezanu, D. Cho, H. Chen, "Deep autoencoding gaussian mixture model for unsupervised anomaly detection", In Proceeding of International Conference on Learning Representations, 2018.
3 D. Li, D. Chen, J. Goh, S.K. Ng, "Anomaly detection with generative adversarial networks for multivariate time series", arXiv preprint arXiv:1809.04758, 2018.
4 C. Baur, B. Wiestler, S. Albarqouni, N. Navab, "Deep autoencoding models for unsupervised anomaly segmentation in brain MR images", In Proceeding of International MICCAI Brainlesion Workshop, pp. 161-169, 2018. DOI: https://doi.org/10.1007/978-3-030-11723-8_16   DOI
5 ,M.R. Moore, J.M. Vann. (2019, January). Anomaly detection of cyber physical network data using 2D images. In Proceeding of 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-5), 2019. DOI: https://doi.org/10.1109/ICCE.2019.8662084   DOI
6 Q. Wei, Y, Ren, R. Hou, B. Shi, J.Y. Lo, L. Carin, "Anomaly detection for medical images based on a one-class classification", In Proceeding of Medical Imaging 2018: Computer-Aided Diagnosis, 2018. DOI: https://doi.org/10.1117/12.2293408   DOI
7 M. Hasan, M.M. Islam, M.I.I. Zarif, M.M.A. Hashem, "Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches", Internet of Things, 7, 100059, 2019. DOI: https://doi.org/10.1016/j.iot.2019.100059   DOI
8 P. Malhotra, L. Vig, G. Shroff, P. Agarwal, "Long short term memory networks for anomaly detection in time series", In Proceedings of Presses universitaires de Louvain, Vol. 89, pp. 89-94, 2015.
9 M., Heinrich, A., Golz, T., Arul, S. Katzenbeisser, "Rule-based Anomaly Detection for Railway Signalling Networks", arXiv preprint arXiv:2008.05241, 2020.
10 H. Sarmadi, A. Karamodin, "A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-c lass kNN rule for structural health monitoring under environmental effects.", Mechanical Systems and Signal Processing, Vol. 140, 106495, 2020. DOI: https://doi.org/10.1016/j.ymssp.2019.106495   DOI
11 S. Lawrence, C.L. Giles, A.C. Tsoi, A.D. Back, "Face recognition: A convolutional neural-network approach." IEEE transactions on neural networks, Vol. 8, No. 1, pp. 98-113, 1997. DOI: https://doi.org/110.1109/72.554195   DOI
12 M. Xia, T. Li, L. Xu, L. Liu, C.W. de Silva, "Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks", IEEE/ASME Transactions on Mechatronics, Vol. 23, No. 1, pp. 101-110, 2018. DOI: https://doi.org/10.1109/TMECH.2017.2728371   DOI
13 S.H. Lee, J.S. Kim, B.B. Shin, "CNN-Based Noise System for Motorized Driving Unit Fault Causes Classification System", Proceedings of the Korean Computer Information Conference, Vol. 26, No. 1, pp. 7-8, 2018.
14 P. Malhotra, L. Vig, G. Shroff, P. Agarwal "Long short term memory networks for anomaly detection in time series", In Proceedings, Presses universitaires de Louvain, p.89, 2015.
15 N.Y. Choi, W.H. Kim, "Detecting user behavior anomalies using Generative Adversarial Networks", Intelligence Information Research, 25(3), 43-62, 2019.
16 F.A. Gers, J. Schmidhuber, "Recurrent nets that time and count", In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Neural Computing: New Challenges and Perspectives for the New Millennium, Vol. 3, pp. 189-194, 2000. DOI: https://doi.org/10.1109/IJCNN.2000.861302   DOI