Abstract
Diagnostic Trouble Codes (DTCs) allow monitoring a wide range of fault conditions in heavy trucks. Ideally, a perfectly healthy vehicle should run without any active DTCs; in practice, vehicles often run with some active DTCs even though this does not pose a threat to their normal operation. When a DTC becomes active, it is therefore unclear whether it should be ignored or considered as a serious issue. Recent approaches in machine learning, such as training Variational Autoencoders (VAEs) for anomaly detection, do not help in this respect, for a number of reasons that we discuss based on actual experiments. In particular, a VAE tends to learn that a frequently active DTC is of no importance, when in fact it should not be dismissed completely; instead, such DTC should be assigned a relative weight that is smaller but still non-negligible when compared to other, more serious DTCs. In this work, we present an approach to measure the relative weights of active DTCs in a way that allows the analyst to prioritize them and focus on the most important ones. The approach is based on the concept of binary cross-entropy, and finds application not only in the analysis of DTCs from a single vehicle, but also in monitoring active DTCs across an entire fleet.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Walter, E., Walter, R.: Diagnostic trouble codes (DTCs). In: Data Acquisition from Light-Duty Vehicles Using OBD and CAN, pp. 97–108. SAE International (2018)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: International Conference on Learning Representations (ICLR) (2014)
Suh, S., Chae, D.H., Kang, H., Choi, S.: Echo-state conditional variational autoencoder for anomaly detection. In: International Joint Conference on Neural Networks (IJCNN), pp. 1015–1022, July 2016
Guo, Y., Liao, W., Wang, Q., Yu, L., Ji, T., Li, P.: Multidimensional time series anomaly detection: a GRU-based gaussian mixture variational autoencoder approach. In: 10th Asian Conference on Machine Learning, PMLR, vol. 95, pp. 97–112 (2018)
Park, D., Hoshi, Y., Kemp, C.C.: A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robot. Autom. Lett. 3(3), 1544–1551 (2018)
Kawachi, Y., Koizumi, Y., Harada, N.: Complementary set variational autoencoder for supervised anomaly detection. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2366–2370 (2018)
Pereira, J., Silveira, M.: Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. In: 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1275–1282 (2018)
An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Technical report, SNU Data Mining Center (2015)
Hoffman, M.D., Johnson, M.J.: ELBO surgery: yet another way to carve up the variational evidence lower bound. In: Advances in Approximate Bayesian Inference (NIPS Workshop) (2016)
Singh, S., Pinion, C., Subramania, H.S.: Data-driven framework for detecting anomalies in field failure data. In: IEEE Aerospace Conference, pp. 1–14 (2011)
Pirasteh, P., et al.: Interactive feature extraction for diagnostic trouble codes in predictive maintenance: a case study from automotive domain. In: Proceedings of the Workshop on Interactive Data Mining (WIDM). ACM (2019)
Theissler, A.: Multi-class novelty detection in diagnostic trouble codes from repair shops. In: 15th IEEE International Conference on Industrial Informatics (INDIN), pp. 1043–1049 (2017)
Rögnvaldsson, T., Nowaczyk, S., Byttner, S., Prytz, R., Svensson, M.: Self-monitoring for maintenance of vehicle fleets. Data Mining Knowl. Disc. 32(2), 344–384 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ferreira, D.R., Scholz, T., Prytz, R. (2020). Importance Weighting of Diagnostic Trouble Codes for Anomaly Detection. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-64583-0_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64582-3
Online ISBN: 978-3-030-64583-0
eBook Packages: Computer ScienceComputer Science (R0)