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Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14175))

Abstract

Failure prediction is key to ensuring the reliable operation of vehicles, especially for organizations that depend on a fleet of vehicles. However, traditional approaches often rely on rule-based or heuristic methods that may not be effective in detecting subtle anomalies, rare events, or in more modern vehicles containing a complex sensory network. This paper presents a novel approach to vehicle failure prediction, called mVSG-VFP, which employs self-supervised learning and graph-based techniques. The proposed method realizes the failure prediction task by exploring information hidden in the time-series data recorded through the sensors embedded in the vehicle. mVSG-VFP includes two main components: a graph-based autoencoder that learns representations of normal data while considering the relationship between different sensors and a self-supervised component that maps temporally-adjacent data to similar representations. We propose a novel approach to define the notion of adjacency in vehicle temporal data.

To evaluate mVSG-VFP, we apply it to a dataset comprised of vehicle sensor recordings to identify the abnormal data samples that signal a potential future failure. We performed a flurry of experiments to verify the accuracy of our model and demonstrate it outperforms state-of-the-art models in this task. Overall, the method is robust and intuitive, making it a useful tool for real-world applications.

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Notes

  1. 1.

    We define a trip as a continuous recording of sensors in which the engine is not turned off for more than 5 min.

  2. 2.

    Here R(.) can be any feature extraction function, including predefined feature extraction functions as well as trainable neural networks.

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Acknowledgement

We would like to express our sincere gratitude to Ken Sills, CTO and Co-Founder of Preteckt Inc. company and his team for their invaluable support in providing us with the data used in this research paper. Their contribution was crucial in enabling us to analyze and draw meaningful conclusions from the dataset. We would also acknowledge Fonds de Recherche du Quebec Nature et technologies (FRQNT), Natural Sciences and Engineering Research Council of Canada (NSERC), and Scale AI for funding this research project.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, McGill University, Montreal, Canada

    Hadi Hojjati, Mohammadreza Sadeghi & Narges Armanfard

  2. MILA - Quebec AI Institute, Montreal, Canada

    Hadi Hojjati, Mohammadreza Sadeghi & Narges Armanfard

Authors
  1. Hadi Hojjati
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  2. Mohammadreza Sadeghi
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  3. Narges Armanfard
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Corresponding author

Correspondence to Hadi Hojjati .

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Editors and Affiliations

  1. CENTAI, Turin, Italy

    Gianmarco De Francisci Morales

  2. NYU and Two Sigma, New York, NY, USA

    Claudia Perlich

  3. Netflix, Los Angeles, CA, USA

    Natali Ruchansky

  4. Telefonica Research, Barcelona, Spain

    Nicolas Kourtellis

  5. Politecnico di Torino, Turin, Italy

    Elena Baralis

  6. CENTAI, Turin, Italy

    Francesco Bonchi

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We acknowledge that our research involves collecting and processing potentially sensitive data. We have taken measures to protect the privacy and confidentiality of the organizations represented in the data. We have obtained all necessary permissions and approvals. We recognize that our work has implications for the automotive industry, and we acknowledge our responsibility to consider these implications carefully. We have taken care to report our findings accurately and transparently, and we have made efforts to minimize any potential negative impacts of our research.

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Hojjati, H., Sadeghi, M., Armanfard, N. (2023). Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_15

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  • DOI: https://doi.org/10.1007/978-3-031-43430-3_15

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