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Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise

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Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12975))

Abstract

In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system performance or collect usage statistics of individual machines. In this work, we focus on estimating the power output of a Combined Heat and Power (CHP) machine of a medium-sized company facility by analyzing the total facility power consumption. We formulate the problem as a time-series classification problem, where the class label represents the CHP power output. As the facility is fully instrumented and sensor measurements from the CHP are available, we generate the training labels in an automated fashion from the CHP sensor readings. However, sensor failures result in mislabeled training data samples which are hard to detect and remove from the dataset. Therefore, we propose a novel multi-task deep learning approach that jointly trains a classifier and an autoencoder with a shared embedding representation. The proposed approach targets to gradually correct the mislabelled data samples during training in a self-supervised fashion, without any prior assumption on the amount of label noise. We benchmark our approach on several time-series classification datasets and find it to be comparable and sometimes better than state-of-the-art methods. On the real-world use-case of predicting the CHP power output, we thoroughly evaluate the architectural design choices and show that the final architecture considerably increases the robustness of the learning process and consistently beats other recent state-of-the-art algorithms in the presence of unstructured as well as structured label noise.

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Notes

  1. 1.

    Number of experiments: 10 datasets \(\times \) 4 noise levels \(\times \) 5 algorithms = 200. Each experiment consists of 10 independent runs.

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Author information

Authors and Affiliations

  1. Bielefeld University, Bielefeld, Germany

    Andrea Castellani & Barbara Hammer

  2. Honda Research Institute Europe, Offenbach, Germany

    Sebastian Schmitt

Authors
  1. Andrea Castellani
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  2. Sebastian Schmitt
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  3. Barbara Hammer
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Corresponding author

Correspondence to Andrea Castellani .

Editor information

Editors and Affiliations

  1. ELLIS - The European Laboratory for Learning and Intelligent Systems, Alicante, Spain

    Nuria Oliver

  2. ETHZ and EPFL, Zürich, Switzerland

    Fernando Pérez-Cruz

  3. Johannes Gutenberg University of Mainz, Mainz, Germany

    Stefan Kramer

  4. École Polytechnique, Palaiseau, France

    Jesse Read

  5. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Castellani, A., Schmitt, S., Hammer, B. (2021). Estimating the Electrical Power Output of Industrial Devices with End-to-End Time-Series Classification in the Presence of Label Noise. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12975. Springer, Cham. https://doi.org/10.1007/978-3-030-86486-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-86486-6_29

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