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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Joao Gama

    1. University of Porto, Porto, Portugal

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  2. Sepideh Pashami

    1. Halmstad University, Halmstad, Sweden

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  3. Albert Bifet

    1. Waikato University, Hamilton, New Zealand

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  4. Moamar Sayed-Mouchawe

    1. University of Lille, Lille, France

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  5. Holger Fröning

    1. Heidelberg University, Heidelberg, Germany

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  6. Franz Pernkopf

    1. Graz University of Technology, Graz, Austria

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  7. Gregor Schiele

    1. University of Duisburg-Essen, Essen, Germany

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  8. Michaela Blott

    1. XILINX Research, Dublin, Ireland

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1325)

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Table of contents (22 papers)

  1. Front Matter

    Pages I-XVII
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  2. IoT Streams 2020: Stream Learning

    1. Front Matter

      Pages 1-1
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    2. Self Hyper-parameter Tuning for Stream Classification Algorithms

      • Bruno Veloso, João Gama
      Pages 3-13

    3. Challenges of Stream Learning for Predictive Maintenance in the Railway Sector

      • Minh Huong Le Nguyen, Fabien Turgis, Pierre-Emmanuel Fayemi, Albert Bifet
      Pages 14-29

    4. CycleFootprint: A Fully Automated Method for Extracting Operation Cycles from Historical Raw Data of Multiple Sensors

      • Hadi Fanaee-T, Mohamed-Rafik Bouguelia, Mahmoud Rahat, Jonathan Blixt, Harpal Singh
      Pages 30-44

    5. Valve Health Identification Using Sensors and Machine Learning Methods

      • M. Atif Qureshi, Luis Miralles-Pechuán, Jason Payne, Ronan O’Malley, Brian Mac Namee
      Pages 45-60

    6. Failure Detection of an Air Production Unit in Operational Context

      • Mariana Barros, Bruno Veloso, Pedro M. Pereira, Rita P. Ribeiro, João Gama
      Pages 61-74

  3. IoT Streams 2020: Feature Learning

    1. Front Matter

      Pages 75-75
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    2. Enhancing Siamese Neural Networks Through Expert Knowledge for Predictive Maintenance

      • Patrick Klein, Niklas Weingarz, Ralph Bergmann
      Pages 77-92

    3. Explainable Process Monitoring Based on Class Activation Map: Garbage In, Garbage Out

      • Cheolhwan Oh, Junhyung Moon, Jongpil Jeong
      Pages 93-105

    4. AutoML for Predictive Maintenance: One Tool to RUL Them All

      • Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier
      Pages 106-118

    5. Forklift Truck Activity Recognition from CAN Data

      • Kunru Chen, Sepideh Pashami, Sławomir Nowaczyk, Emilia Johansson, Gustav Sternelöv, Thorsteinn Rögnvaldsson
      Pages 119-126

    6. Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles

      • Vandan Revanur, Ayodeji Ayibiowu, Mahmoud Rahat, Reza Khoshkangini
      Pages 127-141

  4. IoT Streams 2020: Unsupervised Machine Learning

    1. Front Matter

      Pages 143-143
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    2. Unsupervised Machine Learning Methods to Estimate a Health Indicator for Condition Monitoring Using Acoustic and Vibration Signals: A Comparison Based on a Toy Data Set from a Coffee Vending Machine

      • Yonas Tefera, Maarten Meire, Stijn Luca, Peter Karsmakers
      Pages 145-159

    3. Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach

      • Pieter Bonte, Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Filip De Turck, Sofie Van Hoecke et al.
      Pages 160-172

    4. Interactive Anomaly Detection Based on Clustering and Online Mirror Descent

      • Lingyun Cheng, Sadhana Sundaresh, Mohamed-Rafik Bouguelia, Onur Dikmen
      Pages 173-186

  5. ITEM 2020: Hardware

    1. Front Matter

      Pages 187-187
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    2. hxtorch: PyTorch for BrainScaleS-2

      • Philipp Spilger, Eric Müller, Arne Emmel, Aron Leibfried, Christian Mauch, Christian Pehle et al.
      Pages 189-200

    3. Inference with Artificial Neural Networks on Analog Neuromorphic Hardware

      • Johannes Weis, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann, Arne Emmel, Eric Müller et al.
      Pages 201-212
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  1. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Keywords

About this book

This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. 

The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.

Editors and Affiliations

  • University of Porto, Porto, Portugal

    Joao Gama

  • Halmstad University, Halmstad, Sweden

    Sepideh Pashami

  • Waikato University, Hamilton, New Zealand

    Albert Bifet

  • University of Lille, Lille, France

    Moamar Sayed-Mouchawe

  • Heidelberg University, Heidelberg, Germany

    Holger Fröning

  • Graz University of Technology, Graz, Austria

    Franz Pernkopf

  • University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  • XILINX Research, Dublin, Ireland

    Michaela Blott

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