Title 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 / Joao Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed-Mouchawe, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott, (Eds.)

Published Cham, Switzerland : Springer, [2020]

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Description 1 online resource (317 pages) Series Communications in Computer and Information Science ; 1325 Communications in computer and information science ; 1325. Contents Intro -- IoT Streams 2020 Preface -- IoT Streams 2020 Organization -- ITEM 2020 Preface -- ITEM 2020 Organization -- Contents -- IoT Streams 2020: Stream Learning -- Self Hyper-parameter Tuning for Stream Classification Algorithms -- 1 Introduction -- 2 Related Work -- 3 Self Parameter Tuning Method -- 3.1 Nelder-Mead Optimization Algorithm -- 3.2 Dynamic Sample Size -- 3.3 Stream-Based Implementation -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Challenges of Stream Learning for Predictive Maintenance in the Railway Sector -- 1 Introduction 2 An Overview of Predictive Maintenance -- 2.1 Knowledge-Based Approach -- 2.2 Data-Driven Approach -- 3 An Overview of Stream Learning -- 3.1 Algorithms -- 3.2 Concept Drifts -- 4 Application in the Railway Sector -- 4.1 The Need of Maintenance for the Railway -- 4.2 Predictive Maintenance for the Railway -- 4.3 Benefits of Stream Learning in Railway Maintenance -- 5 Conclusion -- References -- CycleFootprint: A Fully Automated Method for Extracting Operation Cycles from Historical Raw Data of Multiple Sensors -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 4 Proposed Solution 5 Algorithm CycleFootprint -- 5.1 Transformation of Signal to State Sequences -- 5.2 Mining Footprints -- 6 Experimental Evaluation -- 6.1 Dataset -- 6.2 Configuration -- 6.3 Results -- 7 Conclusion -- References -- Valve Health Identification Using Sensors and Machine Learning Methods -- 1 Introduction -- 2 Related Work -- 3 Data -- 3.1 Dataset Description -- 3.2 Time and Frequency Domain Features -- 3.3 Signal Transformations -- 4 Classifying Valve States -- 5 Detecting Anomalous Valve Behaviour -- 5.1 Effectiveness of Distance Metrics -- 5.2 Anomaly Detection Using Distance Metrics 6 Conclusion and Future Directions -- References -- Failure Detection of an Air Production Unit in Operational Context -- 1 Introduction -- 2 Problem Definition -- 3 Related Work -- 3.1 Predictive Maintenance -- 3.2 Anomaly Detection -- 4 Our Proposal -- 5 Experimental Evaluation -- 6 Conclusions -- References -- IoT Streams 2020: Feature Learning -- Enhancing Siamese Neural Networks Through Expert Knowledge for Predictive Maintenance -- 1 Introduction -- 2 Foundations and Related Work -- 2.1 Distance-Based Time Series Classification -- 2.2 Feature-Based Time Series Representation 2.3 Siamese Neural Networks for Time Series Similarity -- 2.4 Related Work -- 3 Infusing Expert Knowledge About Attribute Relevance -- 3.1 Infusing Expert Knowledge at the Input Level -- 3.2 Infusing Expert Knowledge with 2D CNNs -- 4 Evaluation -- 4.1 Fischertechnik Model Factory Data Set -- 4.2 Approaches for Measuring Time Series Similarity -- 4.3 Experimental Setup and Training Procedure -- 4.4 Predictive Maintenance Related Quality Measures -- 4.5 Results -- 5 Conclusion and Future Work -- A Dataset -- References Summary 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 Bibliography Includes bibliographical references and index Notes Explainable Process Monitoring Based on Class Activation Map: Garbage In, Garbage Out Online resource; title from digital title page (viewed on March 02, 2021) Subject Artificial intelligence -- Congresses Artificial intelligence -- Industrial applications -- Congresses Internet of things -- Congresses Internet of things Artificial intelligence -- Industrial applications Application software Artificial intelligence Computer architecture Computer networks Education -- Data processing Software engineering Genre/Form proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. Form Electronic book Author Gama, João, editor. Pashami, Sepideh, editor Bifet, Albert, editor. Sayed-Mouchawe, Moamar, editor Fröning, Holger, editor Pernkopf, Franz, editor Schiele, Gregor, editor Blott, Michaela, editor ITEM (Workshop) (1st : 2020 : Online) jointly held conference ECML PKDD (Conference) (2020 : Online) ISBN 9783030667702 3030667707

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