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" Predictive maintenance in dynamic systems : "
Edwin Lughofer, Moamar Sayed-Mouchaweh, editors.
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BL
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| Record Number
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860648
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| Title & Author
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Predictive maintenance in dynamic systems : : advanced methods, decision support tools and real-world applications /\ Edwin Lughofer, Moamar Sayed-Mouchaweh, editors.
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| Publication Statement
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Cham, Switzerland :: Springer,, [2019]
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| Page. NO
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1 online resource
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| ISBN
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3030056457
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: 3030056465
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: 9783030056452
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: 9783030056469
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3030056449
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9783030056445
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; Contents; Contributors; Prologue: Predictive Maintenance in Dynamic Systems; 1 From Predictive to Preventive Maintenance in Dynamic Systems: Motivation, Requirements, and Challenges; 2 Components and Methodologies for Predictive Maintenance; 2.1 Models as Backbone Component; 2.2 Methods and Strategies to Realize Predictive Maintenance; 3 Beyond State-of-the-Art-Contents of the Book; References; Smart Devices in Production System Maintenance; 1 Introduction; 2 State of the Art; 2.1 Definition of Terms; 2.2 Physical Devices/Hardware; 2.2.1 Smartphones and Tablets
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2.2.2 Smartglasses2.2.3 Smartwatches; 2.3 Market View; 2.4 Device Selection and Potentials; 3 Application Examples in Maintenance; 3.1 Local Data Analysis and Communication for Condition Monitoring; 3.2 Remote Expert Solutions; 3.3 Process Data Visualization for Process Monitoring; 4 Limitations and Challenges; 4.1 Hardware Limitations; 4.2 User Acceptance; 4.3 Information Compression on Smart Devices; 4.4 Legal Aspects; 5 Summary; References; On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems; 1 Introduction; 2 Preprocessing; 2.1 Taxonomy; 2.2 Data Cleansing
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3.2 Experimental Schema3.3 Results; 4 Conclusions; References; Part I Anomaly Detection and Localization; A Context-Sensitive Framework for Mining Concept Drifting Data Streams; 1 Concept Drifting Data Streams; 1.1 Concept Drift; 2 A Novel Framework for Online Learning in Adaptive Mode; 2.1 Basic Components; 2.2 Optimizing for Stream Volatility and Speed; 3 Implementation of a Context-Sensitive Staged Learning Framework; 3.1 The Use of the Discrete Fourier Transform in Classification and Concept Encoding; 3.2 Repository Management; 3.3 The Staged Learning Approach
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3.3.1 Transition Between Stages3.4 Space and Time Complexity of Spectral Learning; 4 Empirical Study; 4.1 Datasets Used for the Empirical Study; 4.1.1 Synthetic Data; 4.1.2 Synthetic Data Recurring with Noise; 4.1.3 Synthetic Data Recurring with a Progressively Increasing Pattern of Drift; 4.1.4 Synthetic Data Recurring with an Oscillating Drift Pattern; 4.1.5 Real-World Data; 4.2 Parameter Values; 4.3 Effectiveness of Staged Learning Approach; 4.4 Accuracy Evaluation; 4.4.1 ARF vs SOL Accuracy of a Concept; 4.5 Throughput Evaluation; 4.6 Accuracy Versus Throughput Trade-Off
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| Abstract
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This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
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| Subject
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Plant maintenance.
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Plant maintenance.
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| Dewey Classification
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658.2/02
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| LC Classification
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TS192.P74 2019
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| Added Entry
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Lughofer, Edwin
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Sayed-Mouchaweh, Moamar
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