Big Data Analytics in Smart Manufacturing: Principles and Practices
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Milton
CRC Press LLC
2022
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| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (205 Seiten) |
| ISBN: | 9781000815825 |
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| 505 | 8 | |a Intro -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- 1. Machine Learning Techniques and Big Data Analytics for Smart Manufacturing -- 1.1 An Overview of Smart Manufacturing -- 1.1.1 Upsides and Downsides of Smart Manufacturing -- 1.2 Machine Learning in Smart Manufacturing -- 1.2.1 Supervised Machine Learning in Smart Manufacturing -- 1.2.2 Unsupervised Machine Learning in Smart Manufacturing -- 1.3 Big Data Analysis in Smart Manufacturing -- 1.3.1 Infrastructure -- 1.3.2 Architecture -- 1.4 Comparative Study of Smart Manufacturing -- 1.5 Applications Used in Smart Manufacturing -- 1.5.1 Distinct Examination for Item Quality Assessment -- 1.5.2 Symptomatic Investigation for Shortcoming Appraisal -- 1.5.3 Prescient Examination for Deformity Anticipation -- 1.6 Challenges of Machine Learning in Smart Manufacturing -- 1.7 Advantage of Machine Learning in Smart Manufacturing -- 1.7.1 Deep Learning Model for Smart Manufacturing -- 1.7.2 Smart Manufacturing of Industrial IoT Robotics -- 1.7.3 Smart Factory Production -- 1.7.4 Data Clustering-Based ML -- 1.7.5 Imbalanced Data and Comparative Analysis in Smart Manufacturing -- 1.7.6 Human to Machine Applications for Smart Industry -- 1.8 Future of Smart Manufacturing -- 1.8.1 Smart 3D Printing Techniques Using AI and Cloud -- 1.8.2 Blockchain Secured Industry 4.0 -- 1.8.3 Smart Transportation System -- 1.8.3.1 Safety and Security in Autonomous Vehicles -- 1.8.4 Augmented Reality in AI-Based Education System -- 1.9 Conclusion -- References -- 2. Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics -- 2.1 Introduction -- 2.2 Historical Background -- 2.3 Smart Manufacturing -- 2.4 The DT -- 2.5 Big Data -- 2.6 Data-Driven Paradigm -- 2.7 Conclusion -- References | |
| 505 | 8 | |a 3. Data-Driven Models in Machine Learning: An Enabler of Smart Manufacturing -- 3.1 Introduction -- 3.2 3D Printing Process -- 3.2.1 3D Printing - Advantages -- 3.2.2 3D Printing - Disadvantages -- 3.2.3 3D Printing - Beneficiary Industries -- 3.2.4 3D Printing Techniques -- 3.2.4.1 Powder Bed Fusion -- 3.2.4.2 Selective Laser Sintering and Melting -- 3.2.4.3 Electron Beam Melting -- 3.2.4.4 Photo-Polymerization -- 3.2.4.5 Stereolithography -- 3.2.4.6 Digital Light Processing -- 3.2.4.7 Inkjet: Binder Jetting -- 3.2.4.8 Inkjet: Material Jetting -- 3.2.4.9 Material Extrusion -- 3.2.4.10 Selective Deposition Lamination (SDL) -- 3.3 Need for Parametric Analysis and Optimization in 3D Printing -- 3.4 ML Technique - Overview -- 3.4.1 Reasons for Adoption of ML in 21st Century -- 3.4.2 Popular Techniques of ML Applied in AM -- 3.4.2.1 Linear Regression -- 3.4.2.2 Artificial Neural Networks -- 3.4.3 Applications of ANN in 3D Printing -- 3.5 ML in Additive Manufacturing Industry - State of Art -- 3.6 Case Studies for the Experimental Data -- 3.6.1 Case Study I -- 3.6.2 Case Study II -- 3.7 Comparison of ML Analysis to Statistical Analysis Tools -- 3.8 Challenges Associated for Ml Applications to 3D Printing -- 3.8.1 Big Data Challenges -- 3.8.2 Scope of Issue Addressal/Advanced Techniques -- 3.8.2.1 Data Augmentation -- 3.8.2.2 Transfer Learning -- 3.8.3 Few-Shot Learning -- 3.9 Conclusions -- References -- 4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing -- 4.1 Portfolio of Predictive Maintenance and Condition Monitoring -- 4.1.1 Characteristics of Industry 4.0 -- 4.1.2 Industry 4.0: Revolution or Evolution? -- 4.2 Condition Monitoring and Predictive Maintenance -- 4.2.1 Taxonomy of Maintenance Activities in Industries -- 4.2.1.1 Preventive Maintenance -- 4.2.1.2 Predictive Maintenance | |
| 505 | 8 | |a 4.3 Role of Predictive Maintenance in Smart Manufacturing -- 4.4 Niche of Big Data in Smart Manufacturing -- 4.4.1 Significance of RUL in Mechanical Machineries -- 4.5 Local Time Invariant Learning Through BGRU -- 4.5.1 Gated Recurrent Unit -- 4.5.2 Bidirectional GRU -- 4.6 RUL Prediction Through BGRU from Mechanical Big Data -- 4.7 Exploration of the Experimental Results -- 4.8 Conclusion -- References -- 5. Integration of Industrial IoT and Big Data Analytics for Smart Manufacturing Industries: Perspectives and Challenges -- 5.1 Introduction -- 5.1.1 Industry Automation System -- 5.1.2 Industrial Automation Types -- 5.1.2.1 Fixed Automation System -- 5.1.2.2 Programmable Automation System -- 5.1.2.3 Soft Automation System -- 5.1.2.4 Integrated Automation System -- 5.2 Industry 4.0 Revolution -- 5.2.1 International Standards of Industry 4.0 -- 5.3 IoT Components and Its Protocols -- 5.3.1 Things -- 5.3.2 Gateways -- 5.3.3 Cloud Gateway -- 5.3.4 Data Lake -- 5.3.5 Data Analytics -- 5.3.6 Machine Learning -- 5.3.7 Control Applications -- 5.3.8 User Applications -- 5.4 M2M Communication in Smart Manufacturing -- 5.5 IoT in Smart Manufacturing -- 5.5.1 Advanced Analysis -- 5.5.2 Inventory Monitoring -- 5.5.3 Remote Process Monitoring -- 5.5.4 Abnormality Reporting -- 5.6 Big Data Analytics in Smart Manufacturing -- 5.6.1 Self-Service Systems -- 5.6.1.1 Elimination of bottlenecks -- 5.6.1.2 Predictive Maintenance -- 5.6.1.3 Automation Production Management -- 5.6.1.4 Predictive Demand -- 5.7 Convergence of IIoT and Big Data Analytics -- 5.8 Smart Manufacturing in Industries -- 5.8.1 Building Blocks of Smart Manufacturing -- 5.8.1.1 Flat -- 5.8.1.2 Data-Driven -- 5.8.1.3 Sustainable -- 5.8.1.4 Agile -- 5.8.1.5 Innovative -- 5.8.1.6 Current -- 5.8.1.7 Profitable -- 5.8.2 IIoT Implementation -- 5.9 Smart Manufacturing in MSMEs | |
| 505 | 8 | |a 5.9.1 Smart Manufacturing in Large-Scale Industry -- 5.9.2 Intelligent Robots for Smart Manufacturing -- 5.9.2.1 Industrial Robots -- 5.9.2.2 Collaborative Robots -- 5.10 Challenges in Integrating Industrial IoT and Big Data Analytics -- 5.10.1 Privacy -- 5.10.2 Cyber Security -- 5.10.3 Scalability -- 5.10.4 Connectivity and Communication -- 5.10.5 Efficiency -- 5.11 Research Scope in IIoT -- 5.11.1 Energy Management -- 5.11.2 Heterogeneous QoS -- 5.11.3 Resource Management -- 5.11.4 Data Offloading Decision -- 5.12 Conclusion -- References -- 6. Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Dataset Acquisition -- 6.3.1 Dataset -- 6.3.2 Data Pre-Processing -- 6.4 System Design -- 6.4.1 System Pipeline -- 6.4.2 Convolutional Neural Network -- 6.4.2.1 Input Layer -- 6.4.2.2 Convolutional Layer -- 6.4.2.3 Dense Layer -- 6.4.2.4 Output Layer -- 6.4.3 Audio Feature Extraction -- 6.4.4 Support Vector Machine -- 6.4.5 Multi-Layer Perceptron -- 6.4.6 Ensemble Learning -- 6.5 System Implementation -- 6.5.1 Emotion Prediction from Videos: CNN Model Training -- 6.5.2 Emotion Prediction from Audio: SVM-MLP Training -- 6.5.3 Combining the Video and Audio Using Ensemble Learning -- 6.6 Result and Analysis -- 6.6.1 Testing the CNN Model -- 6.6.2 Testing the SVM and MLP Model -- 6.6.3 Testing the Ensemble Model -- 6.7 Conclusion -- References -- 7. Deep PHM: IoT-Based Deep Learning Approach on Prediction of Prognostics and Health Management of an Aircraft Engine -- 7.1 Introduction -- 7.2 Overview of Prognostics and Health Management -- 7.3 Steps Involved in PHM -- 7.3.1 Data Acquisition -- 7.3.2 Data Pre-processing -- 7.3.3 Detection -- 7.3.4 Diagnostics -- 7.3.5 Prognostics -- 7.3.6 Decision-Making -- 7.3.7 Human-Machine Interface -- 7.4 PHM in Aerospace Industry | |
| 505 | 8 | |a 7.4.1 Sensors Used in the Gas Turbofan Engine -- 7.4.1.1 Temperature Sensors -- 7.4.1.2 Total Air Gas Temperature Sensor -- 7.4.1.3 Exhaust Gas Temperature Sensor -- 7.4.1.4 Vibration Sensors -- 7.4.1.5 Speed Sensors -- 7.4.1.6 Fuel Sensors for Flow -- 7.4.1.7 Altimeter Sensors -- 7.5 Dataset Description -- 7.5.1 Long Short-Term Memory -- 7.5.2 Experimental Analysis on C-MAPPS -- 7.5.2.1 Performance Metric Selection -- 7.5.2.2 Result Analysis -- 7.6 Conclusion -- References -- 8. A Comprehensive Study on Accelerating Smart Manufacturers Using Ubiquitous Robotic Technology -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Smart Manufacturing Systems -- 8.3.1 Why Do We Need Ubiquitous Robotics? -- 8.4 Ubiquitous Robotics -- 8.4.1 System Design -- 8.4.2 Part-Based Hardware Measure -- 8.4.3 Concept for Ubiquitous Industrial Robot Work Cell -- 8.5 Ubiquitous Computing -- 8.5.1 Advantages of Ubiquitous Computing -- 8.6 Conclusion -- 8.7 Future Scope -- References -- 9. Machine Learning Techniques and Big Data Tools in Design and Manufacturing -- 9.1 Introduction -- 9.2 Literature Survey -- 9.2.1 Analytics in Climate Big Data -- 9.2.2 Problem and Challenges -- 9.3 Contribution -- 9.4 Proposed Method -- 9.5 Big Data Analysis -- 9.5.1 Benefits of Big Data Analytics -- 9.5.2 Data Understanding -- 9.5.3 Data Preparation -- 9.6 Feature Selection -- 9.6.1 FA-Based Feature Selection -- 9.7 Exploratory Analysis -- 9.8 Classification -- 9.8.1 NB -- 9.8.2 Multiple Regression-Logistic Regression -- 9.8.3 XGBoost Classifier -- 9.9 Evaluation -- 9.9.1 Methods and Modeling -- 9.10 Result and Discussion -- 9.10.1 Performance Analysis -- 9.11 Conclusion -- References -- 10. Principle Comprehension of IoT and Smart Manufacturing System -- 10.1 Introduction to IoT -- 10.1.1 History and Evolution of IoT -- 10.2 IoT Platforms and Operating System -- 10.2.1 IoT Platforms | |
| 505 | 8 | |a 10.2.1.1 Hardware | |
| 650 | 4 | |a Big data | |
| 700 | 1 | |a Poongodi, T. |e Sonstige |4 oth | |
| 700 | 1 | |a Balamurugan, B. |e Sonstige |4 oth | |
| 700 | 1 | |a Sharma, Meenakshi |e Sonstige |4 oth | |
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| author | Suresh, P. |
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| contents | Intro -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- 1. Machine Learning Techniques and Big Data Analytics for Smart Manufacturing -- 1.1 An Overview of Smart Manufacturing -- 1.1.1 Upsides and Downsides of Smart Manufacturing -- 1.2 Machine Learning in Smart Manufacturing -- 1.2.1 Supervised Machine Learning in Smart Manufacturing -- 1.2.2 Unsupervised Machine Learning in Smart Manufacturing -- 1.3 Big Data Analysis in Smart Manufacturing -- 1.3.1 Infrastructure -- 1.3.2 Architecture -- 1.4 Comparative Study of Smart Manufacturing -- 1.5 Applications Used in Smart Manufacturing -- 1.5.1 Distinct Examination for Item Quality Assessment -- 1.5.2 Symptomatic Investigation for Shortcoming Appraisal -- 1.5.3 Prescient Examination for Deformity Anticipation -- 1.6 Challenges of Machine Learning in Smart Manufacturing -- 1.7 Advantage of Machine Learning in Smart Manufacturing -- 1.7.1 Deep Learning Model for Smart Manufacturing -- 1.7.2 Smart Manufacturing of Industrial IoT Robotics -- 1.7.3 Smart Factory Production -- 1.7.4 Data Clustering-Based ML -- 1.7.5 Imbalanced Data and Comparative Analysis in Smart Manufacturing -- 1.7.6 Human to Machine Applications for Smart Industry -- 1.8 Future of Smart Manufacturing -- 1.8.1 Smart 3D Printing Techniques Using AI and Cloud -- 1.8.2 Blockchain Secured Industry 4.0 -- 1.8.3 Smart Transportation System -- 1.8.3.1 Safety and Security in Autonomous Vehicles -- 1.8.4 Augmented Reality in AI-Based Education System -- 1.9 Conclusion -- References -- 2. Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics -- 2.1 Introduction -- 2.2 Historical Background -- 2.3 Smart Manufacturing -- 2.4 The DT -- 2.5 Big Data -- 2.6 Data-Driven Paradigm -- 2.7 Conclusion -- References 3. Data-Driven Models in Machine Learning: An Enabler of Smart Manufacturing -- 3.1 Introduction -- 3.2 3D Printing Process -- 3.2.1 3D Printing - Advantages -- 3.2.2 3D Printing - Disadvantages -- 3.2.3 3D Printing - Beneficiary Industries -- 3.2.4 3D Printing Techniques -- 3.2.4.1 Powder Bed Fusion -- 3.2.4.2 Selective Laser Sintering and Melting -- 3.2.4.3 Electron Beam Melting -- 3.2.4.4 Photo-Polymerization -- 3.2.4.5 Stereolithography -- 3.2.4.6 Digital Light Processing -- 3.2.4.7 Inkjet: Binder Jetting -- 3.2.4.8 Inkjet: Material Jetting -- 3.2.4.9 Material Extrusion -- 3.2.4.10 Selective Deposition Lamination (SDL) -- 3.3 Need for Parametric Analysis and Optimization in 3D Printing -- 3.4 ML Technique - Overview -- 3.4.1 Reasons for Adoption of ML in 21st Century -- 3.4.2 Popular Techniques of ML Applied in AM -- 3.4.2.1 Linear Regression -- 3.4.2.2 Artificial Neural Networks -- 3.4.3 Applications of ANN in 3D Printing -- 3.5 ML in Additive Manufacturing Industry - State of Art -- 3.6 Case Studies for the Experimental Data -- 3.6.1 Case Study I -- 3.6.2 Case Study II -- 3.7 Comparison of ML Analysis to Statistical Analysis Tools -- 3.8 Challenges Associated for Ml Applications to 3D Printing -- 3.8.1 Big Data Challenges -- 3.8.2 Scope of Issue Addressal/Advanced Techniques -- 3.8.2.1 Data Augmentation -- 3.8.2.2 Transfer Learning -- 3.8.3 Few-Shot Learning -- 3.9 Conclusions -- References -- 4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing -- 4.1 Portfolio of Predictive Maintenance and Condition Monitoring -- 4.1.1 Characteristics of Industry 4.0 -- 4.1.2 Industry 4.0: Revolution or Evolution? -- 4.2 Condition Monitoring and Predictive Maintenance -- 4.2.1 Taxonomy of Maintenance Activities in Industries -- 4.2.1.1 Preventive Maintenance -- 4.2.1.2 Predictive Maintenance 4.3 Role of Predictive Maintenance in Smart Manufacturing -- 4.4 Niche of Big Data in Smart Manufacturing -- 4.4.1 Significance of RUL in Mechanical Machineries -- 4.5 Local Time Invariant Learning Through BGRU -- 4.5.1 Gated Recurrent Unit -- 4.5.2 Bidirectional GRU -- 4.6 RUL Prediction Through BGRU from Mechanical Big Data -- 4.7 Exploration of the Experimental Results -- 4.8 Conclusion -- References -- 5. Integration of Industrial IoT and Big Data Analytics for Smart Manufacturing Industries: Perspectives and Challenges -- 5.1 Introduction -- 5.1.1 Industry Automation System -- 5.1.2 Industrial Automation Types -- 5.1.2.1 Fixed Automation System -- 5.1.2.2 Programmable Automation System -- 5.1.2.3 Soft Automation System -- 5.1.2.4 Integrated Automation System -- 5.2 Industry 4.0 Revolution -- 5.2.1 International Standards of Industry 4.0 -- 5.3 IoT Components and Its Protocols -- 5.3.1 Things -- 5.3.2 Gateways -- 5.3.3 Cloud Gateway -- 5.3.4 Data Lake -- 5.3.5 Data Analytics -- 5.3.6 Machine Learning -- 5.3.7 Control Applications -- 5.3.8 User Applications -- 5.4 M2M Communication in Smart Manufacturing -- 5.5 IoT in Smart Manufacturing -- 5.5.1 Advanced Analysis -- 5.5.2 Inventory Monitoring -- 5.5.3 Remote Process Monitoring -- 5.5.4 Abnormality Reporting -- 5.6 Big Data Analytics in Smart Manufacturing -- 5.6.1 Self-Service Systems -- 5.6.1.1 Elimination of bottlenecks -- 5.6.1.2 Predictive Maintenance -- 5.6.1.3 Automation Production Management -- 5.6.1.4 Predictive Demand -- 5.7 Convergence of IIoT and Big Data Analytics -- 5.8 Smart Manufacturing in Industries -- 5.8.1 Building Blocks of Smart Manufacturing -- 5.8.1.1 Flat -- 5.8.1.2 Data-Driven -- 5.8.1.3 Sustainable -- 5.8.1.4 Agile -- 5.8.1.5 Innovative -- 5.8.1.6 Current -- 5.8.1.7 Profitable -- 5.8.2 IIoT Implementation -- 5.9 Smart Manufacturing in MSMEs 5.9.1 Smart Manufacturing in Large-Scale Industry -- 5.9.2 Intelligent Robots for Smart Manufacturing -- 5.9.2.1 Industrial Robots -- 5.9.2.2 Collaborative Robots -- 5.10 Challenges in Integrating Industrial IoT and Big Data Analytics -- 5.10.1 Privacy -- 5.10.2 Cyber Security -- 5.10.3 Scalability -- 5.10.4 Connectivity and Communication -- 5.10.5 Efficiency -- 5.11 Research Scope in IIoT -- 5.11.1 Energy Management -- 5.11.2 Heterogeneous QoS -- 5.11.3 Resource Management -- 5.11.4 Data Offloading Decision -- 5.12 Conclusion -- References -- 6. Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Dataset Acquisition -- 6.3.1 Dataset -- 6.3.2 Data Pre-Processing -- 6.4 System Design -- 6.4.1 System Pipeline -- 6.4.2 Convolutional Neural Network -- 6.4.2.1 Input Layer -- 6.4.2.2 Convolutional Layer -- 6.4.2.3 Dense Layer -- 6.4.2.4 Output Layer -- 6.4.3 Audio Feature Extraction -- 6.4.4 Support Vector Machine -- 6.4.5 Multi-Layer Perceptron -- 6.4.6 Ensemble Learning -- 6.5 System Implementation -- 6.5.1 Emotion Prediction from Videos: CNN Model Training -- 6.5.2 Emotion Prediction from Audio: SVM-MLP Training -- 6.5.3 Combining the Video and Audio Using Ensemble Learning -- 6.6 Result and Analysis -- 6.6.1 Testing the CNN Model -- 6.6.2 Testing the SVM and MLP Model -- 6.6.3 Testing the Ensemble Model -- 6.7 Conclusion -- References -- 7. Deep PHM: IoT-Based Deep Learning Approach on Prediction of Prognostics and Health Management of an Aircraft Engine -- 7.1 Introduction -- 7.2 Overview of Prognostics and Health Management -- 7.3 Steps Involved in PHM -- 7.3.1 Data Acquisition -- 7.3.2 Data Pre-processing -- 7.3.3 Detection -- 7.3.4 Diagnostics -- 7.3.5 Prognostics -- 7.3.6 Decision-Making -- 7.3.7 Human-Machine Interface -- 7.4 PHM in Aerospace Industry 7.4.1 Sensors Used in the Gas Turbofan Engine -- 7.4.1.1 Temperature Sensors -- 7.4.1.2 Total Air Gas Temperature Sensor -- 7.4.1.3 Exhaust Gas Temperature Sensor -- 7.4.1.4 Vibration Sensors -- 7.4.1.5 Speed Sensors -- 7.4.1.6 Fuel Sensors for Flow -- 7.4.1.7 Altimeter Sensors -- 7.5 Dataset Description -- 7.5.1 Long Short-Term Memory -- 7.5.2 Experimental Analysis on C-MAPPS -- 7.5.2.1 Performance Metric Selection -- 7.5.2.2 Result Analysis -- 7.6 Conclusion -- References -- 8. A Comprehensive Study on Accelerating Smart Manufacturers Using Ubiquitous Robotic Technology -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Smart Manufacturing Systems -- 8.3.1 Why Do We Need Ubiquitous Robotics? -- 8.4 Ubiquitous Robotics -- 8.4.1 System Design -- 8.4.2 Part-Based Hardware Measure -- 8.4.3 Concept for Ubiquitous Industrial Robot Work Cell -- 8.5 Ubiquitous Computing -- 8.5.1 Advantages of Ubiquitous Computing -- 8.6 Conclusion -- 8.7 Future Scope -- References -- 9. Machine Learning Techniques and Big Data Tools in Design and Manufacturing -- 9.1 Introduction -- 9.2 Literature Survey -- 9.2.1 Analytics in Climate Big Data -- 9.2.2 Problem and Challenges -- 9.3 Contribution -- 9.4 Proposed Method -- 9.5 Big Data Analysis -- 9.5.1 Benefits of Big Data Analytics -- 9.5.2 Data Understanding -- 9.5.3 Data Preparation -- 9.6 Feature Selection -- 9.6.1 FA-Based Feature Selection -- 9.7 Exploratory Analysis -- 9.8 Classification -- 9.8.1 NB -- 9.8.2 Multiple Regression-Logistic Regression -- 9.8.3 XGBoost Classifier -- 9.9 Evaluation -- 9.9.1 Methods and Modeling -- 9.10 Result and Discussion -- 9.10.1 Performance Analysis -- 9.11 Conclusion -- References -- 10. Principle Comprehension of IoT and Smart Manufacturing System -- 10.1 Introduction to IoT -- 10.1.1 History and Evolution of IoT -- 10.2 IoT Platforms and Operating System -- 10.2.1 IoT Platforms 10.2.1.1 Hardware |
| ctrlnum | (ZDB-30-PQE)EBC7130153 (ZDB-30-PAD)EBC7130153 (ZDB-89-EBL)EBL7130153 (OCoLC)1350420588 (DE-599)BVBBV049874426 |
| dewey-full | 670.285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 670 - Manufacturing |
| dewey-raw | 670.285 |
| dewey-search | 670.285 |
| dewey-sort | 3670.285 |
| dewey-tens | 670 - Manufacturing |
| discipline | Werkstoffwissenschaften / Fertigungstechnik |
| edition | 1st ed |
| format | Electronic eBook |
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Machine Learning Techniques and Big Data Analytics for Smart Manufacturing -- 1.1 An Overview of Smart Manufacturing -- 1.1.1 Upsides and Downsides of Smart Manufacturing -- 1.2 Machine Learning in Smart Manufacturing -- 1.2.1 Supervised Machine Learning in Smart Manufacturing -- 1.2.2 Unsupervised Machine Learning in Smart Manufacturing -- 1.3 Big Data Analysis in Smart Manufacturing -- 1.3.1 Infrastructure -- 1.3.2 Architecture -- 1.4 Comparative Study of Smart Manufacturing -- 1.5 Applications Used in Smart Manufacturing -- 1.5.1 Distinct Examination for Item Quality Assessment -- 1.5.2 Symptomatic Investigation for Shortcoming Appraisal -- 1.5.3 Prescient Examination for Deformity Anticipation -- 1.6 Challenges of Machine Learning in Smart Manufacturing -- 1.7 Advantage of Machine Learning in Smart Manufacturing -- 1.7.1 Deep Learning Model for Smart Manufacturing -- 1.7.2 Smart Manufacturing of Industrial IoT Robotics -- 1.7.3 Smart Factory Production -- 1.7.4 Data Clustering-Based ML -- 1.7.5 Imbalanced Data and Comparative Analysis in Smart Manufacturing -- 1.7.6 Human to Machine Applications for Smart Industry -- 1.8 Future of Smart Manufacturing -- 1.8.1 Smart 3D Printing Techniques Using AI and Cloud -- 1.8.2 Blockchain Secured Industry 4.0 -- 1.8.3 Smart Transportation System -- 1.8.3.1 Safety and Security in Autonomous Vehicles -- 1.8.4 Augmented Reality in AI-Based Education System -- 1.9 Conclusion -- References -- 2. Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics -- 2.1 Introduction -- 2.2 Historical Background -- 2.3 Smart Manufacturing -- 2.4 The DT -- 2.5 Big Data -- 2.6 Data-Driven Paradigm -- 2.7 Conclusion -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3. Data-Driven Models in Machine Learning: An Enabler of Smart Manufacturing -- 3.1 Introduction -- 3.2 3D Printing Process -- 3.2.1 3D Printing - Advantages -- 3.2.2 3D Printing - Disadvantages -- 3.2.3 3D Printing - Beneficiary Industries -- 3.2.4 3D Printing Techniques -- 3.2.4.1 Powder Bed Fusion -- 3.2.4.2 Selective Laser Sintering and Melting -- 3.2.4.3 Electron Beam Melting -- 3.2.4.4 Photo-Polymerization -- 3.2.4.5 Stereolithography -- 3.2.4.6 Digital Light Processing -- 3.2.4.7 Inkjet: Binder Jetting -- 3.2.4.8 Inkjet: Material Jetting -- 3.2.4.9 Material Extrusion -- 3.2.4.10 Selective Deposition Lamination (SDL) -- 3.3 Need for Parametric Analysis and Optimization in 3D Printing -- 3.4 ML Technique - Overview -- 3.4.1 Reasons for Adoption of ML in 21st Century -- 3.4.2 Popular Techniques of ML Applied in AM -- 3.4.2.1 Linear Regression -- 3.4.2.2 Artificial Neural Networks -- 3.4.3 Applications of ANN in 3D Printing -- 3.5 ML in Additive Manufacturing Industry - State of Art -- 3.6 Case Studies for the Experimental Data -- 3.6.1 Case Study I -- 3.6.2 Case Study II -- 3.7 Comparison of ML Analysis to Statistical Analysis Tools -- 3.8 Challenges Associated for Ml Applications to 3D Printing -- 3.8.1 Big Data Challenges -- 3.8.2 Scope of Issue Addressal/Advanced Techniques -- 3.8.2.1 Data Augmentation -- 3.8.2.2 Transfer Learning -- 3.8.3 Few-Shot Learning -- 3.9 Conclusions -- References -- 4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing -- 4.1 Portfolio of Predictive Maintenance and Condition Monitoring -- 4.1.1 Characteristics of Industry 4.0 -- 4.1.2 Industry 4.0: Revolution or Evolution? -- 4.2 Condition Monitoring and Predictive Maintenance -- 4.2.1 Taxonomy of Maintenance Activities in Industries -- 4.2.1.1 Preventive Maintenance -- 4.2.1.2 Predictive Maintenance</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.3 Role of Predictive Maintenance in Smart Manufacturing -- 4.4 Niche of Big Data in Smart Manufacturing -- 4.4.1 Significance of RUL in Mechanical Machineries -- 4.5 Local Time Invariant Learning Through BGRU -- 4.5.1 Gated Recurrent Unit -- 4.5.2 Bidirectional GRU -- 4.6 RUL Prediction Through BGRU from Mechanical Big Data -- 4.7 Exploration of the Experimental Results -- 4.8 Conclusion -- References -- 5. Integration of Industrial IoT and Big Data Analytics for Smart Manufacturing Industries: Perspectives and Challenges -- 5.1 Introduction -- 5.1.1 Industry Automation System -- 5.1.2 Industrial Automation Types -- 5.1.2.1 Fixed Automation System -- 5.1.2.2 Programmable Automation System -- 5.1.2.3 Soft Automation System -- 5.1.2.4 Integrated Automation System -- 5.2 Industry 4.0 Revolution -- 5.2.1 International Standards of Industry 4.0 -- 5.3 IoT Components and Its Protocols -- 5.3.1 Things -- 5.3.2 Gateways -- 5.3.3 Cloud Gateway -- 5.3.4 Data Lake -- 5.3.5 Data Analytics -- 5.3.6 Machine Learning -- 5.3.7 Control Applications -- 5.3.8 User Applications -- 5.4 M2M Communication in Smart Manufacturing -- 5.5 IoT in Smart Manufacturing -- 5.5.1 Advanced Analysis -- 5.5.2 Inventory Monitoring -- 5.5.3 Remote Process Monitoring -- 5.5.4 Abnormality Reporting -- 5.6 Big Data Analytics in Smart Manufacturing -- 5.6.1 Self-Service Systems -- 5.6.1.1 Elimination of bottlenecks -- 5.6.1.2 Predictive Maintenance -- 5.6.1.3 Automation Production Management -- 5.6.1.4 Predictive Demand -- 5.7 Convergence of IIoT and Big Data Analytics -- 5.8 Smart Manufacturing in Industries -- 5.8.1 Building Blocks of Smart Manufacturing -- 5.8.1.1 Flat -- 5.8.1.2 Data-Driven -- 5.8.1.3 Sustainable -- 5.8.1.4 Agile -- 5.8.1.5 Innovative -- 5.8.1.6 Current -- 5.8.1.7 Profitable -- 5.8.2 IIoT Implementation -- 5.9 Smart Manufacturing in MSMEs</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.9.1 Smart Manufacturing in Large-Scale Industry -- 5.9.2 Intelligent Robots for Smart Manufacturing -- 5.9.2.1 Industrial Robots -- 5.9.2.2 Collaborative Robots -- 5.10 Challenges in Integrating Industrial IoT and Big Data Analytics -- 5.10.1 Privacy -- 5.10.2 Cyber Security -- 5.10.3 Scalability -- 5.10.4 Connectivity and Communication -- 5.10.5 Efficiency -- 5.11 Research Scope in IIoT -- 5.11.1 Energy Management -- 5.11.2 Heterogeneous QoS -- 5.11.3 Resource Management -- 5.11.4 Data Offloading Decision -- 5.12 Conclusion -- References -- 6. Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Dataset Acquisition -- 6.3.1 Dataset -- 6.3.2 Data Pre-Processing -- 6.4 System Design -- 6.4.1 System Pipeline -- 6.4.2 Convolutional Neural Network -- 6.4.2.1 Input Layer -- 6.4.2.2 Convolutional Layer -- 6.4.2.3 Dense Layer -- 6.4.2.4 Output Layer -- 6.4.3 Audio Feature Extraction -- 6.4.4 Support Vector Machine -- 6.4.5 Multi-Layer Perceptron -- 6.4.6 Ensemble Learning -- 6.5 System Implementation -- 6.5.1 Emotion Prediction from Videos: CNN Model Training -- 6.5.2 Emotion Prediction from Audio: SVM-MLP Training -- 6.5.3 Combining the Video and Audio Using Ensemble Learning -- 6.6 Result and Analysis -- 6.6.1 Testing the CNN Model -- 6.6.2 Testing the SVM and MLP Model -- 6.6.3 Testing the Ensemble Model -- 6.7 Conclusion -- References -- 7. Deep PHM: IoT-Based Deep Learning Approach on Prediction of Prognostics and Health Management of an Aircraft Engine -- 7.1 Introduction -- 7.2 Overview of Prognostics and Health Management -- 7.3 Steps Involved in PHM -- 7.3.1 Data Acquisition -- 7.3.2 Data Pre-processing -- 7.3.3 Detection -- 7.3.4 Diagnostics -- 7.3.5 Prognostics -- 7.3.6 Decision-Making -- 7.3.7 Human-Machine Interface -- 7.4 PHM in Aerospace Industry</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.4.1 Sensors Used in the Gas Turbofan Engine -- 7.4.1.1 Temperature Sensors -- 7.4.1.2 Total Air Gas Temperature Sensor -- 7.4.1.3 Exhaust Gas Temperature Sensor -- 7.4.1.4 Vibration Sensors -- 7.4.1.5 Speed Sensors -- 7.4.1.6 Fuel Sensors for Flow -- 7.4.1.7 Altimeter Sensors -- 7.5 Dataset Description -- 7.5.1 Long Short-Term Memory -- 7.5.2 Experimental Analysis on C-MAPPS -- 7.5.2.1 Performance Metric Selection -- 7.5.2.2 Result Analysis -- 7.6 Conclusion -- References -- 8. A Comprehensive Study on Accelerating Smart Manufacturers Using Ubiquitous Robotic Technology -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Smart Manufacturing Systems -- 8.3.1 Why Do We Need Ubiquitous Robotics? -- 8.4 Ubiquitous Robotics -- 8.4.1 System Design -- 8.4.2 Part-Based Hardware Measure -- 8.4.3 Concept for Ubiquitous Industrial Robot Work Cell -- 8.5 Ubiquitous Computing -- 8.5.1 Advantages of Ubiquitous Computing -- 8.6 Conclusion -- 8.7 Future Scope -- References -- 9. Machine Learning Techniques and Big Data Tools in Design and Manufacturing -- 9.1 Introduction -- 9.2 Literature Survey -- 9.2.1 Analytics in Climate Big Data -- 9.2.2 Problem and Challenges -- 9.3 Contribution -- 9.4 Proposed Method -- 9.5 Big Data Analysis -- 9.5.1 Benefits of Big Data Analytics -- 9.5.2 Data Understanding -- 9.5.3 Data Preparation -- 9.6 Feature Selection -- 9.6.1 FA-Based Feature Selection -- 9.7 Exploratory Analysis -- 9.8 Classification -- 9.8.1 NB -- 9.8.2 Multiple Regression-Logistic Regression -- 9.8.3 XGBoost Classifier -- 9.9 Evaluation -- 9.9.1 Methods and Modeling -- 9.10 Result and Discussion -- 9.10.1 Performance Analysis -- 9.11 Conclusion -- References -- 10. Principle Comprehension of IoT and Smart Manufacturing System -- 10.1 Introduction to IoT -- 10.1.1 History and Evolution of IoT -- 10.2 IoT Platforms and Operating System -- 10.2.1 IoT Platforms</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">10.2.1.1 Hardware</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big data</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Poongodi, T.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Balamurugan, B.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sharma, Meenakshi</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Suresh, P.</subfield><subfield code="t">Big Data Analytics in Smart Manufacturing</subfield><subfield code="d">Milton : CRC Press LLC,c2022</subfield><subfield code="z">9781032065519</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035213884</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7130153</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV049874426 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-19T05:22:06Z |
| institution | BVB |
| isbn | 9781000815825 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035213884 |
| oclc_num | 1350420588 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (205 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press LLC |
| record_format | marc |
| spelling | Suresh, P. Verfasser aut Big Data Analytics in Smart Manufacturing Principles and Practices 1st ed Milton CRC Press LLC 2022 ©2023 1 Online-Ressource (205 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- 1. Machine Learning Techniques and Big Data Analytics for Smart Manufacturing -- 1.1 An Overview of Smart Manufacturing -- 1.1.1 Upsides and Downsides of Smart Manufacturing -- 1.2 Machine Learning in Smart Manufacturing -- 1.2.1 Supervised Machine Learning in Smart Manufacturing -- 1.2.2 Unsupervised Machine Learning in Smart Manufacturing -- 1.3 Big Data Analysis in Smart Manufacturing -- 1.3.1 Infrastructure -- 1.3.2 Architecture -- 1.4 Comparative Study of Smart Manufacturing -- 1.5 Applications Used in Smart Manufacturing -- 1.5.1 Distinct Examination for Item Quality Assessment -- 1.5.2 Symptomatic Investigation for Shortcoming Appraisal -- 1.5.3 Prescient Examination for Deformity Anticipation -- 1.6 Challenges of Machine Learning in Smart Manufacturing -- 1.7 Advantage of Machine Learning in Smart Manufacturing -- 1.7.1 Deep Learning Model for Smart Manufacturing -- 1.7.2 Smart Manufacturing of Industrial IoT Robotics -- 1.7.3 Smart Factory Production -- 1.7.4 Data Clustering-Based ML -- 1.7.5 Imbalanced Data and Comparative Analysis in Smart Manufacturing -- 1.7.6 Human to Machine Applications for Smart Industry -- 1.8 Future of Smart Manufacturing -- 1.8.1 Smart 3D Printing Techniques Using AI and Cloud -- 1.8.2 Blockchain Secured Industry 4.0 -- 1.8.3 Smart Transportation System -- 1.8.3.1 Safety and Security in Autonomous Vehicles -- 1.8.4 Augmented Reality in AI-Based Education System -- 1.9 Conclusion -- References -- 2. Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics -- 2.1 Introduction -- 2.2 Historical Background -- 2.3 Smart Manufacturing -- 2.4 The DT -- 2.5 Big Data -- 2.6 Data-Driven Paradigm -- 2.7 Conclusion -- References 3. Data-Driven Models in Machine Learning: An Enabler of Smart Manufacturing -- 3.1 Introduction -- 3.2 3D Printing Process -- 3.2.1 3D Printing - Advantages -- 3.2.2 3D Printing - Disadvantages -- 3.2.3 3D Printing - Beneficiary Industries -- 3.2.4 3D Printing Techniques -- 3.2.4.1 Powder Bed Fusion -- 3.2.4.2 Selective Laser Sintering and Melting -- 3.2.4.3 Electron Beam Melting -- 3.2.4.4 Photo-Polymerization -- 3.2.4.5 Stereolithography -- 3.2.4.6 Digital Light Processing -- 3.2.4.7 Inkjet: Binder Jetting -- 3.2.4.8 Inkjet: Material Jetting -- 3.2.4.9 Material Extrusion -- 3.2.4.10 Selective Deposition Lamination (SDL) -- 3.3 Need for Parametric Analysis and Optimization in 3D Printing -- 3.4 ML Technique - Overview -- 3.4.1 Reasons for Adoption of ML in 21st Century -- 3.4.2 Popular Techniques of ML Applied in AM -- 3.4.2.1 Linear Regression -- 3.4.2.2 Artificial Neural Networks -- 3.4.3 Applications of ANN in 3D Printing -- 3.5 ML in Additive Manufacturing Industry - State of Art -- 3.6 Case Studies for the Experimental Data -- 3.6.1 Case Study I -- 3.6.2 Case Study II -- 3.7 Comparison of ML Analysis to Statistical Analysis Tools -- 3.8 Challenges Associated for Ml Applications to 3D Printing -- 3.8.1 Big Data Challenges -- 3.8.2 Scope of Issue Addressal/Advanced Techniques -- 3.8.2.1 Data Augmentation -- 3.8.2.2 Transfer Learning -- 3.8.3 Few-Shot Learning -- 3.9 Conclusions -- References -- 4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing -- 4.1 Portfolio of Predictive Maintenance and Condition Monitoring -- 4.1.1 Characteristics of Industry 4.0 -- 4.1.2 Industry 4.0: Revolution or Evolution? -- 4.2 Condition Monitoring and Predictive Maintenance -- 4.2.1 Taxonomy of Maintenance Activities in Industries -- 4.2.1.1 Preventive Maintenance -- 4.2.1.2 Predictive Maintenance 4.3 Role of Predictive Maintenance in Smart Manufacturing -- 4.4 Niche of Big Data in Smart Manufacturing -- 4.4.1 Significance of RUL in Mechanical Machineries -- 4.5 Local Time Invariant Learning Through BGRU -- 4.5.1 Gated Recurrent Unit -- 4.5.2 Bidirectional GRU -- 4.6 RUL Prediction Through BGRU from Mechanical Big Data -- 4.7 Exploration of the Experimental Results -- 4.8 Conclusion -- References -- 5. Integration of Industrial IoT and Big Data Analytics for Smart Manufacturing Industries: Perspectives and Challenges -- 5.1 Introduction -- 5.1.1 Industry Automation System -- 5.1.2 Industrial Automation Types -- 5.1.2.1 Fixed Automation System -- 5.1.2.2 Programmable Automation System -- 5.1.2.3 Soft Automation System -- 5.1.2.4 Integrated Automation System -- 5.2 Industry 4.0 Revolution -- 5.2.1 International Standards of Industry 4.0 -- 5.3 IoT Components and Its Protocols -- 5.3.1 Things -- 5.3.2 Gateways -- 5.3.3 Cloud Gateway -- 5.3.4 Data Lake -- 5.3.5 Data Analytics -- 5.3.6 Machine Learning -- 5.3.7 Control Applications -- 5.3.8 User Applications -- 5.4 M2M Communication in Smart Manufacturing -- 5.5 IoT in Smart Manufacturing -- 5.5.1 Advanced Analysis -- 5.5.2 Inventory Monitoring -- 5.5.3 Remote Process Monitoring -- 5.5.4 Abnormality Reporting -- 5.6 Big Data Analytics in Smart Manufacturing -- 5.6.1 Self-Service Systems -- 5.6.1.1 Elimination of bottlenecks -- 5.6.1.2 Predictive Maintenance -- 5.6.1.3 Automation Production Management -- 5.6.1.4 Predictive Demand -- 5.7 Convergence of IIoT and Big Data Analytics -- 5.8 Smart Manufacturing in Industries -- 5.8.1 Building Blocks of Smart Manufacturing -- 5.8.1.1 Flat -- 5.8.1.2 Data-Driven -- 5.8.1.3 Sustainable -- 5.8.1.4 Agile -- 5.8.1.5 Innovative -- 5.8.1.6 Current -- 5.8.1.7 Profitable -- 5.8.2 IIoT Implementation -- 5.9 Smart Manufacturing in MSMEs 5.9.1 Smart Manufacturing in Large-Scale Industry -- 5.9.2 Intelligent Robots for Smart Manufacturing -- 5.9.2.1 Industrial Robots -- 5.9.2.2 Collaborative Robots -- 5.10 Challenges in Integrating Industrial IoT and Big Data Analytics -- 5.10.1 Privacy -- 5.10.2 Cyber Security -- 5.10.3 Scalability -- 5.10.4 Connectivity and Communication -- 5.10.5 Efficiency -- 5.11 Research Scope in IIoT -- 5.11.1 Energy Management -- 5.11.2 Heterogeneous QoS -- 5.11.3 Resource Management -- 5.11.4 Data Offloading Decision -- 5.12 Conclusion -- References -- 6. Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Dataset Acquisition -- 6.3.1 Dataset -- 6.3.2 Data Pre-Processing -- 6.4 System Design -- 6.4.1 System Pipeline -- 6.4.2 Convolutional Neural Network -- 6.4.2.1 Input Layer -- 6.4.2.2 Convolutional Layer -- 6.4.2.3 Dense Layer -- 6.4.2.4 Output Layer -- 6.4.3 Audio Feature Extraction -- 6.4.4 Support Vector Machine -- 6.4.5 Multi-Layer Perceptron -- 6.4.6 Ensemble Learning -- 6.5 System Implementation -- 6.5.1 Emotion Prediction from Videos: CNN Model Training -- 6.5.2 Emotion Prediction from Audio: SVM-MLP Training -- 6.5.3 Combining the Video and Audio Using Ensemble Learning -- 6.6 Result and Analysis -- 6.6.1 Testing the CNN Model -- 6.6.2 Testing the SVM and MLP Model -- 6.6.3 Testing the Ensemble Model -- 6.7 Conclusion -- References -- 7. Deep PHM: IoT-Based Deep Learning Approach on Prediction of Prognostics and Health Management of an Aircraft Engine -- 7.1 Introduction -- 7.2 Overview of Prognostics and Health Management -- 7.3 Steps Involved in PHM -- 7.3.1 Data Acquisition -- 7.3.2 Data Pre-processing -- 7.3.3 Detection -- 7.3.4 Diagnostics -- 7.3.5 Prognostics -- 7.3.6 Decision-Making -- 7.3.7 Human-Machine Interface -- 7.4 PHM in Aerospace Industry 7.4.1 Sensors Used in the Gas Turbofan Engine -- 7.4.1.1 Temperature Sensors -- 7.4.1.2 Total Air Gas Temperature Sensor -- 7.4.1.3 Exhaust Gas Temperature Sensor -- 7.4.1.4 Vibration Sensors -- 7.4.1.5 Speed Sensors -- 7.4.1.6 Fuel Sensors for Flow -- 7.4.1.7 Altimeter Sensors -- 7.5 Dataset Description -- 7.5.1 Long Short-Term Memory -- 7.5.2 Experimental Analysis on C-MAPPS -- 7.5.2.1 Performance Metric Selection -- 7.5.2.2 Result Analysis -- 7.6 Conclusion -- References -- 8. A Comprehensive Study on Accelerating Smart Manufacturers Using Ubiquitous Robotic Technology -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Smart Manufacturing Systems -- 8.3.1 Why Do We Need Ubiquitous Robotics? -- 8.4 Ubiquitous Robotics -- 8.4.1 System Design -- 8.4.2 Part-Based Hardware Measure -- 8.4.3 Concept for Ubiquitous Industrial Robot Work Cell -- 8.5 Ubiquitous Computing -- 8.5.1 Advantages of Ubiquitous Computing -- 8.6 Conclusion -- 8.7 Future Scope -- References -- 9. Machine Learning Techniques and Big Data Tools in Design and Manufacturing -- 9.1 Introduction -- 9.2 Literature Survey -- 9.2.1 Analytics in Climate Big Data -- 9.2.2 Problem and Challenges -- 9.3 Contribution -- 9.4 Proposed Method -- 9.5 Big Data Analysis -- 9.5.1 Benefits of Big Data Analytics -- 9.5.2 Data Understanding -- 9.5.3 Data Preparation -- 9.6 Feature Selection -- 9.6.1 FA-Based Feature Selection -- 9.7 Exploratory Analysis -- 9.8 Classification -- 9.8.1 NB -- 9.8.2 Multiple Regression-Logistic Regression -- 9.8.3 XGBoost Classifier -- 9.9 Evaluation -- 9.9.1 Methods and Modeling -- 9.10 Result and Discussion -- 9.10.1 Performance Analysis -- 9.11 Conclusion -- References -- 10. Principle Comprehension of IoT and Smart Manufacturing System -- 10.1 Introduction to IoT -- 10.1.1 History and Evolution of IoT -- 10.2 IoT Platforms and Operating System -- 10.2.1 IoT Platforms 10.2.1.1 Hardware Big data Poongodi, T. Sonstige oth Balamurugan, B. Sonstige oth Sharma, Meenakshi Sonstige oth Erscheint auch als Druck-Ausgabe Suresh, P. Big Data Analytics in Smart Manufacturing Milton : CRC Press LLC,c2022 9781032065519 |
| spellingShingle | Suresh, P. Big Data Analytics in Smart Manufacturing Principles and Practices Intro -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- 1. Machine Learning Techniques and Big Data Analytics for Smart Manufacturing -- 1.1 An Overview of Smart Manufacturing -- 1.1.1 Upsides and Downsides of Smart Manufacturing -- 1.2 Machine Learning in Smart Manufacturing -- 1.2.1 Supervised Machine Learning in Smart Manufacturing -- 1.2.2 Unsupervised Machine Learning in Smart Manufacturing -- 1.3 Big Data Analysis in Smart Manufacturing -- 1.3.1 Infrastructure -- 1.3.2 Architecture -- 1.4 Comparative Study of Smart Manufacturing -- 1.5 Applications Used in Smart Manufacturing -- 1.5.1 Distinct Examination for Item Quality Assessment -- 1.5.2 Symptomatic Investigation for Shortcoming Appraisal -- 1.5.3 Prescient Examination for Deformity Anticipation -- 1.6 Challenges of Machine Learning in Smart Manufacturing -- 1.7 Advantage of Machine Learning in Smart Manufacturing -- 1.7.1 Deep Learning Model for Smart Manufacturing -- 1.7.2 Smart Manufacturing of Industrial IoT Robotics -- 1.7.3 Smart Factory Production -- 1.7.4 Data Clustering-Based ML -- 1.7.5 Imbalanced Data and Comparative Analysis in Smart Manufacturing -- 1.7.6 Human to Machine Applications for Smart Industry -- 1.8 Future of Smart Manufacturing -- 1.8.1 Smart 3D Printing Techniques Using AI and Cloud -- 1.8.2 Blockchain Secured Industry 4.0 -- 1.8.3 Smart Transportation System -- 1.8.3.1 Safety and Security in Autonomous Vehicles -- 1.8.4 Augmented Reality in AI-Based Education System -- 1.9 Conclusion -- References -- 2. Data-Driven Paradigm for Smart Manufacturing in the Context of Big Data Analytics -- 2.1 Introduction -- 2.2 Historical Background -- 2.3 Smart Manufacturing -- 2.4 The DT -- 2.5 Big Data -- 2.6 Data-Driven Paradigm -- 2.7 Conclusion -- References 3. Data-Driven Models in Machine Learning: An Enabler of Smart Manufacturing -- 3.1 Introduction -- 3.2 3D Printing Process -- 3.2.1 3D Printing - Advantages -- 3.2.2 3D Printing - Disadvantages -- 3.2.3 3D Printing - Beneficiary Industries -- 3.2.4 3D Printing Techniques -- 3.2.4.1 Powder Bed Fusion -- 3.2.4.2 Selective Laser Sintering and Melting -- 3.2.4.3 Electron Beam Melting -- 3.2.4.4 Photo-Polymerization -- 3.2.4.5 Stereolithography -- 3.2.4.6 Digital Light Processing -- 3.2.4.7 Inkjet: Binder Jetting -- 3.2.4.8 Inkjet: Material Jetting -- 3.2.4.9 Material Extrusion -- 3.2.4.10 Selective Deposition Lamination (SDL) -- 3.3 Need for Parametric Analysis and Optimization in 3D Printing -- 3.4 ML Technique - Overview -- 3.4.1 Reasons for Adoption of ML in 21st Century -- 3.4.2 Popular Techniques of ML Applied in AM -- 3.4.2.1 Linear Regression -- 3.4.2.2 Artificial Neural Networks -- 3.4.3 Applications of ANN in 3D Printing -- 3.5 ML in Additive Manufacturing Industry - State of Art -- 3.6 Case Studies for the Experimental Data -- 3.6.1 Case Study I -- 3.6.2 Case Study II -- 3.7 Comparison of ML Analysis to Statistical Analysis Tools -- 3.8 Challenges Associated for Ml Applications to 3D Printing -- 3.8.1 Big Data Challenges -- 3.8.2 Scope of Issue Addressal/Advanced Techniques -- 3.8.2.1 Data Augmentation -- 3.8.2.2 Transfer Learning -- 3.8.3 Few-Shot Learning -- 3.9 Conclusions -- References -- 4. Local Time Invariant Learning from Industrial Big Data for Predictive Maintenance in Smart Manufacturing -- 4.1 Portfolio of Predictive Maintenance and Condition Monitoring -- 4.1.1 Characteristics of Industry 4.0 -- 4.1.2 Industry 4.0: Revolution or Evolution? -- 4.2 Condition Monitoring and Predictive Maintenance -- 4.2.1 Taxonomy of Maintenance Activities in Industries -- 4.2.1.1 Preventive Maintenance -- 4.2.1.2 Predictive Maintenance 4.3 Role of Predictive Maintenance in Smart Manufacturing -- 4.4 Niche of Big Data in Smart Manufacturing -- 4.4.1 Significance of RUL in Mechanical Machineries -- 4.5 Local Time Invariant Learning Through BGRU -- 4.5.1 Gated Recurrent Unit -- 4.5.2 Bidirectional GRU -- 4.6 RUL Prediction Through BGRU from Mechanical Big Data -- 4.7 Exploration of the Experimental Results -- 4.8 Conclusion -- References -- 5. Integration of Industrial IoT and Big Data Analytics for Smart Manufacturing Industries: Perspectives and Challenges -- 5.1 Introduction -- 5.1.1 Industry Automation System -- 5.1.2 Industrial Automation Types -- 5.1.2.1 Fixed Automation System -- 5.1.2.2 Programmable Automation System -- 5.1.2.3 Soft Automation System -- 5.1.2.4 Integrated Automation System -- 5.2 Industry 4.0 Revolution -- 5.2.1 International Standards of Industry 4.0 -- 5.3 IoT Components and Its Protocols -- 5.3.1 Things -- 5.3.2 Gateways -- 5.3.3 Cloud Gateway -- 5.3.4 Data Lake -- 5.3.5 Data Analytics -- 5.3.6 Machine Learning -- 5.3.7 Control Applications -- 5.3.8 User Applications -- 5.4 M2M Communication in Smart Manufacturing -- 5.5 IoT in Smart Manufacturing -- 5.5.1 Advanced Analysis -- 5.5.2 Inventory Monitoring -- 5.5.3 Remote Process Monitoring -- 5.5.4 Abnormality Reporting -- 5.6 Big Data Analytics in Smart Manufacturing -- 5.6.1 Self-Service Systems -- 5.6.1.1 Elimination of bottlenecks -- 5.6.1.2 Predictive Maintenance -- 5.6.1.3 Automation Production Management -- 5.6.1.4 Predictive Demand -- 5.7 Convergence of IIoT and Big Data Analytics -- 5.8 Smart Manufacturing in Industries -- 5.8.1 Building Blocks of Smart Manufacturing -- 5.8.1.1 Flat -- 5.8.1.2 Data-Driven -- 5.8.1.3 Sustainable -- 5.8.1.4 Agile -- 5.8.1.5 Innovative -- 5.8.1.6 Current -- 5.8.1.7 Profitable -- 5.8.2 IIoT Implementation -- 5.9 Smart Manufacturing in MSMEs 5.9.1 Smart Manufacturing in Large-Scale Industry -- 5.9.2 Intelligent Robots for Smart Manufacturing -- 5.9.2.1 Industrial Robots -- 5.9.2.2 Collaborative Robots -- 5.10 Challenges in Integrating Industrial IoT and Big Data Analytics -- 5.10.1 Privacy -- 5.10.2 Cyber Security -- 5.10.3 Scalability -- 5.10.4 Connectivity and Communication -- 5.10.5 Efficiency -- 5.11 Research Scope in IIoT -- 5.11.1 Energy Management -- 5.11.2 Heterogeneous QoS -- 5.11.3 Resource Management -- 5.11.4 Data Offloading Decision -- 5.12 Conclusion -- References -- 6. Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Dataset Acquisition -- 6.3.1 Dataset -- 6.3.2 Data Pre-Processing -- 6.4 System Design -- 6.4.1 System Pipeline -- 6.4.2 Convolutional Neural Network -- 6.4.2.1 Input Layer -- 6.4.2.2 Convolutional Layer -- 6.4.2.3 Dense Layer -- 6.4.2.4 Output Layer -- 6.4.3 Audio Feature Extraction -- 6.4.4 Support Vector Machine -- 6.4.5 Multi-Layer Perceptron -- 6.4.6 Ensemble Learning -- 6.5 System Implementation -- 6.5.1 Emotion Prediction from Videos: CNN Model Training -- 6.5.2 Emotion Prediction from Audio: SVM-MLP Training -- 6.5.3 Combining the Video and Audio Using Ensemble Learning -- 6.6 Result and Analysis -- 6.6.1 Testing the CNN Model -- 6.6.2 Testing the SVM and MLP Model -- 6.6.3 Testing the Ensemble Model -- 6.7 Conclusion -- References -- 7. Deep PHM: IoT-Based Deep Learning Approach on Prediction of Prognostics and Health Management of an Aircraft Engine -- 7.1 Introduction -- 7.2 Overview of Prognostics and Health Management -- 7.3 Steps Involved in PHM -- 7.3.1 Data Acquisition -- 7.3.2 Data Pre-processing -- 7.3.3 Detection -- 7.3.4 Diagnostics -- 7.3.5 Prognostics -- 7.3.6 Decision-Making -- 7.3.7 Human-Machine Interface -- 7.4 PHM in Aerospace Industry 7.4.1 Sensors Used in the Gas Turbofan Engine -- 7.4.1.1 Temperature Sensors -- 7.4.1.2 Total Air Gas Temperature Sensor -- 7.4.1.3 Exhaust Gas Temperature Sensor -- 7.4.1.4 Vibration Sensors -- 7.4.1.5 Speed Sensors -- 7.4.1.6 Fuel Sensors for Flow -- 7.4.1.7 Altimeter Sensors -- 7.5 Dataset Description -- 7.5.1 Long Short-Term Memory -- 7.5.2 Experimental Analysis on C-MAPPS -- 7.5.2.1 Performance Metric Selection -- 7.5.2.2 Result Analysis -- 7.6 Conclusion -- References -- 8. A Comprehensive Study on Accelerating Smart Manufacturers Using Ubiquitous Robotic Technology -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Smart Manufacturing Systems -- 8.3.1 Why Do We Need Ubiquitous Robotics? -- 8.4 Ubiquitous Robotics -- 8.4.1 System Design -- 8.4.2 Part-Based Hardware Measure -- 8.4.3 Concept for Ubiquitous Industrial Robot Work Cell -- 8.5 Ubiquitous Computing -- 8.5.1 Advantages of Ubiquitous Computing -- 8.6 Conclusion -- 8.7 Future Scope -- References -- 9. Machine Learning Techniques and Big Data Tools in Design and Manufacturing -- 9.1 Introduction -- 9.2 Literature Survey -- 9.2.1 Analytics in Climate Big Data -- 9.2.2 Problem and Challenges -- 9.3 Contribution -- 9.4 Proposed Method -- 9.5 Big Data Analysis -- 9.5.1 Benefits of Big Data Analytics -- 9.5.2 Data Understanding -- 9.5.3 Data Preparation -- 9.6 Feature Selection -- 9.6.1 FA-Based Feature Selection -- 9.7 Exploratory Analysis -- 9.8 Classification -- 9.8.1 NB -- 9.8.2 Multiple Regression-Logistic Regression -- 9.8.3 XGBoost Classifier -- 9.9 Evaluation -- 9.9.1 Methods and Modeling -- 9.10 Result and Discussion -- 9.10.1 Performance Analysis -- 9.11 Conclusion -- References -- 10. Principle Comprehension of IoT and Smart Manufacturing System -- 10.1 Introduction to IoT -- 10.1.1 History and Evolution of IoT -- 10.2 IoT Platforms and Operating System -- 10.2.1 IoT Platforms 10.2.1.1 Hardware Big data |
| title | Big Data Analytics in Smart Manufacturing Principles and Practices |
| title_auth | Big Data Analytics in Smart Manufacturing Principles and Practices |
| title_exact_search | Big Data Analytics in Smart Manufacturing Principles and Practices |
| title_full | Big Data Analytics in Smart Manufacturing Principles and Practices |
| title_fullStr | Big Data Analytics in Smart Manufacturing Principles and Practices |
| title_full_unstemmed | Big Data Analytics in Smart Manufacturing Principles and Practices |
| title_short | Big Data Analytics in Smart Manufacturing |
| title_sort | big data analytics in smart manufacturing principles and practices |
| title_sub | Principles and Practices |
| topic | Big data |
| topic_facet | Big data |
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