Data Intensive Industrial Asset Management: IoT-based Algorithms and Implementation

Preface\nContents\nList of Figures\nList of Tables\nAuthor Biography\nChapter 1: Internet of Things (IoT): Principles and Framework\n	1.1 Internet of Things (IoT)\n	1.2 Smart Systems vs. Smart Connected Systems\n		1.2.1 Smart Connected System Main Layers\n	1.3 Dataflow in the Wisdom Pyramid\n		1.3.1 Smart Connected Systems Components and Technologies\n	1.4 Selection of Smart Devices\n	1.5 Centralized and Decentralized IoT Structures\n	1.6 Business Models´ Perspective\n	1.7 Concluding Remarks\n	References\nChapter 2: Industrial Asset Management and Maintenance Policies\n	2.1 Asset Management (AM)\n	2.2 Maintenance Strategies\n	2.3 Remaining Useful Lifetime (RUL)\n	2.4 IoT-Based Asset Management\n	2.5 General Key Performance Indicators\n	2.6 Concluding Remarks\n	References\nChapter 3: Asset Aging Through Degradation Mechanism\n	3.1 Asset Degradation\n	3.2 Challenges of Classical Models\n	3.3 Prognosis and Health Management (PHM)\n	3.4 Degradation Process\n	3.5 Remaining Useful Life (RUL)\n	3.6 Concluding Remarks\n	References\nChapter 4: Predictive Degradation Models\n	4.1 Degradation Models\n	4.2 Applications of the Degradation Models\n	4.3 General Path Degradation Models\n	4.4 Non-destructive Degradation Models\n	4.5 Non-destructive Degradation Predictive Models\n	4.6 Destructive Degradation Models\n	4.7 Destructive Degradation Predictive Models\n	4.8 Concluding Remarks\n	References\nChapter 5: IoT Platform: Smart Devices, Gateways, and Communication Networks\n	5.1 Smart Devices\n	5.2 Device Security\n	5.3 Smart Gateway\n	5.4 Communication Types\n	5.5 Network Service\n	5.6 Data Network Security\n	5.7 Concluding Remarks\n	References\nChapter 6: Data Features\n	6.1 Data Analysis\n		6.1.1 Qualitative and Quantitative Analysis\n		6.1.2 Parametric and Nonparametric Analysis\n	6.2 Data Requirement\n	6.3 Data Type\n	6.4 Data Process\n		6.4.1 Data Collection\n		6.4.2 Data Preparation\n			6.4.2.1 Data Fusion\n			6.4.2.2 Data Cleansing\n			6.4.2.3 Data Wrangling\n			6.4.2.4 Data Scraping\n			6.4.2.5 Data Filtration\n			6.4.2.6 Data Mapping\n	6.5 Data Visualization\n	6.6 Data Acquisition\n	6.7 Data Lake\n	6.8 Dark Data\n	6.9 Big Data\n	6.10 Time-Series Data\n	6.11 Data Mining\n	6.12 Concluding Remarks\n	References\nChapter 7: Data Analytics\n	7.1 Data Analytics\n	7.2 Analytics Value\n	7.3 Descriptive Analytics\n	7.4 Diagnostic Analytics\n	7.5 Predictive Analytics\n	7.6 Prescriptive Analytics\n	7.7 Concluding Remarks\n	References\nChapter 8: Machine Learning Principles\n	8.1 Introduction\n	8.2 Machine Learning Algorithms\n		8.2.1 Linear Regression\n			8.2.1.1 Linear Regression Assumptions\n		8.2.2 Logistic Regression\n			8.2.2.1 Logistic Regression Assumption\n		8.2.3 Decision Tree\n		8.2.4 Support Vector Machine\n		8.2.5 Naïve Bayes\n		8.2.6 Artificial Neural Network\n		8.2.7 Deep Neural Network\n		8.2.8 Convolution Neural Network\n	8.3 Machine Learning Algorithms Evaluation\n	8.4 Ensembles\n	8.5 Regression Example\n	8.6 Classification Example\n	8.7 Machine Learning as a Service (MLaaS)\n	8.8 Concluding Remarks\n	References\nChapter 9: Implementation Tools of IoT Systems\n	9.1 Introduction\n		9.1.1 IoT Platforms\n		9.1.2 Requirements of IoT Applications\n		9.1.3 Cloud-Based IoT Platform\n	9.2 Azure IoT Platform\n		9.2.1 IoT Devices and Gateways\n		9.2.2 Storage\n		9.2.3 Data Analysis\n		9.2.4 User Interface\n	9.3 Experiences with Azure\n		9.3.1 Data Set\n		9.3.2 Azure Setup\n		9.3.3 Resource Groups\n		9.3.4 IoT Hub and Storage\n		9.3.5 Stream Analytics\n		9.3.6 Azure Functions\n		9.3.7 Azure Machine Learning\n		9.3.8 Power BI\n		9.3.9 Experiment Observations\n	9.4 Conclusion\n	References\nAppendix A: Regression Example\n	Linear Regression Matlab Code\n	Linear Interaction Regression Model\n	Linear Stepwise Regression Model\n	Decision Tress\n	Bagged Decision Tree\n	Linear SVM\nAppendix B: Classification Example\n	Fine Tree\n	Bagged Tree\n	Fine KNN\n	Weighted KNN\n	Logistic Regression\n	Linear SVM\n	Quadratic SVM\n	Cubic SVM\nIndex