Contents
Chapter 1 Introduction to Multi-source Heterogeneous Data 1
1.1 Overview of MSHD 1
1.2 Significance of MSHD 2
1.3 Key Challenges in MSHD Processing 3
1.4 Advanced Techniques and Methodologies for Processing MSHD 4
1.5 Applications and Use Cases of MSHD 6
1.6 Organization of the Book 8
References 9
Part I Fundamental Methodologies for
Processing Multi-source Heterogeneous Data
Chapter 2 Preprocessing of Multi-source Heterogeneous Data 13
2.1 Introduction to MSHD Preprocessing 13
2.2 Data Types and Quality Assessments 14
2.2.1 Types and Sources of Heterogeneous Data 15
2.2.2 Metrics and Methods for Data Quality Assessments 16
2.3 Data Cleaning 19
2.3.1 Introduction to Data Cleaning 19
2.3.2 Challenges and Solutions in Cleaning Heterogeneous Data 21
2.3.3 Methods for Data Cleaning 22
2.4 Data Transformation 24
2.4.1 Concepts of Data Transformation 24
2.4.2 Methods for Data Normalization 25
2.4.3 Challenges and Solutions in Data Transformation of Heterogeneous Data 27
2.5 Handling Missing Data 28
2.5.1 Types and Causes of Missing Data 28
2.5.2 Methods of Handling Missing Data 29
2.6 Handling Anomalies 32
2.6.1 Types of Anomaly Data 32
2.6.2 Methods for Anomaly Detection 33
2.6.3 Strategies for Handling Anomalies in Heterogeneous Data 34
2.7 Summary 36
References 36
Chapter 3 Representation Learning for Multi-source Heterogeneous Data 39
3.1 Introduction to MSHD Representation Learning 39
3.2 Multi-view Learning for MSHD 41
3.2.1 Concepts of Multi-view Learning 41
3.2.2 Methods for Multi-view Representation Learning 42
3.3 Cross-modal Learning for MSHD 47
3.3.1 Concepts of Cross-modal Learning 47
3.3.2 Methods for Cross-modal Learning 48
3.4 Graph-based Representation Learning for MSHD 51
3.4.1 Graph Models for Heterogeneous Data 52
3.4.2 Techniques of GRL for Heterogeneous Data 54
3.5 Deep Learning for MSHD 56
3.5.1 Overview of Deep Learning for Heterogeneous Data 57
3.5.2 Transfer Learning and Domain Adaptation 59
3.6 Summary 62
References 62
Chapter 4 Integration and Fusion for Multi-source Heterogeneous Data 66
4.1 Introduction to MSHD Integration and Fusion 66
4.1.1 Introduction to MSHD Integration 66
4.1.2 Introduction to MSHD Fusion 67
4.2 Data Integration Methods and Techniques 69
4.2.1 Schema Matching 69
4.2.2 Data Mapping and Transformation 70
4.2.3 Semantic Integration and Ontology-based Approaches 72
4.3 Data Fusion Strategies and Algorithms 74
4.3.1 Approaches for Combining and Fusing Information from Different Data
Sources 74
4.3.2 Ensemble Learning 76
4.3.3 Statistical and Probabilistic Models 79
4.4 Architectures and Systems for MSHD Integration and Fusion 81
4.4.1 Distributed and Parallel Computing Techniques for Scalable Data Integration
and Fusion 82
4.4.2 Integration Systems and Tools for Handling Large-scale Heterogeneous
Data 83
4.5 Summary 84
References 84
Chapter 5 Modeling of Systems with Multi-source Heterogeneous Data 88
5.1 Introduction to MSHD Modeling 88
5.2 Multi-scene Association Analysis 89
5.2.1 Importance of MSAA 90
5.2.2 Methods for MSAA 91
5.2.3 Application of MSAA 97
5.3 Distributed Self-organizing Modeling 98
5.3.1 Importance of DSOM 99
5.3.2 Methods for DSOM 99
5.3.3 Application of DSOM 103
5.4 Lightweight Collaborative Modeling 105
5.4.1 Importance of LCM 105
5.4.2 Methods for LCM 106
5.4.3 Application of LCM with MSHD 112
5.5 Summary 113
References 113
Part II Methodological Advances in Processing
Multi-source Heterogeneous Data via Case Studies
Chapter 6 Case Study 1: Fusion and Prognosis of Systems with Heterogeneous Degradation
Profiles 119
6.1 Introduction 120
6.2 Model Details 123
6.2.1 A Generic Indirect Deep Learning Approach for Multisensor Fusion and
Degradation Modeling 123
6.2.2 An Integrated Deep Learning-based Data Fusion and Degradation Modeling
Method for Improving Prognostics 134
6.3 Experimental Results of Model 1 145
6.3.1 Model Training Using Historical Units 146
6.3.2 RUL Prediction for In-service Units 147
6.4 Experiment Results of Model 2 149
6.4.1 Model Training Using Historical Units 150
6.4.2 RUL Prediction for In-service Units 153
6.5 Summary 158
References 158
Chapter 7 Case Study 2: Prognosis and Diagnosis of Systems Under Multiple Failure
Modes 162
7.1 Introduction 163
7.2 Model Details 167
7.2.1 Joint Learning of FM Recognition and Prognostics for Degradation
Processes 167
7.2.2 Multimodal Regression and Mode Recognition via An Integrated Deep Neural
Network 175
7.3 Experiment Results of Model 1 183
7.3.1 Degradation Feature Extraction 184
7.3.2 Hyperparameter Selection 186
7.3.3 Results and Comparison 188
7.4 Experiment Results of Model 2 193
7.4.1 Hyperparameter Selection 193
7.4.2 Results and Comparison 195
7.5 Summary 199
References 199
Chapter 8 Case Study 3: Prognosis and Diagnosis of Systems Under Multiple Operating
Conditions 203
8.1 Introduction 204
8.2 Model Details 207
8.2.1 An Adaptation-aware Interactive Learning Approach for Multiple OC-based
Degradation Modeling 207
8.2.2 Deep Learning-based Sensor Selection for Failure Mode Recognition and
Prognostics Under Time-varying Operating Conditions 217
8.3 Experiment Results of Model 1 225
8.3.1 Influence of Hyperparameters 226
8.3.2 Model Training 229
8.3.3 Results and Comparison 231
8.4 Experiment Results of Model 2 234
8.4.1 Hyperparameter Selection 235
8.4.2 Results and Comparison 237
8.4.3 Ablation Study 247
8.5 Summary 249
References 249
Chapter 9 Future of Advanced Techniques for Multi-source Heterogeneous Data
Processing 254
9.1 Summary of Key Findings and Contributions 254
9.2 Potential Impacts 256
9.3 Future Directions for MSHD 257
9.4 Conclusion and Remarks 259
