Industrial applications of machine learning:
"This book shows how machine learning can be applied to address real-world problems in the fourth industrial revolution and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolutio...
Gespeichert in:
| Beteiligte Personen: | , , , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton
Taylor & Francis, CRC Press
[2019]
|
| Schriftenreihe: | Chapman & Hall / CRC data mining and knowledge series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030690544&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030690544&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Zusammenfassung: | "This book shows how machine learning can be applied to address real-world problems in the fourth industrial revolution and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society"-- |
| Umfang: | XIII, 333 Seiten Illustrationen, Diagramme |
| ISBN: | 9780815356226 |
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Datensatz im Suchindex
| _version_ | 1819305976165040128 |
|---|---|
| adam_text | Contents Preface 1 The Fourth Industrial Revolution 1.1 Introduction ................................................................................. 1.1.1 Industrie 4.0 .................................................................. 1.1.2 Industrial Internet of Things ....................................... 1.1.3 Other International Strategies.......................................... 1.2 Industry Smartization............................................................... 1.2.1 At the Component Level................................................ 1.2.2 At the Machine Level ................................................... 1.2.3 At the Production Level................................................... 1.2.4 At the Distribution Level............................................. 1.3 Machine Learning Challenges and Opportunities within Smart Industries.................................................................................... 1.3.1 Impact on Business......................................................... 1.3.2 Impact on Technology................................................... 1.3.3 Impact on People............................................................ 1.4 Concluding Remarks ............................................................... xi 1 1 5 6 7 9 9 10 11 12 12 13 15 15 16 2 Machine Learning 19 Introduction .............................................................................. 19 Basic Statistics........................................................................... 23 2.2.1 Descriptive
Statistics...................................................... 23 2.2.1.1 Visualization and Summary of Univariate Data 24 2.2.1.2 Visualization and Summary of Bivariate Data 26 2.2.1.3 Visualization and Summary of Multivariate Data ............................................................... 26 2.2.1.4 Imputation of MissingData............................ 29 2.2.1.5 Variable Transformation ................................. 31 2.2.2 Inference ........................................................................ 32 2.2.2.1 Parameter Point Estimation........................ 32 2.2.2.2 Parameter ConfidenceEstimation................ 36 2.2.2.3 Hypothesis Testing ....................................... 36 2.3 Clustering ................................................................................. 40 2.3.1 Hierarchical Clustering................................................... 40 2.3.2 AT-Means Algorithm...................................................... 42 2.1 2.2 v
vi Contents 2.4 2.5 2.6 2.7 2.8 2.3.3 Spectral Clustering......................................................... 43 2.3.4 Affinity Propagation...................................................... 45 2.3.5 Probabilistic Clustering................................................ 46 Supervised Classification ......................................................... 49 2.4.1 Model Performance Evaluation....................................... 51 2.4.1.1 Performance Evaluation Measures.................. 51 2.4.1.2 Honest Performance Estimation Methods . . 56 2.4.2 Feature Subset Selection................................................ 59 2.4.3 ќ-Nearest Neighbors...................................................... 65 2.4.4 Classification Trees............................................................ 67 2.4.5 Rule Induction............................................................... 69 2.4.6 Artificial Neural Networks............................................. 72 2.4.7 Support Vector Machines............................................. 76 2.4.8 Logistic Regression......................................................... 80 2.4.9 Bayesian Network Classifiers ....................................... 82 2.4.9.1 Discrete Bayesian Network Classifiers .... 82 2.4.9.2 Continuous Bayesian Network Classifiers . . 89 2.4.10 Metaclassifiers ............................................................... 90 Bayesian Networks..................................................................... 94 2.5.1 Fundamentals of Bayesian Networks........................... 94 2.5.2
Inference in Bayesian Networks.................................... 100 2.5.2.1 Types of Inference.......................................... 100 2.5.2.2 Exact Inference .............................................. 102 2.5.2.3 Approximate Inference ........................................107 2.5.3 Learning Bayesian Networks fromData...................... 108 2.5.3.1 Learning Bayesian Network Parameters . . . 108 2.5.3.2 Learning Bayesian Network Structures............... Ill Modeling Dynamic Scenarios with Bayesian Networks .... 115 2.6.1 Data Streams........................... 115 2.6.2 Dynamic, Temporal and Continuous Time Bayesian Networks........................................................................ 119 2.6.3 Hidden Markov Models ................................... 123 2.6.3.1 Evaluation of the Likelihood of an Observation Sequence......................................................... 125 2.6.3.2 Decoding......................................................... 126 2.6.3.3 Hidden MarkovModel Training ......................... 127 Machine Learning Tools............................................................ 128 The Frontiers of Machine Learning................................................131 3 Applications of Machine Learning in Industrial Sectors 3.1 Energy Sector ........................................................................... 3.1.1 Oil.................................................................................... 3.1.2 Gas ................................................................................. 3.2 Basic Materials
Sector............................................................... 3.2.1 Chemicals........................................................................ 133 133 134 135 136 136
Contents vii 3.2.2 Basic Resources.................................................................. 138 Industrials Sector ....................................................................... 139 3.3.1 Construction and Materials................................................. 141 3.3.2 Industrial Goods and Services.............................................. 141 3.4 Consumer Services Sector.......................................................... 143 3.4.1 Retail................................................................................ 143 3.4.2 Media................................................................................ 144 3.4.3 Tourism............................................................................. 144 3.5 Healthcare Sector ....................................................................... 145 3.5.1 Cancer................................................................................ 146 3.5.2 Neuroscience................................................................... 148 3.5.3 Cardiovascular................................................................ 149 3.5.4 Diabetes............................................................................. 150 3.5.5 Obesity............................................................................. 150 3.5.6 Bioinformatics................................................................ 150 3.6 Consumer Goods Sector....................................................................151 3.6.1 Automobiles ..........................................................................151
3.6.2 Food and Beverages....................................................... 152 3.6.3 Personal and Household Goods..................................... 155 3.7 Telecommunications Sector....................................................... 156 3.7.1 Software for Network Analysis ........................................... 157 3.7.2 Data Transmission.................................................................157 3.8 Utilities Sector............................................................................. 159 3.8.1 Utilities Generation ....................................................... 159 3.8.2 Utilities Distribution....................................................... 160 3.9 Financial Services Sector .................................................................161 3.9.1 Customer-Focused Applications........................................... 161 3.9.2 Operations-Focused Applications.................................. 162 3.9.3 Trading and Portfolio Management Applications ... 163 3.9.4 Regulatory Compliance and Supervision Applications 163 3.10 Information Technology Sector................................................. 164 3.10.1 Hardware and semi-conductors..................................... 164 3.10.2 Software............................................................................. 165 3.10.3 Data Center Management.............................................. 165 3.10.4 Cybersecurity.................................................................... 166 3.3 4 Component-Level Case Study: Remaining Useful Life of Bearings 167 4.1
Introduction ......................................................................................167 4.2 Ball Bearing Prognostics ........................................................... 168 4.2.1 Data-Driven Techniques................................................. 168 4.2.2 PRONOSTIA Testbed.................................................... 170 4.3 Feature Extraction from Vibration Signals ............................ 170 4.4 Hidden Markov Model-Based RUL Estimation ...................... 175 4.4.1 Hidden Markov Model Construction...................................177
viii Contents 4.5 4.6 Results and Discussion ............................................................ 179 4.5.1 RUL Results.................................................................. 179 4.5.2 Interpretation of the Degradation Model..................... 180 Conclusions and Future Research ............................................. 181 4.6.1 Conclusions........................................................................... 181 4.6.2 Future Research .................................................................. 181 5 Machine-Level Case Study: Fingerprint of Industrial Motors 185 5.1 5.2 Introduction .............................................................................. 185 Performance of Industrial Motors as a Fingerprint ............... 186 5.2.1 Improving Reliability Models with Fingerprints .... 186 5.2.2 Industrial Internet Consortium Testbed...........................187 5.2.3 Testbed Dataset Description ....................................... 193 5.3 Clustering Algorithms forFingerprint Development .............. 194 5.3.1 Agglomerative Hierarchical Clustering........................ 195 5.3.2 iF-means Clustering...................................................... 195 5.3.3 Spectral Clustering......................................................... 196 5.3.4 Affinity Propagation...................................................... 196 5.3.5 Gaussian Mixture Model Clustering.................................197 5.3.6 Implementation Details................................................ 198 5.4 Results and Discussion
............................................................. 198 5.5 Conclusions and FutureResearch ............................................ 205 5.5.1 Conclusions..................................................................... 205 5.5.2 Future Research ............................................................ 205 6 Production-Level Case Study: Automated Visual Inspection of a Laser Process 207 6.1 6.2 Introduction .............................................................................. Laser Surface Heat Treatment ................................................ 6.2.1 Image Acquisition ......................................................... 6.2.2 Response Time Requirement ....................................... 6.3 Anomaly Detection-Based AVI System ................................. 6.3.1 Anomaly Detection Algorithms in Image Processing . 6.3.1.1 Probabilistic Anomaly Detection.................. 6.3.1.2 Distance-Based Anomaly Detection............ 6.3.1.3 Reconstruction-Based Anomaly Detection . . 6.3.1.4 Domain-Based Anomaly Detection............... 6.3.2 Proposed Methodology................................................... 6.3.2.1 Feature Extraction.......................................... 6.3.2.2 Dynamic Bayesian Networks Implementation 6.3.2.3 Performance Assessment .............................. 6.4 Results and Discussion ............................................................ 6.4.1 Performance of the AVI System.................................... 6.4.2 Interpretation of the Normality Model........................ 207 210 211 215 215 216
217 217 218 219 219 222 225 227 227 227 229
Contents ix 6.4.2.1 6.5 Relationships in the Dynamic Bayesian Network Structure.......................................................... 229 6.4.2.2 Relationships in the Dynamic Bayesian Network Parameters....................................................... 239 Conclusions and Future Research .......................................... 246 6.5.1 Conclusions....................................................................... 246 6.5.2 Future Research................................................................... 247 7 Distribution-Level Case Study: Forecasting of Air Freight Delays 249 7.1 Introduction ................................................................................ 249 7.2 Air Freight Process ......................................................................... 251 7.2.1 Data Preprocessing.......................................................... 252 7.2.1.1 Simplification of Planned/Actual Times . . . 255 7.2.1.2 Transport Leg Reordering..................................... 257 7.2.1.3 Airport Simplification...................................... 258 7.2.1.4 Normalizing the Length of Each Business Process................................................................... 261 7.3 Supervised Classification Algorithms for Forecasting Delays . 262 7.3.1 fc-Nearest Neighbors....................................................... 262 7.3.2 Classification Trees.......................................................... 263 7.3.3 Rule Induction................................................................. 264 7.3.4 Artificial Neural
Networks.............................................. 265 7.3.5 Support Vector Machines.............................................. 266 7.3.6 Logistic Regression................................................................ 267 7.3.7 Bayesian Network Classifiers ..............................................267 7.3.8 Metaclassifiers ................................................................. 268 7.3.9 Implementation Details of Classification Algorithms . 270 7.4 Results and Discussion ............................................................. 270 7.4.1 Compared Classifiers............................................................. 271 7.4.2 Quantitative Comparison of Classifiers......................... 273 7.4.2.1 Multiple Hypothesis Testing.......................... 274 7.4.2.2 Online Classification of Business Processes . 275 7.4.3 Qualitative Comparison of Classifiers..................................277 7.4.3.1 C4.5.......................................................................... 277 7.4.3.2 RIPPER........................................................... 284 7.4.3.3 Bayesian Network Classifiers.......................... 284 7.4.4 Feature Subset Selection................................................. 288 7.5 Conclusions and Future Research ........................................... 289 7.5.1 Conclusions....................................................................... 289 7.5.2 Future Research....................................................................291 Bibliography 293 Index 325
“This book brings machine learning techniques to the lloT community through a story that is able to be understood and applied by engineers and practitioners traditionally distanced from these techniques. Testbeds fuel the advancement of the lloT with results from real-world data that can be applied to showcase machine learning capabilities. This book provides a clear description of how data is managed through an lloT architecture and demonstrates the power of testbeds.” -Richard Soley, Executive Director, Industrial Internet Consortium (IIC) “The Machine Learning techniques presented in this highly relevant publication provide an excellent overview of key areas critical to know about when imple menting those fundamentally renewed algorithms that are driving the Fourth Industrial Revolution. Using real-world lloT applications, this book presents a clear description of sensor fusion and machine learning analytics technologies, where programmable logic and other hardware technologies play a central role in the data acquisition, analysis, and transformation implementations to realize actionable insights through real world lloT applications described in this book.” -Christoph Fritsch, Senior Director Industrial loT, Scientific and Medical Business Unit, Xilinx Inc. “This book fills a gap in the current technological developments presenting the most extensive and in-depth analysis of machine learning methods for industrial applications. It is very well written and organized, with special focus on professional, researchers, and post-graduate students of both industrial
engineering and machine learning.” -Joao Gama, Associate Professor, Porto University Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the Fourth Industrial Revo lution and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the Fourth Industrial Revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address real-world problems in the manufacturing or logistics do mains, and approaches machine learning solutions from an application-orient ed point of view. It should be of special interest to researchers interested in real-world industrial problems.
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| id | DE-604.BV045303438 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:23:30Z |
| institution | BVB |
| isbn | 9780815356226 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030690544 |
| oclc_num | 1083270508 |
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| physical | XIII, 333 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
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| publisher | Taylor & Francis, CRC Press |
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| series2 | Chapman & Hall / CRC data mining and knowledge series |
| spellingShingle | Larrañaga, Pedro Atienza, David Diaz-Rozo, Javier Ogbechie, Alberto Puerto-Santana, Carlos Bielza, Concha Industrial applications of machine learning Machine learning / Industrial applications Machine learning / Industrial applications fast Industrie (DE-588)4026779-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Fertigung (DE-588)4016899-2 gnd |
| subject_GND | (DE-588)4026779-9 (DE-588)4193754-5 (DE-588)4016899-2 |
| title | Industrial applications of machine learning |
| title_auth | Industrial applications of machine learning |
| title_exact_search | Industrial applications of machine learning |
| title_full | Industrial applications of machine learning Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza |
| title_fullStr | Industrial applications of machine learning Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza |
| title_full_unstemmed | Industrial applications of machine learning Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza |
| title_short | Industrial applications of machine learning |
| title_sort | industrial applications of machine learning |
| topic | Machine learning / Industrial applications Machine learning / Industrial applications fast Industrie (DE-588)4026779-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Fertigung (DE-588)4016899-2 gnd |
| topic_facet | Machine learning / Industrial applications Industrie Maschinelles Lernen Fertigung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030690544&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030690544&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT larranagapedro industrialapplicationsofmachinelearning AT atienzadavid industrialapplicationsofmachinelearning AT diazrozojavier industrialapplicationsofmachinelearning AT ogbechiealberto industrialapplicationsofmachinelearning AT puertosantanacarlos industrialapplicationsofmachinelearning AT bielzaconcha industrialapplicationsofmachinelearning |