This book reviews and presents several advanced approaches to energy infrastructure assets' intelligent reliability and maintainability. Each introduced model provides case studies indicating high efficiency, robustness, and applicability, allowing readers to utilize them in their understudy intelligent reliability and maintainability of energy infrastructure assets domains.
The book begins by reviewing the state-of-the-art research on the reliability and maintainability of energy infrastructure assets and emphasizes the intelligent tools and methods proposed from a bibliometric and literature review point of view. It then progresses logically, dedicating a chapter to each approach, dynamic Bayesian modeling network, convolutional neural network model, global average pooling-based convolutional Siamese network, an integrated probabilistic model for the failure consequence assessment, and more.
This book interests professionals and researchers working in reliability and maintainability and postgraduate and undergraduate students studying intelligent reliability applications and energy infrastructure assets' maintainability.
Author(s): He Li, Weiwen Peng, Sidum Adumene, Mohammad Yazdi
Series: Studies in Systems, Decision and Control, 473
Publisher: Springer
Year: 2023
Language: English
Pages: 153
City: Cham
Preface
Contents
1 Advances in Intelligent Reliability and Maintainability of Energy Infrastructure Assets
1.1 Introduction
1.2 Data Collection and Data Source
1.3 Advances in Intelligent Reliability Investigations of Energy Infrastructure Assets
1.3.1 Bibliometric
1.3.2 Literature Review
1.4 Advances in Intelligent Maintainability Investigations of Energy Infrastructure Assets
1.4.1 Bibliometric
1.4.2 Literature Review
1.5 Discussions
1.6 Conclusions
References
2 Cutting Edge Research Topics on System Safety, Reliability, Maintainability, and Resilience of Energy-Critical Infrastructures
2.1 Introduction
2.2 Research Methodology
2.3 Results and Discussions
2.4 Conclusion
References
3 Operations Management of Critical Energy Infrastructure: A Sustainable Approach
3.1 Introduction
3.2 Methodology
3.2.1 Constructing a Dynamic Bayesian Network
3.2.2 Dynamic Bayesian Network Inference
3.2.3 Discretizing the Variables
3.2.4 Reliability Analysis of the Energy Infrastructure Under the Material Degradation Process
3.3 Application of Study
3.4 Conclusion
References
4 An Improved LeNet-5 Convolutional Neural Network Supporting Condition-Based Maintenance and Fault Diagnosis of Bearings
4.1 Introduction
4.2 Preliminary
4.2.1 Convolutional Neural Network (CNN)
4.2.2 Time–Frequency Analysis of Vibration Signal
4.3 The Proposed Model for Bearing Fault Diagnosis
4.4 Case Study and Validation
4.4.1 Data Preprocessing
4.4.2 Results
4.5 Conclusion
References
5 Using Global Average Pooling Convolutional Siamese Networks for Fault Diagnosis of Planetary Gearboxes
5.1 Introduction
5.2 Methods
5.2.1 Convolutional Siamese Networks Based on Global Average Pooling
5.2.2 GAPCSN-Based Fault Diagnosis
5.3 Illustrative Cases and Results
5.3.1 Data Description
5.3.2 Network Parameter Design
5.3.3 Model Training
5.3.4 Model Testing
5.3.5 Results
5.4 Conclusion
References
6 Advances in Failure Prediction of Subsea Components Considering Complex Dependencies
6.1 Introduction
6.2 The Complexity of Subsea Components Failure
6.3 Recent Advances in Subsea Components Failure Assessment
6.3.1 Subsea Blowout Preventer (SBOP)
6.3.2 Subsea Pipelines
6.4 Further Considerations
6.5 Conclusions
References
7 An Intelligent Cost-Based Consequence Model for Offshore Systems in Harsh Environments
7.1 Introduction
7.2 Dynamic Failure Assessment of Offshore System
7.2.1 Structural Learning Using Bayesian Network
7.2.2 Failure Consequence Analysis
7.3 Application and Results Analysis
7.3.1 UDC Propagation Prediction Under Unstable Influencing Factors
7.3.2 An Integrated Cost-Based Consequences Assessment
7.4 Conclusions
References
8 A Sustainable Circular Economy in Energy Infrastructure: Application of Supercritical Water Gasification System
8.1 Introduction
8.1.1 Background: Circular Economy and Risk Management
8.1.2 Background: Circular Economy and Energy Infrastructure
8.1.3 Background: Energy Infrastructure and Risk Management
8.2 Discussion
8.3 Conclusion
References
9 Attention Towards Energy Infrastructures: Challenges and Solutions
9.1 Introduction
9.2 The Identified Challenges of Energy Infrastructures
9.3 How to Deal with All Energy Infrastructure Challenges
9.4 What is the Main Role of Artificial Intelligence (AI) Utilization in Energy Infrastructure
9.5 Conclusion
References
