Reliability Analysis and Asset Management of Engineering Systems.

Souza, Gilberto Francisco Martha de.
San Diego : Elsevier, 2021.
1 online resource (316 pages)
Advances in Reliability Science Ser.
Advances in Reliability Science Ser.

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Electronic books.
Reliability Analysis and Asset Management of Engineering Systems
Chapter 1: Introduction
1.1. Analysis of modern engineering systems
1.2. Book proposal
1.3. Book organization
Chapter 2: Reliability and maintenance fundamentals
2.1. Introduction
2.2. Reliability concepts
2.2.1. Failure rate function
2.2.2. Systems reliability Reliability block diagram Failure Mode and Effects Analysis (FMEA) Fault Tree Analysis (FTA)
2.3. Risk analysis
2.3.1. Risk management
2.3.2. Risk evaluation
2.4. Maintenance practices and planning
2.4.1. Reliability-centered maintenance
2.4.2. Risk-based maintenance
2.4.3. Availability
2.5. Concluding remarks
Chapter 3: Engineering systems fundamentals
3.1. Systems theory
3.2. Engineering systems
3.2.1. Properties
3.2.2. Hierarchy and control
3.3. Complex engineering system hazard analysis
3.3.1. Systems-Theoretic Process Analysis (STPA)
3.3.2. Functional Resonance Analysis Method (FRAM)
3.4. Systems engineering concepts applied to engineering systems design
3.4.1. Fundamentals of systems engineering
3.4.2. Functional analysis and allocation
3.4.3. Reliability allocation
3.5. Robust design
3.6. Model-based engineering fundamentals
3.7. Additional relevant complex engineering systems design topics
3.7.1. Maintainability
3.7.2. Interdependence
Chapter 4: Analysis of systems' reliability and availability
4.1. Introduction
4.2. Advanced techniques for reliability and availability analyses of complex systems
4.2.1. Markov analysis
4.2.2. Petri nets
4.2.3. Bayesian networks
4.2.4. Dynamic fault trees
4.3. Comments on imperfect maintenance.
4.4. Development of system reliability and availability analyses
Chapter 5: Engineering systems fault detection methods
5.1. Fault detection fundamental concepts
5.1.1. Fault classification
5.1.2. Excitability, detectability, and the fault detection process
5.1.3. Data monitoring Sensor sets
5.1.4. From measurements to symptoms
5.2. Fault detection methods and applications
5.2.1. Fault detection methods classification Fault detection and the input data source Fault detection and algorithm data labeling Fault detection and feature generation methods
5.2.2. Model-based fault detection methods Residual-based approach Parity space approach State observer approach Parameter estimation approach Causal models Abstraction hierarchy
5.2.3. Data-based fault detection methods Principal component analysis Partial least squares Independent component analysis Gaussian mixture model
5.2.4. Neural networks and fuzzy inference system approaches Input-output representation Fault feature generation
5.3. Dealing with uncertainties
Chapter 6: Engineering systems fault diagnosis methods
6.1. Fault diagnosis in the context
6.2. Challenges in fault diagnosis
6.2.1. Ability to represent and take advantage of prior knowledge
6.2.2. Ability to take into account different types of symptoms
6.2.3. Agility
6.2.4. Ability to discriminate different faults
6.2.5. Ease of elucidation
6.2.6. Ability to identify multiple simultaneous faults
6.2.7. Robustness
6.2.8. Adaptability
6.2.9. Novelty identifiability
6.2.10. Minimal development efforts
6.2.11. Owning uncertainty.
6.3. Fault diagnosis methods
6.3.1. Classification methods Naïve Bayes classifier Support vector machine Neural networks
6.3.2. Inference methods Fault tree analysis Expert systems Bayesian networks
6.4. Development of a fault diagnosis process
Chapter 7: Framework for engineering systems health monitoring and fault diagnosis
7.1. Fundamentals of health monitoring and fault diagnosis
7.2. A framework for health monitoring and fault diagnosis
7.2.1. Setting up the health monitoring and fault diagnosis process
7.2.2. Data acquisition, selection, and preprocessing
7.2.3. Fault detection and diagnosis
7.2.4. Fault evaluation
7.2.5. Maintenance decision making
7.3. Challenges of health monitoring and fault diagnosis
7.3.1. Complexity of engineering systems
7.3.2. Data management
7.3.3. Alignment
7.3.4. Embracing HMFD
Chapter 8: Engineering systems asset management
8.1. Asset management fundamentals
8.2 Maintenance in asset management landscape
8.3. Maintenance planning in asset management
8.4. Maintenance planning decision-making
8.5. Maintenance planning performance evaluation and improvement
Chapter 9: Examples of application
9.1. Methods and techniques for the examples of application
9.1.1. Study of the engineering systems
9.1.2. Reliability, availability and maintainability (RAM) analysis
9.1.3. Health monitoring and fault diagnosis
9.2. Analysis of a CODOG propulsion system of military ships
9.2.1. Study of the CODOG propulsion system
9.2.2. RAM analysis of the CODOG propulsion system
9.2.3. HMFD of the CODOG propulsion system
9.2.4. Asset management considerations
9.3. Analysis of a FPSO power generation system.
9.3.1. Study of the FPSO generation system
9.3.2. RAM analysis of the FPSO generation system
9.3.3. HMFD of the FPSO generation system
9.4. Final remarks
Chapter 10: Concluding remarks
10.1. Book contributions
10.2. Future developments
Description based on publisher supplied metadata and other sources.
USP, Escola Politécnica da.
Melani, Arthur Henrique De Andrade.
Michalski, Miguel Angelo De Carvalho.
Silva, Renan Favarao Da.