Chapter 1.0 - A.I - Machine learning applied for maintenance 4.0
Chapter 2 - Maintenance Concepts
Chapter 3 - Prognostic Health management
Chapter 4 - Machine Learning Concepts
Chapter 5 - Unsupervised Machine Learning
Chapter 6 - supervised Machine Learning
Classification
Chapter 7 - Supervised Machine Learning Regression
Chapter 8 - Ensemble Methods
Chapter 9 - Convolutional Neural Network
Chapter 10 - Reiforcement Learning (RL)
Chapter 11 - Natural Language Processing
Chapter 12 - Asset Management
Chapter 1 - RAMS and LCC Engineering Program
Chapter 2 - Failure Mode and Effect Analysis(FMEA)
Chapter 3 - Reliability Centred Maintenance (RCM)
Chapter 4 - Lifetime Data Analysis (LDA)
Chapter 5 - Design for Reliability (DFR)
Chapter 6 - Human factor
Chapter 7 - Reliability, Availability and Maintainability Analysis (RAM)
Chapter 8 - Integrated Logistic Support (ILS)
Chapter 9 - Safety
Chapter 10 - Asset Management (AM)
CHAPTER 1: Lifetime Data Analysis
CHAPTER 2: Accelerated Life Test, Reliability Growth Analysis,
and Probabilistic Degradation Analysis
CHAPTER 3: Reliability and Maintenance
CHAPTER 4: Reliability, Availability, and Maintainability (RAM Analysis)
CHAPTER 5: Human Reliability Analysis
CHAPTER 6: Reliability and Safety Processes
CHAPTER 7: Reliability Management
CHAPTER 8: Asset Management
CHAPTER 1: Occupational Risk
CHAPTER 2: Qualitative Risk Analysis: Concepts and Methods
CHAPTER 3: Quantitative Risk Analysis: Concepts and Methods
CHAPTER 4: Consequence and Effect Analysis
CHAPTER 5: Emergency Response Planning
CHAPTER 6: Incident and Accident Analysis
CHAPTER 7: Human Factor
CHAPTER 8: Safety Standards
CHAPTER 9: Safety and Occupational Health Management