What Every Engineer Should Know About Risk Engineering and Management

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
List of Figures
List of Tables
Foreword
Preface
About the Author
Chapter 1: Risk Engineering: Dealing with System Complexity and Engineering Dynamics
1.1. Understanding Failure Is Critical to Engineering Success
1.2. Risk Assessment – Quantification of Potential Failures
1.3. Risk Engineering – Converting Risk into Opportunities
1.4. System Complexity – Measured by Wang Entropy
1.4.1. Imagination for Problem-Solving: Transfer a Risk Engineering Problem into a Thermodynamic Problem
1.4.2. Wang Entropy as a Measure of System Complexity
1.5. Engineering – A Profession of Managing Technical Risk
Chapter 2: Risk Identification: Understanding the Limits of Engineering Designs
2.1. The Fall of Icarus – Understanding the Limits of Engineering Design
2.2. Overload of Failures: Fracture and Its Mechanics
2.3. Wear-Out Failures: Crack Initiation and Growth
2.4. Environmental Impact: Temperature-Related Failure
2.5. Software and Related “Hard” Failures
2.5.1. Identification of Hazards and Software Causal Factors
2.5.2. Identification of Software Safety Requirements
2.5.3. Identification of Testing and IV&V Requirements
2.6. Artificial Intelligence (AI) and Its Shocking Failures
2.6.1. Case Study: A320 Warsaw Crash
2.6.1.1. Description of the Software of the Aircraft System
2.6.1.2. Causes of the Accident and Insufficiency with Software Algorithms
2.6.1.3. Consequence
2.6.2. Examples: Why Artificial Intelligence Fails?
2.6.2.1. AI to Treat Cancer May Result in Patient Death
2.6.2.2. A Mask can Fool AI for Secure System Access by a Face
2.6.2.3. Death from an Uber Self-Driving Car
2.6.2.4. Mars Polar Lander
2.6.2.5. Boeing 787 Lithium Battery Fires
2.6.2.6. Schiaparelli Lander (2016)
2.6.2.7. Hitomi Satellite (2016)
2.6.2.8. Ford Fusion/Escape (2013): Ford Tells 89,000 Escape, Fusion Owners to Park Cars because of Engine Fire Risk
2.6.2.9. Honda Odyssey: 344,000 Minivans Recalled
2.6.2.10. Toyota Unintended Acceleration
2.6.2.11. AI-driven Cart Broken Down on the Tarmac
2.6.2.12. AI Was Unable to Identify Images for Traffic Jam Pilots
2.6.3. Risk Engineering of Artificial Intelligence: Dealing with Unknown/Unsafe Scenarios
2.6.4. Failure Modes in Artificial Intelligence (AI)/Machine Learning (ML)
2.6.4.1. Intentional Failures
2.6.4.2. Unintentional Failures
Chapter 3: Risk Assessment: Extending Murphy’s Law
3.1. Titanic: Connoisseurs of Engineering Failure
3.2. Risk Assessment: “How Likely It Is That A Thing Will Go Wrong”
3.3. Risk Assessment for Multiple Failure Modes
3.4. Fault Tree Analysis: Deductive Risk Assessment
3.5. Event Tree Analysis: Inductive Risk Assessment
3.6. A Risk Example: The TMI Accident
3.7. An International Risk Scale
3.8. Information Gain: Causal Risk Assessment Based on Wang Entropy
3.8.1. Case Study Artificial Intelligence: Level 3 Autonomous Driving Vehicle with a Traffic Jam Pilot
3.8.1.1. Risk Engineering Goal for a Traffic Jam Pilot
3.8.1.2. Risk Management for a Traffic Jam Pilot
3.8.1.3. AI Was Unable to Identify Images for Traffic Jam Pilots
3.8.1.4. Health Monitoring System for Next-Generation Autonomous Driving Using Machine Learning
3.8.1.5. What is Risk Assessment and Why Is It Required?
3.8.1.6. Using the Hazard and Operability (HAZOP) Technique to Identify Malfunctions
Chapter 4: Design for Risk Engineering: The Art of War Against Failures
4.1. Challenger: Challenging Engineering Design
4.2. Goal Tree: Understand “What” and “How”
4.3. Path Sets and Wang Entropy: “All the Roads to Rome”
4.3.1. Minimal Cut Sets (MCSs) and Minimal Path Sets (MPSs)
4.3.1.1. Minimal Cut Sets (MCSs)
4.3.1.2. Minimal Path Sets (MPSs)
4.3.2. Wang Entropy’s Applications to Design Risk Engineering
4.3.2.1. System Fault Diagnosis
4.3.2.2. System Reliability, Maintainability, and Testability Evaluation
4.3.2.3. Reliability Block Diagram (RBD) as a Tool for Design for Risk Engineering
4.4. FMEA: Failure Mode and Effect Analysis
4.4.1. Failure
4.4.2. Failure Mode
4.4.3. Failure Mechanisms
4.4.4. Failure Effect
4.4.5. Occurrence Ranking (O)
4.4.6. Severity Ranking (S)
4.4.7. Detection Ranking (D)
4.5. Redundancy and Fault Tolerance
4.5.1. Fault Avoidance
4.5.2. Fault Removal
4.5.3. Fault Tolerance and Fault Evasion
4.6. Risk Engineering and Integrating Information Science
4.6.1. Diagnosis: A Process of Minimizing Wang Entropy
4.6.2. Maximal Information: The Criteria for Selecting Inspection
4.6.3. Conclusion
Chapter 5: Risk Acceptability: Uncertainty in Perspective
5.1. Uncertainty: Why Bridges Fall Down
5.2. Risk Mitigation: How Buildings Stand Up
5.3. From Safety Factor to Safety Index
5.4. Converting Safety Index into Probability of Failure
5.5. Quantitative Safety Goals: Probability vs. Consequence
5.6. Diagnosability: Additional Element of Risk
5.6.1. Diagnosability
5.6.2. Diagnosability Plan: Ensure the Diagnosability of a Partially Controllable System
5.6.3. Design for Diagnosability
5.6.3.1. Preventative Maintenance
5.6.3.2. Modularization
5.6.3.3. Poka-yoke Design
5.6.3.4. Failure Mode Effects Analysis (FMEA)
5.6.3.5. Component Test
5.6.3.6. Status Signal’s Redundancy
5.6.3.7. Components and Sensors with Built-in Communication Protocols
5.6.3.8. Summary
5.6.4. Fault Detection and Identification (FDI)
5.6.5. Case Study: Fault Detection and Identification (FDI) and Diagnosability for an Electric Vehicle
5.7. Risk and Benefit: Balancing the Engineering Equation
Chapter 6: From Risk Engineering to Risk Management
6.1. Panama Canal: Recognizing and Managing Risk
6.1.1. Risk Recognition at an Earlier Stage
6.1.2. Risk Management at a Later Stage
6.2. Project Risk Assessment: Quantify Risk Triangle
6.2.1. Identify Risk Items
6.2.2. Quantify Risk Items
6.2.2.1. Tigers
6.2.2.2. Alligators
6.2.2.3. Puppies
6.2.2.4. Kittens
6.2.3. Prioritize Risk Items
6.3. Why Artificial Intelligence Projects Fail – How to Avoid?
6.3.1. Uncertain Business Goals
6.3.2. Data of Poor Quality
6.3.3. Teams Not Working Together as a Team
6.3.4. Inadequate Talent
6.3.5. Absence of Standards and Governance
6.3.6. Lack of Ownership and Commitment from the Leadership
6.3.7. QA Testing Not Being Used
6.3.8. How to Ensure Successful AI/ML Projects?
6.4. Project Risk Control
6.4.1. Mitigate Risks
6.4.2. Plan for Emergencies
6.4.3. Measure and Control Residual
Chapter 7: Cost Risk: Interacting with Engineering Economy
7.1. Engineering: The Art of Doing Well Inexpensively
7.2. Taguchi’s Robust Design: Minimize Total Cost
7.3. Step 1: Identify System Function and Noise Factors
7.4. Step 2: Identify Total Cost Function and Control Factors
7.5. Step 3: Design Matrix of Experiments and Define Data Analysis
7.5.1. Construct Orthogonal Arrays
7.5.2. Orthogonal-Array-Based Simulation
7.6. Step 4: Conduct Experiments and Data Analysis
7.6.1. Signal-to-Noise Ratio
7.6.2. Data Analysis Using the S/N
7.7. Step 5: Prediction of Cost Risk Under Selected Parameter Levels
7.8. Life-Cycle Cost Management (LCCM)
7.8.1. Why Life-Cycle Cost Management?
7.8.2. Life-Cycle Cost Analysis Theory Is Simple
7.8.3. Control Life-Cycle Cost Management Process
7.9. Probabilistic Cost Drivers: Quantifying Complexity of Project Budgeting
7.9.1. Common Probabilistic Cost Risk Modeling Methods
7.10. Summary
Chapter 8: Schedule Risk: Identifying and Controlling Critical Paths
8.1. Schedule: Deliver Engineering Products on Time
8.2. Critical Path: Driver of Schedule Risk
8.3. Find and Analyze Critical Path
8.3.1. PERT Analysis
8.3.2. CPM Analysis
8.4. Schedule Risk for a Single Dominant Critical Path
8.5. Schedule Risk for Multiple Critical Paths
8.5.1. Structure
8.5.2. Management
8.6. Probabilistic Critical Paths: Quantifying Complexity of Project Scheduling
8.6.1. Probabilistic Critical Path
8.6.2. Identifying the Probabilistic Critical Path(s)
8.6.3. What Can We Learn from the Probabilistic Critical Path?
Chapter 9: Integrated Risk Management and Computer Simulation
9.1. An Integrated View of Risk
9.2. Integrated Risk Management
9.2.1. Solution
9.3. Incorporating the Impact of Schedule Risk
9.4. Monte Carlo Simulation
9.5. Digital Transformation in the Face of COVID-19
9.5.1. Risk Engineering of Digital Transformation: COVID-19 Has Upped the Bar
9.5.2. Digital Revolution: A Top Priority for Governments’ Policy Objectives
9.5.3. Need to Create a Digital Future that Is More Inclusive
9.5.4. Managing Big Data Transformation’s Complexity, A Driver of Risk
9.5.4.1. Aggregation
9.5.4.2. Attribute Construction
9.5.4.3. Discretization
9.5.4.4. Generalization
9.5.4.5. Integration
9.5.4.6. Manipulation
9.5.4.7. Normalization
9.5.4.8. Smoothing
9.5.5. Conclusion
9.6. Apply Wang Entropy to Analyze Mode Confusion, a Challenge to Risk Management at the Age of Autonomy
9.6.1. Case Study 1: Developing a Clear Interface for Control Transfer in a Level 2 Automated Driving System
9.6.1.1. How Can We Eliminate Mode Confusion by Intuitively Managing the Transition between Autonomous and Manual Driving Modes?
9.6.1.2. Developing a Clear Interface for Control Transfer in a Level 2 Automated Driving System
9.6.1.3. What Is Mode Awareness and Automated Driving, and How Is It Quantified?
9.6.1.4. Conclusion
9.6.2. Case Study 2: Pilots of 777s and 787s Warned over Pitch-Guidance Mode Slip before Take-Off
9.6.3. Apply Wang Entropy to Analyze Mode Confusion
Index