What Every Engineer Should Know About Risk Engineering and Management
Completely updated, this new edition uniquely explains how to assess and handle technical risk, schedule risk, and cost risk efficiently and effectively for complex systems that include Artificial Intelligence, Machine Learning, and Deep Learning. It enables engineering professionals to anticipate failures and highlight opportunities to turn failure into success through the systematic application of Risk Engineering. What Every Engineer Should Know About Risk Engineering and Management, Second Edition discusses Risk Engineering and how to deal with System Complexity and Engineering Dynamics, as it highlights how AI can present new and unique ways that failures can take place. The new edition extends the term "Risk Engineering" introduced by the first edition, to Complex Systems in the new edition. The book also relates Decision Tree which was explored in the first edition to Fault Diagnosis in the new edition and introduces new chapters on System Complexity, AI, and Causal Risk Assessment along with other chapter updates to make the book current.
- Discusses Risk Engineering and how to deal with System Complexity and Engineering Dynamics
- Highlights how AI can present new and unique ways of failure that need to be addressed
- Extends the term "Risk Engineering" introduced by the first edition to Complex Systems in this new edition
- Relates Decision Tree which was explored in the first edition to Fault Diagnosis in the new edition
- Includes new chapters on System Complexity, AI, and Causal Risk Assessment along with other chapters being updated to make the book more current
The audience is the beginner with no background in Risk Engineering and can be used by new practitioners, undergraduates, and first-year graduate students.
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. 1.5 Engineering - A Profession of Managing Technical Risk. 2. Risk Identification - Understanding the Limits of Engineering Designs. 2.1 The Fall of Icarus - 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. 2.6 Artificial Intelligence (AI) and its Shocking Failures. 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. 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”. 4.4 FMEA: Failure Mode and Effect Analysis. 4.5 Redundancy and Fault Tolerance. 4.6 Risk Engineering and Integrating Information Science. 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. 5.7 Risk and Benefit: Balancing the Engineering Equation. 6. From Risk Engineering to Risk Management. 6.1 Panama Canal: Recognizing and Managing Risk. 6.2 Project Risk Assessment: Quantify Risk Triangle. 6.3 Why Artificial Intelligence Projects Fail - How To Avoid?. 6.4 Project Risk Control. 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. 7.6 Step 4: Conduct Experiments and Data Analysis. 7.7 Step 5: Prediction of Cost-Risk Under Selected Parameter Levels. 7.8 Life-Cycle Cost Management (LCCM). 7.9 Probabilistic Cost Drivers: Quantifying Complexity Of Project Budgeting. 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. 8.4 Schedule Risk for a Single Dominant Critical Path. 8.5 Schedule Risk for Multiple Critical Paths. 8.6 Probabilistic Critical Paths: Quantifying Complexity of Project Scheduling. 9. Integrated Risk Management and Computer Simulation. 9.1 An Integrated View of Risk. 9.2 Integrated Risk Management. 9.3 Incorporating the Impact of Schedule Risk. 9.4 Monte-Carlo Simulation. 9.5 Digital Transformation in the Face of Covid-19. 9.6 Apply Wang Entropy to Analyze Mode Confusion, a Challenge to Risk Management at Age of Autonomy.