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What Every Engineer Should Know About Risk Engineering and Management, 2nd Edition

Name: What Every Engineer Should Know About Risk Engineering and Management, 2nd Edition
Author: John X. Wang
ISBN: 9781000906622

by John X. Wang

July 2023

Intermediate to advanced

264 pages

8h 22m

English

CRC Press

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Cover Page
Half-Title Page
Series Page
Title Page
Copyright Page
Dedication Page
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 Success1.2 Risk Assessment – Quantification of Potential Failures1.3 Risk Engineering – Converting Risk into Opportunities1.4 System Complexity – Measured by Wang Entropy1.4.1 Imagination for Problem-Solving: Transfer a Risk Engineering Problem into a Thermodynamic Problem1.4.2 Wang Entropy as a Measure of System Complexity1.5 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 Design2.2 Overload of Failures: Fracture and Its Mechanics2.3 Wear-Out Failures: Crack Initiation and Growth2.4 Environmental Impact: Temperature-Related Failure2.5 Software and Related “Hard” Failures2.5.1 Identification of Hazards and Software Causal Factors2.5.2 Identification of Software Safety Requirements2.5.3 Identification of Testing and IV&V Requirements2.6 Artificial Intelligence (AI) and Its Shocking Failures2.6.1 Case Study: A320 Warsaw Crash2.6.1.1 Description of the Software of the Aircraft System2.6.1.2 Causes of the Accident and Insufficiency with Software Algorithms2.6.1.3 Consequence2.6.2 Examples: Why Artificial Intelligence Fails?2.6.2.1 AI to Treat Cancer May Result in Patient Death2.6.2.2 A Mask can Fool AI for Secure System Access by a Face2.6.2.3 Death from an Uber Self-Driving Car2.6.2.4 Mars Polar Lander2.6.2.5 Boeing 787 Lithium Battery Fires2.6.2.6 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 Risk2.6.2.9 Honda Odyssey: 344,000 Minivans Recalled2.6.2.10 Toyota Unintended Acceleration2.6.2.11 AI-driven Cart Broken Down on the Tarmac2.6.2.12 AI Was Unable to Identify Images for Traffic Jam Pilots2.6.3 Risk Engineering of Artificial Intelligence: Dealing with Unknown/Unsafe Scenarios2.6.4 Failure Modes in Artificial Intelligence (AI)/Machine Learning (ML)2.6.4.1 Intentional Failures2.6.4.2 Unintentional Failures
Chapter 3 Risk Assessment: Extending Murphy's Law
3.1 Titanic: Connoisseurs of Engineering Failure3.2 Risk Assessment: “How Likely It Is That A Thing Will Go Wrong”3.3 Risk Assessment for Multiple Failure Modes3.4 Fault Tree Analysis: Deductive Risk Assessment3.5 Event Tree Analysis: Inductive Risk Assessment3.6 A Risk Example: The TMI Accident3.7 An International Risk Scale3.8 Information Gain: Causal Risk Assessment Based on Wang Entropy3.8.1 Case Study Artificial Intelligence: Level 3 Autonomous Driving Vehicle with a Traffic Jam Pilot3.8.1.1 Risk Engineering Goal for a Traffic Jam Pilot3.8.1.2 Risk Management for a Traffic Jam Pilot3.8.1.3 AI Was Unable to Identify Images for Traffic Jam Pilots3.8.1.4 Health Monitoring System for Next-Generation Autonomous Driving Using Machine Learning3.8.1.5 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 Design4.2 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 Engineering4.3.2.1 System Fault Diagnosis4.3.2.2 System Reliability, Maintainability, and Testability Evaluation4.3.2.3 Reliability Block Diagram (RBD) as a Tool for Design for Risk Engineering4.4 FMEA: Failure Mode and Effect Analysis4.4.1 Failure4.4.2 Failure Mode4.4.3 Failure Mechanisms4.4.4 Failure Effect4.4.5 Occurrence Ranking (O)4.4.6 Severity Ranking (S)4.4.7 Detection Ranking (D)4.5 Redundancy and Fault Tolerance4.5.1 Fault Avoidance4.5.2 Fault Removal4.5.3 Fault Tolerance and Fault Evasion4.6 Risk Engineering and Integrating Information Science4.6.1 Diagnosis: A Process of Minimizing Wang Entropy4.6.2 Maximal Information: The Criteria for Selecting Inspection4.6.3 Conclusion
Chapter 5 Risk Acceptability: Uncertainty in Perspective
5.1 Uncertainty: Why Bridges Fall Down5.2 Risk Mitigation: How Buildings Stand Up5.3 From Safety Factor to Safety Index5.4 Converting Safety Index into Probability of Failure5.5 Quantitative Safety Goals: Probability vs. Consequence5.6 Diagnosability: Additional Element of Risk5.6.1 Diagnosability5.6.2 Diagnosability Plan: Ensure the Diagnosability of a Partially Controllable System5.6.3 Design for Diagnosability5.6.3.1 Preventative Maintenance5.6.3.2 Modularization5.6.3.3 Poka-yoke Design5.6.3.4 Failure Mode Effects Analysis (FMEA)5.6.3.5 Component Test5.6.3.6 Status Signal's Redundancy5.6.3.7 Components and Sensors with Built-in Communication Protocols5.6.3.8 Summary5.6.4 Fault Detection and Identification (FDI)5.6.5 Case Study: Fault Detection and Identification (FDI) and Diagnosability for an Electric Vehicle5.7 Risk and Benefit: Balancing the Engineering Equation
Chapter 6 From Risk Engineering to Risk Management
6.1 Panama Canal: Recognizing and Managing Risk6.1.1 Risk Recognition at an Earlier Stage6.1.2 Risk Management at a Later Stage6.2 Project Risk Assessment: Quantify Risk Triangle6.2.1 Identify Risk Items6.2.2 Quantify Risk Items6.2.2.1 Tigers6.2.2.2 Alligators6.2.2.3 Puppies6.2.2.4 Kittens6.2.3 Prioritize Risk Items6.3 Why Artificial Intelligence Projects Fail – How to Avoid?6.3.1 Uncertain Business Goals6.3.2 Data of Poor Quality6.3.3 Teams Not Working Together as a Team6.3.4 Inadequate Talent6.3.5 Absence of Standards and Governance6.3.6 Lack of Ownership and Commitment from the Leadership6.3.7 QA Testing Not Being Used6.3.8 How to Ensure Successful AI/ML Projects?6.4 Project Risk Control6.4.1 Mitigate Risks6.4.2 Plan for Emergencies6.4.3 Measure and Control Residual Risks
Chapter 7 Cost Risk: Interacting with Engineering Economy
7.1 Engineering: The Art of Doing Well Inexpensively7.2 Taguchi's Robust Design: Minimize Total Cost7.3 Step 1: Identify System Function and Noise Factors7.4 Step 2: Identify Total Cost Function and Control Factors7.5 Step 3: Design Matrix of Experiments and Define Data Analysis7.5.1 Construct Orthogonal Arrays7.5.2 Orthogonal-Array-Based Simulation7.6 Step 4: Conduct Experiments and Data Analysis7.6.1 Signal-to-Noise Ratio7.6.2 Data Analysis Using the S/N 7.7 Step 5: Prediction of Cost Risk Under Selected Parameter Levels7.8 Life-Cycle Cost Management (LCCM)7.8.1 Why Life-Cycle Cost Management?7.8.2 Life-Cycle Cost Analysis Theory Is Simple7.8.3 Control Life-Cycle Cost Management Process7.9 Probabilistic Cost Drivers: Quantifying Complexity of Project Budgeting7.9.1 Common Probabilistic Cost Risk Modeling Methods7.10 Summary
Chapter 8 Schedule Risk: Identifying and Controlling Critical Paths
8.1 Schedule: Deliver Engineering Products on Time8.2 Critical Path: Driver of Schedule Risk8.3 Find and Analyze Critical Path8.3.1 PERT Analysis8.3.2 CPM Analysis8.4 Schedule Risk for a Single Dominant Critical Path8.5 Schedule Risk for Multiple Critical Paths8.5.1 Structure8.5.2 Management8.6 Probabilistic Critical Paths: Quantifying Complexity of Project Scheduling8.6.1 Probabilistic Critical Path8.6.2 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 Risk9.2 Integrated Risk Management9.2.1 Solution9.3 Incorporating the Impact of Schedule Risk9.4 Monte Carlo Simulation9.5 Digital Transformation in the Face of COVID-199.5.1 Risk Engineering of Digital Transformation: COVID-19 Has Upped the Bar9.5.2 Digital Revolution: A Top Priority for Governments’ Policy Objectives9.5.3 Need to Create a Digital Future that Is More Inclusive9.5.4 Managing Big Data Transformation's Complexity, A Driver of Risk9.5.4.1 Aggregation9.5.4.2 Attribute Construction9.5.4.3 Discretization9.5.4.4 Generalization9.5.4.5 Integration9.5.4.6 Manipulation9.5.4.7 Normalization9.5.4.8 Smoothing9.5.5 Conclusion9.6 Apply Wang Entropy to Analyze Mode Confusion, a Challenge to Risk Management at the Age of Autonomy9.6.1 Case Study 1: Developing a Clear Interface for Control Transfer in a Level 2 Automated Driving System9.6.1.1 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 System9.6.1.3 What Is Mode Awareness and Automated Driving, and How Is It Quantified?9.6.1.4 Conclusion9.6.2 Case Study 2: Pilots of 777s and 787s Warned over Pitch-Guidance Mode Slip before Take-Off9.6.3 Apply Wang Entropy to Analyze Mode Confusion
Index

Overview

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.

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