Cover......Page 1
Title Page......Page 5
Copyright......Page 6
Contents......Page 9
Preface......Page 17
1.1 The Domain‐Specific Methods for Risk Reduction......Page 21
1.2 The Statistical, Data‐Driven Approach......Page 23
1.3 The Physics‐of‐Failure Approach......Page 24
1.5 The Domain‐Independent Methods for Reliability Improvement and Risk Reduction......Page 26
2.1 Likelihood of Failure, Consequences from Failure, Potential Loss, and Risk of Failure......Page 29
2.2 Drawbacks of the Expected Loss as a Measure of the Potential Loss from Failure......Page 34
2.3 Potential Loss, Conditional Loss, and Risk of Failure......Page 35
2.4 Improving Reliability and Reducing Risk......Page 39
2.5 Resilience......Page 41
3.1.1 Techniques for Identifying and Assessing Failure Modes......Page 43
3.1.2 Effective Risk Reduction Procedure Related to Preventing Failure Modes from Occurring......Page 47
3.1.3.1 Case Study: Improving the Reliability of Automotive Suspension Springs by Root Cause Analysis......Page 48
3.1.4 Preventing Failure Modes by Removing Latent Faults......Page 49
3.2.1 Building in Redundancy......Page 51
3.2.2 Fault‐Tolerant Design......Page 54
3.2.3 Fail‐Safe Principle and Fail‐Safe Design......Page 55
3.2.4.1 Eliminating Design Vulnerabilities......Page 56
3.2.4.2 Reducing the Negative Impact of Weak Links......Page 57
3.2.4.3 Reducing the Likelihood of Unfavourable Combinations of Risk‐Critical Random Factors......Page 58
3.2.4.4 Reducing the Vulnerability of Computational Models......Page 59
3.3 Improving Reliability and Reducing Risk by Protecting Against Common Cause......Page 60
3.4 Improving Reliability and Reducing Risk by Simplifying at a System and Component Level......Page 62
3.5 Improving Reliability and Reducing Risk by Reducing the Variability of Risk‐Critical Parameters......Page 64
3.5.1 Case Study: Interaction Between the Upper Tail of the Load Distribution and the Lower Tail of the Strength Distribution......Page 66
3.6 Improving Reliability and Reducing Risk by Making the Design Robust......Page 68
3.6.1 Case Study: Increasing the Robustness of a Spring Assembly with Constant Clamping Force......Page 70
3.7.2 Built‐In Protection Reinforcement......Page 71
3.8 Improving Reliability and Reducing Risk by Condition Monitoring......Page 72
3.9 Reducing the Risk of Failure by Improving Maintainability......Page 76
3.10 Reducing Risk by Eliminating Factors Promoting Human Errors......Page 77
3.11 Reducing Risk by Reducing the Hazard Potential......Page 78
3.12 Reducing Risk by using Protective Barriers......Page 79
3.14 Risk Planning and Training......Page 80
4.1 The Method of Separation......Page 81
4.2.1.1 Case Study: Full Time Separation with Random Starts of the Events......Page 82
4.2.2.1 Case Study: A Time Separation by Using an Interlock......Page 83
4.2.3 Time Separation in Distributed Systems by Using Logical Clocks......Page 84
4.2.5 Separation of Duties to Reduce the Risk of Compromised Safety, Errors, and Fraud......Page 85
4.2.6.1 Case Study: Logical Separation of X‐ray Equipment by a Shared Unique Key......Page 86
4.2.7 Separation by Providing Conditions for Independent Operation......Page 87
4.3.1.1 Separation of Functions to Optimise for Maximum Reliability......Page 88
4.3.1.2 Separation of Functions to Reduce Load Magnitudes......Page 90
4.3.1.5 Separation of Functions to Prevent Unwanted Interactions......Page 91
4.4.1 Separation of Strength Across Components and Zones According to the Intensity of the Stresses from Loading......Page 92
4.4.2 Separation of Properties to Satisfy Conflicting Requirements......Page 94
4.4.3.1 Case Study: Separation in Geometry for a Cantilever Beam......Page 95
4.5.1 Separation at Distinct Values of a Risk‐Critical Parameter Through Deliberate Weaknesses and Stress Limiters......Page 96
4.5.3 Separation of Reliability Across Components and Assemblies According to Their Cost of Failure......Page 97
4.5.3.1 Case Study: Separation of the Reliability of Components Based on the Cost of Failure......Page 98
5.1 Reducing the Consequences from Failure Through Deliberate Weaknesses......Page 101
5.2.1 Deliberate Weaknesses Disconnecting Excessive Load......Page 102
5.2.2.1 Case Study: Reducing the Maximum Stress from Dynamic Loading by Energy‐Absorbing Elastic Components......Page 105
5.2.3 Designing Frangible Objects or Weakly Fixed Objects......Page 106
5.3.1 Deliberate Weaknesses Decoupling Damaged Regions and Limiting the Spread of Damage......Page 107
5.3.2 Deliberate Weaknesses Providing Stress and Strain Relaxation......Page 108
5.3.3 Deliberate Weaknesses Separating from Excessive Levels of Damage Accumulation......Page 110
5.4.1 Deflecting the Failure Location from Places Where the Cost of Failure is High......Page 111
5.5 Deliberate Weaknesses Designed to Provide Warning......Page 112
5.7 Deliberate Weaknesses and Stress Limiters......Page 114
6.1.1 Real‐Life Applications that Require Stochastic Separation......Page 117
6.1.2 Stochastic Separation of a Fixed Number of Random Events with Different Duration Times......Page 119
6.1.2.1 Case Study: Stochastic Separation of Consumers by Proportionally Reducing Their Demand Times......Page 122
6.1.3 Stochastic Separation of Random Events Following a Homogeneous Poisson Process......Page 125
6.1.4 Stochastic Separation Based on the Probability of Overlapping of Random Events for More than a Single Source Servicing the Random Demands......Page 126
6.1.5 Computer Simulation Algorithm Determining the Probability of Overlapping for More than a Single Source Servicing the Demands......Page 128
6.2 Expected Time Fraction of Simultaneous Presence of Critical Events......Page 130
6.2.1 Case Study: Expected Fraction of Unsatisfied Demand at a Constant Sum of the Time Fractions of User Demands......Page 132
6.3 Analytical Method for Determining the Expected Fraction of Unsatisfied Demand for Repair......Page 134
6.3.1 Case Study: Servicing Random Repairs from a System Including Components of Three Different Types, Each Characterised by a Distinct Repair Time......Page 135
6.4 Expected Time Fraction of Simultaneous Presence of Critical Events that have been Initiated with Specified Probabilities......Page 136
6.4.1 Case Study: Servicing Random Demands from Patients in a Hospital......Page 137
6.4.2 Case Study: Servicing Random Demands from Four Different Types of Users, Each Issuing a Demand with Certain Probability......Page 138
6.5.1 Fixed Number of Random Demands on a Time Interval......Page 139
6.5.2 Random Demands Following a Poisson Process on a Time Interval......Page 140
6.5.2.1 Case Study: Servicing Random Failures from Circular Knitting Machines by an Optimal Number of Repairmen......Page 142
7.1 Segmentation as a Problem‐Solving Strategy......Page 145
7.2 Creating a Modular System by Segmentation......Page 147
7.3.1 Creating Barriers Containing Damage......Page 149
7.3.4 Reducing Hazard Potential by Segmentation......Page 151
7.3.6 Limiting the Presence of Flaws by Segmentation......Page 152
7.4.1 Case Study: Improving Fault Tolerance of a Column Loaded in Compression by Segmentation......Page 153
7.4.2 Reducing the Vulnerability to a Single Failure by Segmentation......Page 156
7.5.1 Improving Load Distribution by Segmentation......Page 158
7.5.2 Improving Heat Dissipation by Segmentation......Page 159
7.5.3 Case Study: Reducing Stress by Increasing the Perimeter to Cross‐Sectional Area Ratio Through Segmentation......Page 160
7.6.1 Reducing the Likelihood of a Loss by Segmenting Opportunity Bets......Page 162
7.6.1.1 Case Study: Reducing the Risk of a Loss from a Risky Prospect Involving a Single Opportunity Bet......Page 163
7.6.2 Reducing the Likelihood of a Loss by Segmenting an Investment Portfolio......Page 164
7.6.3 Reducing the Likelihood of Erroneous Conclusion from Imperfect Tests by Segmentation......Page 165
7.7 Decreasing the Variation of Properties by Segmentation......Page 166
7.8 Improved Control and Condition Monitoring by Time Segmentation......Page 168
8.1 The Method of Inversion......Page 169
8.2 Improving Reliability by Inverting Functions, Relative Position, and Motion......Page 170
8.2.1 Case Study: Eliminating Failure Modes of an Alarm Circuit by Inversion of Functions......Page 171
8.2.2 Improving Reliability by Inverting the Relative Position of Objects......Page 172
8.2.2.1 Case Study: Inverting the Position of an Object with Respect to its Support to Improve Reliability......Page 173
8.3.1 Case Study: Improving Reliability by Inverting Mechanical Properties Whilst Maintaining an Invariant......Page 175
8.3.2 Case Study: Improving Reliability by Inverting Geometry Whilst Maintaining an Invariant......Page 176
8.4.1 Inverse States Cancelling Anticipated Undesirable Effects......Page 178
8.4.2 Inverse States Buffering Anticipated Undesirable Effects......Page 179
8.4.3 Inverse States Reducing the Likelihood of an Erroneous Action......Page 180
8.5.1 Inverting the Problem Related to Reliability Improvement and Risk Reduction......Page 181
8.5.1.1 Case Study: Reducing the Risk of High Employee Turnover......Page 182
8.5.2.2 Starting from the Desired Ideal End Result......Page 183
8.5.3 Improving Reliability and Reducing Risk by Moving Backwards to Contributing Factors......Page 184
8.5.3.1 Case Study: Identifying Failure Modes of a Lubrication System by Moving Backwards to Contributing Factors......Page 185
8.5.4 Inverse Thinking in Mathematical Models Evaluating or Reducing Risk......Page 186
8.5.4.1 Case Study: Using the Method of Inversion for Fast Evaluation of the Production Availability of a Complex System......Page 187
8.5.4.2 Case Study: Repeated Inversion for Evaluating the Risk of Collision of Ships......Page 190
9.1 Self‐Reinforcement Mechanisms......Page 197
9.2.1.1 Capturing a Self‐Reinforcing Proportional Response from Friction Forces......Page 199
9.2.1.2 Case Study: Transforming Friction Forces into a Proportional Response in the Design of a Friction Grip......Page 200
9.2.1.5 Transforming Moments into a Self‐Reinforcing Response......Page 202
9.2.1.6 Self‐Reinforcement by Self‐Balancing......Page 203
9.2.1.7 Self‐Reinforcement by Self‐Anchoring......Page 204
9.2.3 Self‐Reinforcement by Self‐Alignment......Page 206
9.2.3.1 Case Study: Self‐Reinforcement by Self‐Alignment of a Rectangular Panel Under Wind Pressure......Page 207
9.3.1 Self‐Reinforcement by Creating Negative Feedback Loops......Page 208
9.3.2 Positive Feedback Loops......Page 209
9.3.3 Reducing Risk by Eliminating or Inhibiting Positive Feedback Loops with Negative Impact......Page 210
9.3.3.1 Case Study: Growth of Damage Sustained by a Positive Feedback Loop with Negative Impact......Page 212
9.3.4 Self‐Reinforcement by Creating Positive Feedback Loops with Positive Impact......Page 214
9.3.4.1 Case Study: Positive Feedback Loop Providing Self‐Reinforcement by Self‐Energising......Page 215
10.1.1 Classification of Failures Caused by Accumulation of Damage......Page 217
10.1.2 Minimising the Rate of Damage Accumulation by Optimal Replacement......Page 218
10.1.3.1 A Case Related to a Single Damage‐Inducing Factor......Page 223
10.1.3.2 A Case Related to Multiple Damage‐Inducing Factors......Page 226
10.1.3.3 Reducing the Rate of Damage Accumulation by Derating......Page 229
10.1.4 Reducing the Rate of Damage Accumulation by Deliberate Weaknesses......Page 230
10.1.5.2 Reducing Exposure to Acceleration Stresses by Modifying or Replacing the Working Environment......Page 231
10.1.6 Reducing the Rate of Damage Accumulation by Appropriate Materials Selection, Design, and Manufacturing......Page 232
10.2 Improving Reliability and Reducing Risk by Substitution with Assemblies Working on Different Physical Principles......Page 233
10.2.2 Increasing Reliability by a Substitution with Electrical Systems......Page 235
10.2.3 Increasing Reliability by a Substitution with Optical Assemblies......Page 236
10.2.4 Increasing Reliability and Reducing Risk by a Substitution with Software......Page 237
11.1 A Comparative Method for Improving System Reliability......Page 239
11.1.1 Comparative Method for Improving System Reliability Based on Proving an Inequality......Page 240
11.1.2 The Method of Biased Coins for Proving System Reliability Inequalities......Page 241
11.1.2.1 Case Study: Comparative Method for Improving System Reliability by the Method of Biased Coins......Page 243
11.1.3 A Comparative Method Based on Computer Simulation for Production Networks......Page 245
11.2 Improving Reliability and Reducing Risk by Permutations of Interchangeable Components and Processes......Page 246
11.3 Improving Reliability and Availability by Appropriate Placement of the Condition Monitoring Equipment......Page 249
11.4.1 Reducing the Time of Exposure......Page 251
11.4.2.1 Case Study: Reducing the Risk of Failure of Wires by Simultaneously Reducing the Cost......Page 252
11.4.2.2 Case Study: Evaluating the Risk of Failure of Components with Complex Shape......Page 253
12.1 Uncertainty Associated with Properties from Multiple Sources......Page 255
12.2 Quantifying Uncertainty in the Case of Known Mixing Proportions......Page 257
12.2.1.1 Case Study: Estimating the Uncertainty in Setting Positioning Distance......Page 259
12.3.1 Variance Upper Bound Theorem......Page 262
12.3.2 An Algorithm for Determining the Exact Upper Bound of the Variance of Properties from Multiple Sources......Page 263
12.3.3 Determining the Source Whose Removal Results in the Largest Decrease of the Exact Variance Upper Bound......Page 264
12.4.1 Using the Variance Upper Bound for Increasing the Robustness of Products and Processes......Page 265
12.4.2.1 Case Study: Calculating the Worst‐Case Variation by the Variance Upper Bound Theorem......Page 266
12.4.3.1 Case Study: Identifying the Distributions Associated with the Worst‐Case Variation During Virtual Testing......Page 267
12.5 Using Standard Inequalities to Obtain a Tight Upper Bound for the Uncertainty in Mechanical Properties......Page 268
References......Page 271
Index......Page 281