Cover
Dedication
Series Editor's Foreword
Preface
Acknowledgement
About the Companion Website
1 Basic Reliability Concepts and Models
1. 1.1 Introduction
2. 1.2 Reliability Definition and Hazard Rate
3. 1.3 Mean Lifetime and Mean Residual Life
4. 1.4 System Downtime and Availability
5. 1.5 Discrete Random Variable for Reliability Modeling
6. 1.6 Continuous Random Variable for Reliability Modeling
7. 1.7 Bayesian Reliability Model
8. 1.8 Markov Model and Poisson Process
9. References
10. Problems
2 Reliability Estimation with Uncertainty
1. 2.1 Introduction
2. 2.2 Reliability Block Diagram
3. 2.3 Series Systems
4. 2.4 Parallel Systems
5. 2.5 Mixed Series and Parallel Systems
6. 2.6 Systems with k‐out‐of‐n:G Redundancy
7. 2.7 Network Systems
8. 2.8 Reliability Confidence Intervals
9. 2.9 Reliability of Multistate Systems
10. 2.10 Reliability Importance
11. References
12. Problems
3 Design and Optimization for Reliability
1. 3.1 Introduction
2. 3.2 Lifecycle Reliability Optimization
3. 3.3 Reliability and Redundancy Allocation
4. 3.4 Multiobjective Reliability–Redundancy Allocation
5. 3.5 Failure‐in‐Time Based Design
6. 3.6 Failure Rate Considering Uncertainty
7. 3.7 Fault‐Tree Method
8. 3.8 Failure Mode, Effect, and Criticality Analysis
9. 3.9 Case Study: Reliability Design for Six Sigma
10. References
11. Problems
4 Reliability Growth Planning
1. 4.1 Introduction
2. 4.2 Classification of Failures
3. 4.3 Failure Mode Types
4. 4.4 No Fault Found (NFF) Failures
5. 4.5 Corrective Action Effectiveness
6. 4.6 Reliability Growth Model
7. 4.7 Reliability Growth and Demonstration Test
8. 4.8 Lifecycle Reliability Growth Planning
9. 4.9 Case Study
10. References
11. Problems
5 Accelerated Stress Testing and Economics
1. 5.1 Introduction
2. 5.2 Design of Accelerated Stress Test
3. 5.3 Scale Acceleration Model and Usage Rate
4. 5.4 Arrhenius Model
5. 5.5 Eyring Model and Power Law Model
6. 5.6 Semiparametric Acceleration Models
7. 5.7 Highly Accelerated Stress Screening Testing
8. 5.8 A Case Study for HASS Project
9. References
10. Problems
6 Renewal Theory and Superimposed Renewal
1. 6.1 Introduction
2. 6.2 Renewal Integral Equation
3. 6.3 Exponential and Erlang Renewal
4. 6.4 Generalized Exponential Renewal
5. 6.5 Weibull Renewal with Decreasing Failure Rate
6. 6.6 Weibull Renewal with Increasing Failure Rate
7. 6.7 Renewal under Deterministic Fleet Expansion
8. 6.8 Renewal under Stochastic Fleet Expansion
9. 6.9 Case Study
10. References
11. Problems
7 Performance‐Based Maintenance
1. 7.1 Introduction
2. 7.2 Corrective Maintenance
3. 7.3 Preventive Maintenance
4. 7.4 Condition‐Based Maintenance
5. 7.5 Inverse Gaussian Degradation Process
6. 7.6 Non‐Stationary Gaussian Degradation Process
7. 7.7 Performance‐Based Maintenance
8. 7.8 Contracting for Performance‐Based Logistics
9. 7.9 Case Study – RUL Prediction of Electronics Equipment
10. Finding the Optimal “Tao” under Age-Based PM
11. References
12. Problems
8 Warranty Models and Services
1. 8.1 Introduction
2. 8.2 Warranty Concept and Its Roles
3. 8.3 Warranty Policy for Non‐repairable Product
4. 8.4 Warranty Models for Repairable Products
5. 8.5 Warranty Service for Variable Installed Base
6. 8.6 Warranty Service under Reliability Growth
7. 8.7 Other Warranty Services
8. 8.8 Case Study: Design for Warranty
9. References
10. Problems
9 Basic Spare Parts Inventory Models
1. 9.1 Introduction
2. 9.2 Overview of Inventory Model
3. 9.3 Deterministic EOQ Model
4. 9.4 The Newsvendor Model
5. 9.5 The (q, r) Inventory System under Continuous Review
6. 9.6 The (s, S, T) Policy under Periodic Review
7. 9.7 Basic Supply Chain Systems
8. 9.8 Spare Parts Demand Forecasting
9. References
10. Problems
10 Repairable Inventory System
1. 10.1 Introduction
2. 10.2 Characteristics of Repairable Inventory Systems
3. 10.3 Single‐Echelon Inventory with Uncapacitated Repair
4. 10.4 Single‐Echelon Inventory with Capacitated Repair
5. 10.5 Repairable Inventory for a Finite Fleet Size
6. 10.6 Single‐Echelon Inventory with Emergency Repair
7. 10.7 Repairable Inventory Planning under Fleet Expansion
8. 10.8 Multi‐echelon, Multi‐item Repairable Inventory
9. 10.9 Case Study: Teradyne's Spare Parts Supply Chain
10. References
11. Problems
11 Reliability and Service Integration
1. 11.1 Introduction
2. 11.2 The Rise of Product‐Service System
3. 11.3 Allocation of Reliability and Inventory for a Static Fleet
4. 11.4 Allocation of Reliability and Inventory under Fleet Expansion
5. 11.5 Joint Allocation of Maintenance, Inventory, and Repair
6. 11.6 Case Study: Supporting Wind Generation Using PBC
7. Matlab Codes for Casey Study with Linear Reward
8. References
9. Problems
12 Resilience Engineering and Management
1. 12.1 Introduction
2. 12.2 Resilience Concept and Measures
3. 12.3 Disaster Resilience Models of Power Grid
4. 12.4 Prevention, Survivability, and Recovery
5. 12.5 Variable Generation System Model
6. 12.6 Case Study: Design for Resilient Distribution Systems
7. References
8. Problems
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Failure arrival times and ROCOF of two systems.
2. Table 1.2 Median ranks of the life data.
3. Table 1.3 Notation of queuing system.
Chapter 2
1. Table 2.1 Sample size and survivals att = 100 hours.
2. Table 2.2 Mean and variance of component reliability estimates.
3. Table 2.3 Component test data, reliability, and variance att = 100 and 200 hours...
4. Table 2.4 Mean and variance of reliability estimates for subsystems and system.
5. Table 2.5 Component reliability.
6. Table 2.6 Reliability of minimum cut sets.
7. Table 2.7 Reliability of minimum path sets.
8. Table 2.8 Component failure data.
9. Table 2.9 Component lifetime testing data.
10. Table 2.10 Reported diode failures in each quarter in 2014.
11. Table 2.11 Reliability of multistate components.
12. Table 2.12 Reliability of four‐state components.
Chapter 3
1. Table 3.1 Baseline design cost and reliability–cost parameters.
2. Table 3.2 Baseline design cost and reliability–cost parameters.
3. Table 3.3 Component reliability and cost.
4. Table 3.4 Component reliability, cost, and configuration of ATE.
5. Table 3.5 Component reliability, cost, and weight.
6. Table 3.6 Pareto optimal solutions for independent component estimates.
7. Table 3.7 Empirical and standard based prediction methods (ReliaSoft 2017).
8. Table 3.8 Nominal FIT and reliability factors of inverter components.
9. Table 3.9 Parametric value ofm for certain electronic devices.
10. Table 3.10 Failure mode types and ratios of resistor and replays.
11. Table 3.11 Data for estimating failure rates of new components.
12. Table 3.12 Data for failure rate of existing components.
13. Table 3.13 Triangular distribution parameters for non‐component failure rates.
14. Table 3.14 Mean and variance of failure rates for the new PCB assembly.
Chapter 4
1. Table 4.1 The first occurrence time of different failure modes.
2. Table 4.2 Failure rates before and post CA.
3. Table 4.3 Reliability growth rate under different CA effectiveness.
4. Table 4.4 Trend test methods.
5. Table 4.5 Time‐to‐failure data.
6. Table 4.6 Notation of RGT model.
7. Table 4.7 Notation for latent failure prediction model.
8. Table 4.8 The format of for a surfaced failure mode in Eq. 4.8.5.
9. Table 4.9 Input parameters to the engine RGP.
10. Table 4.10 Optimal testing units and time.
Chapter 5
1. Table 5.1 Summary of HALT, HASS, ESS, and other screening techniques.
2. Table 5.2 Classification of stress types and their factors.
3. Table 5.3 The experimental outcome of 10 LED devices.
4. Table 5.4 Failure intensity of HASSed and non‐HASSed products.
5. Table 5.5 Potential cost savings of reducing infant mortality rates (IMR) (unit:...
Chapter 6
1. Table 6.1 Optimal solutions for Weibull reliability withα= 1.
2. Table 6.2 Values ofT for various α and β based on g _a(t ₀) = 0.1B.
3. Table 6.3 Approximations versus actualg(t) for α = 1 and 1< β≤ 1.5.
4. Table 6.4 Values ofT for g(t) for various α and β∈ (1.5, 10].
5. Table 6.5 Comparison betweenM(t) and M _a(t) for different β with α= 1.
6. Table 6.6 Erlang renewals of individual systems and fleet in a period oft = 183 ...
7. Table 6.7 Reliability of subsystems of wind turbines (n/a = not applicable).
8. Table 6.8 Aggregate fleet renewals of components.
Chapter 7
1. Table 7.1 Fleet operating hours and expected amount of spare inverters from year...
2. Table 7.2 The mean and variance of the voltage degradation in each month.
3. Table 7.3 Statistics for mean hypothesis testing.
4. Table 7.4 Statistics for variance hypothesis testing.
5. Table 7.5 A four‐step process of performance‐based maintenance.
6. Table 7.6 Summary of five overarching performance measures.
7. Table 7.7 Selected results of hypothesis tests and RUL (n/a = not applicable).
Chapter 8
1. Table 8.1 Different customers during a new production introduction.
2. Table 8.2 Parameters for component choices.
3. Table 8.3 Parameters for non‐component failure rate γ.
4. Table 8.4 Optimal design of the digital instrument.
Chapter 9
1. Table 9.1 Parameters and features of inventory system.
2. Table 9.2 Cumulative distribution of Poisson process.
Chapter 10
1. Table 10.1 Classification of repairable inventory models.
2. Table 10.2 Probability mass function and cumulative distribution.
3. Table 10.3 Data for installed base and operating hours in a particular week.
4. Table 10.4 The variation of failure mode percentage from first to sixth month.
Chapter 11
1. Table 11.1 Notation of reliability‐inventory allocation model.
2. Table 11.2 Notation for reliability‐inventory allocation under fleet expansion.
3. Table 11.3 Notation for allocation of maintenance, spares, and repair capacity.
4. Table 11.4 Decomposition of a wind turbine system.
5. Table 11.5 Reliability and cost data.
6. Table 11.6 Performance outcome under a 5‐year contract.
7. Table 11.7 Performance outcome under a 10‐year contract.
Chapter 12
1. Table 12.1 Comparisons between grid reliability and resilience.
2. Table 12.2 Grid resilience measures broken down by stakeholders.
3. Table 12.3 Saffir–Simpson scale in Xu and Brown (2008) (N/A = not applicable).
4. Table 12.4 Underground line failure probability undera _u = 0.02 and b _u = 0.05.
5. Table 12.5 Mean and variance of load in years 1 and 3 (unit: MW).
6. Table 12.6 Power capacity and costs for DER units (SS = Substation).
7. Table 12.7 Cumulative Allocated DER units by the end of Year 3.
8. Table 12.8 The amount of protected power of each node in Year 3.
9. Table 12.9 Survivability in Years 1 to 3 with lost substation.

List of Illustrations

Chapter 1
1. Figure 1.1 The role of reliability in the lifecycle of a product.
2. Figure 1.2 Bathtub hazard rate curve.
3. Figure 1.3 Reliability and cumulative hazard rate.
4. Figure 1.4 ROCOF of systems A and B.
5. Figure 1.5 Hazard rate function of main bearing.
6. Figure 1.6 Cumulative hazard rate function of main bearing.
7. Figure 1.7Figure 1.7 Reliability function of main bearing.
8. Figure 1.8Figure 1.8 Probability density function of main bearing.
9. Figure 1.9 Replacement scenarios for a repairable system.
10. Figure 1.10 The MTTR of replacing an aircraft engine.
11. Figure 1.11 The probability mass function for software bugs.
12. Figure 1.12 The hazard rate of the Weibull function.
13. Figure 1.13 Weibull probability density function.
14. Figure 1.14Figure 1.14 Weibull reliability function.
15. Figure 1.15 Normal PDF under different means and variances.
16. Figure 1.16 Lognormal PDF plots.
17. Figure 1.17 (a) Normal probability plot and (b) lognormal probability plot.
18. Figure 1.18 Gamma PDF plots.
19. Figure 1.19 Bayesian reliability inference process.
20. Figure 1.20 Transition diagram for the birth–death queuing system.
Chapter 2
1. Figure 2.1 Reliability block diagrams of basic system structures.
2. Figure 2.2 Series reliability block diagram of the electric power system.
3. Figure 2.3 Reliability block diagram of the power system at the structure level.
4. Figure 2.4 A series system comprised of m components.
5. Figure 2.5 Distributions of component reliability estimates.
6. Figure 2.6 Reliability diagram for a parallel system.
7. Figure 2.7 A series–parallel system.
8. Figure 2.8 A parallel–series system.
9. Figure 2.9 A mixed series–parallel system with seven components.
10. Figure 2.10 System structure after the decomposition of subsystems 1 and 2.
11. Figure 2.11 System structure after the decomposition of component 4 and subsyste...
12. Figure 2.12 System structure after the decomposition of component 7 and subsyste...
13. Figure 2.13 A RAID‐5 system and its reliability block diagram.
14. Figure 2.14 Reliability for RAID‐5 and RAID‐6 from years 1 to 100.
15. Figure 2.15 A simple bridge network with five components.
16. Figure 2.16 A series–parallel system conditioned on a function of component 3.
17. Figure 2.17 A mixed series and parallel system conditioned on the failure of com...
18. Figure 2.18 The network with its equivalent series–parallel system.
19. Figure 2.19 Network with its equivalent parallel–series system.
20. Figure 2.20 Network under different status of components 3 and 6.
21. Figure 2.21 Compare with the exact network reliability.
22. Figure 2.22 Time‐dependent reliability importance.
23. Figure 2.23 A series–parallel system with three components.
24. Figure 2.24 (a) Series–parallel system, (b) parallel–series system.
25. Figure 2.25 A mixed series–parallel system.
26. Figure 2.26 (a) A series–parallel system, (b) a mixed series–parallel system.
27. Figure 2.27 (a) Parallel system; (b) a series–parallel system; (c) parallel–ser...
Chapter 3
1. Figure 3.1 Reliability–design cost under different r _min with r _max = 1, k = 0.3 ...
2. Figure 3.2 Reliability–design cost under various k with r _min = 0.7, r _max = 1 and...
3. Figure 3.3 Reliability–manufacturing cost with α _min = 0.005 and α _max =...
4. Figure 3.4 Sum of the design, manufacturing, and warranty costs.
5. Figure 3.5 The optimal reliability of the product in three cases.
6. Figure 3.6 Two design options for a series–parallel system.
7. Figure 3.7 A series system consists of three subsystems.
8. Figure 3.8 The optimal redundancy level for each subsystem.
9. Figure 3.9 k‐out‐of‐n subsystem redundancy allocation in ATE.
10. Figure 3.10 Pareto frontier and Pareto solutions.
11. Figure 3.11 The principle of crossover operation.
12. Figure 3.12 The principle of mutation.
13. Figure 3.13 Pareto optimal solution = 0.9844, = 0.000438.
14. Figure 3.14 Pareto optimal solution = 0.9842, = 0.00038.
15. Figure 3.15Figure 3.15 Pareto optimal solution = 0.9834, = 0.000353.
16. Figure 3.16 Ambient temperature versus the temperature factor.
17. Figure 3.17 Triangular distribution for failure rate λ.
18. Figure 3.18 Temperature distribution of ASIC units in a PCB module.
19. Figure 3.19 Distribution of voltage derating of capacitors.
20. Figure 3.20 A functional block diagram with input, process, and output parts.
21. Figure 3.21 Schematic diagram of a PV system with battery storage.
22. Figure 3.22 Functional block diagram of a roof‐top PV system.
23. Figure 3.23 Standard logic symbols used in fault‐tree analysis.
24. Figure 3.24 FTA for the roof‐top PV system.
25. Figure 3.25 A typical FMECA spreadsheet.
Chapter 4
1. Figure 4.1 Classification of field failures.
2. Figure 4.2 PCB failures breakdown by root‐cause category.
3. Figure 4.3 Actual MTBF versus predictions.
4. Figure 4.4 System MTBF with and without latent failures.
5. Figure 4.5 Implementation of ECO and retrofit post installation.
6. Figure 4.6 CA effectiveness under various b.
7. Figure 4.7 The impact of CA effectiveness on failure rate.
8. Figure 4.8 Crow/AMSAA failure intensity function.
9. Figure 4.9 Failure intensity function with different α and β.
10. Figure 4.10 A holistic approach to reliability growth planning.
11. Figure 4.11 Surfaced and latent failure intensity function.
12. Figure 4.12 Failure mode Pareto chart (C/A = corrective action).
Chapter 5
1. Figure 5.1 ALT and HASS and product design margins.
2. Figure 5.2 Constant stress level.
3. Figure 5.3 Sinusoid stress.
4. Figure 5.4 Step stress (or stair stress).
5. Figure 5.5 Dwell and ramp‐up stress.
6. Figure 5.6 Zigzag stress profile.
7. Figure 5.7 Cyclic stress profile.
8. Figure 5.8 Reliability with different A _f values.
9. Figure 5.9 Probability density with different A _f values.
10. Figure 5.10 A typical cyclic load profile for mechanical systems.
11. Figure 5.11 S–N curve.
12. Figure 5.12 Time‐to‐failure with different A and E _a.
13. Figure 5.13 Acceleration factor increases with temperature.
14. Figure 5.14 Plot of inverse power law model in log base 10.
15. Figure 5.15 HASS versus non‐HASS product flow.
16. Figure 5.16 Process map from subcontractor, in‐house testing to field installati...
17. Figure 5.17 DMAIC in closed‐loop Six‐Sigma reliability growth (Jin et al. 2011).
18. Figure 5.18 Formation of a cross‐functional team.
19. Figure 5.19 PCB infant mortality distribution.
20. Figure 5.20 Tracking the infant mortality rate.
Chapter 6
1. Figure 6.1 Computational methods of renewal functions.
2. Figure 6.2 Failure sequences of a renewal process.
3. Figure 6.3 PDF for generalized exponential distributions.
4. Figure 6.4 Hazard rate of generalized exponential function.
5. Figure 6.5 Weibull reliability approximation under different shape parameters.
6. Figure 6.6 Weibull renewal and it approximation for 0 < β < 1 and α = ...
7. Figure 6.7 Weibull renewal data with different β > 1.
8. Figure 6.8 Weibull transient renewals.
9. Figure 6.9 Estimation T for Weibull 0 < β ≤ 1.5.
10. Figure 6.10 Characteristic terms of transient renewal.
11. Figure 6.11 Approximations versus actual for β = 3.
12. Figure 6.12 Approximations versus actual for β = 5.
13. Figure 6.13 Approximation versus actual for β = 8.
14. Figure 6.14 Installation and spare parts demand in product lifecycle.
15. Figure 6.15 Spare part demand under a single‐item system fleet.
16. Figure 6.16 Erlang hazard rate function with k = 2.
17. Figure 6.17 Mean and variance of Z(t) under different α and λ.
18. Figure 6.18 Current WT fleet size and installed capacity.
19. Figure 6.19 Reliability block diagram for the wind turbine.
Chapter 7
1. Figure 7.1 The evolution of maintenance technologies.
2. Figure 7.2 Block replacement policy.
3. Figure 7.3 Cost rate of block replacement policy.
4. Figure 7.4 Age‐based preventive replacement.
5. Figure 7.5 The optimal maintenance intervals for three cases.
6. Figure 7.6 Degradations under different trajectory or paths.
7. Figure 7.7 Hazard rate of aircraft engines at different covariate levels.
8. Figure 7.8 Probability density function of inverse Gaussian distribution.
9. Figure 7.9 System degradation with increasing μ(t) and σ(t).
10. Figure 7.10 The expected trajectory path of voltage degradation.
11. Figure 7.11 Two normal samples at different time epochs.
12. Figure 7.12 The evolution of asset management strategy.
13. Figure 7.13 An integrated product‐service system.
14. Figure 7.14 Weibull reliability (α = 1, β = 3) under different utiliza...
15. Figure 7.15 The core performance measure in PBM.
16. Figure 7.16 Integrated product‐service under PM policy for single‐item systems.
17. Figure 7.17 Chronological readings of voltage test.
18. Figure 7.18 Normality test of initial voltage data.
19. Figure 7.19 (a) Mean voltage data and (b) variance of voltage data.
Chapter 8
1. Figure 8.1 Linear and exponential pro‐rata rebate functions.
2. Figure 8.2 Plot the relation with λw and r/c.
3. Figure 8.3 Fixed rebate versus pro‐rata rebate.
4. Figure 8.4 Plot the relation between ρ and λw.
5. Figure 8.5 Mixed rebate function.
6. Figure 8.6 Warranty and post‐warranty replacement.
7. Figure 8.7 Warranty planning for variable sales.
8. Figure 8.8 Unclaimed failures in warranty and post‐warranty periods.
9. Figure 8.9 Repair and spare parts supply during the warranty period.
10. Figure 8.10 Extended warranty service models: (a) through retailer; (b) from man...
11. Figure 8.11 Illustration of warranty demand at different risk levels.
12. Figure 8.12 A 2‐D warranty period for a non‐renewing warranty of Type I usage.
13. Figure 8.13 A 2‐D warranty period for a non‐renewing warranty of Type II usage.
Chapter 9
1. Figure 9.1 Various types of demand patterns.
2. Figure 9.2 EOQ inventory control with no backorders.
3. Figure 9.3 An inventory system with backlogs.
4. Figure 9.4 The cycle times of the laptop inventory system.
5. Figure 9.5 Operation of (q, r) inventory under continuous review.
6. Figure 9.6 The relation between reorder point and safety stock.
7. Figure 9.7 Lead time distribution: (a) single mode; (b) dual mode.
8. Figure 9.8 The relation of base stock inventory parameters.
9. Figure 9.9 Using inventory position to control of the (s, S, T) system.
10. Figure 9.10 The uncertain lead time versus the demand variation.
11. Figure 9.11 A four‐stage series system.
12. Figure 9.12 Example of an assembly system.
13. Figure 9.13 Example of a distribution system.
14. Figure 9.14 Example of a mixed supply chain system.
15. Figure 9.15 Echelons in a series system.
16. Figure 9.16 Echelons in a three‐stage distribution system.
17. Figure 9.17 A simple distribution system to model the bullwhip effect.
18. Figure 9.18 Simulated demands of products A, B, and C.
19. Figure 9.19 Spare parts demand of aircraft parts.
Chapter 10
1. Figure 10.1 A multi‐echelon spare parts distribution network.
2. Figure 10.2 A repairable inventory system with ample capacity.
3. Figure 10.3 M/G/∞ queue for a repair shop.
4. Figure 10.4 The breakdown of repair turn‐around time.
5. Figure 10.5 A repairable inventory system with limited capacity.
6. Figure 10.6 M/M/m queue for limited repair capacity.
7. Figure 10.7 The breakdown of TAT in a capacitated repair shop.
8. Figure 10.8 The machine‐inventory model with a limited fleet size.
9. Figure 10.9 The transition diagram of the machine‐inventory problem.
10. Figure 10.10 A repairable inventory system with emergency repair.
11. Figure 10.11 Flowchart of the repairable inventory with emergency repair.
12. Figure 10.12 Markov transition diagram for M/M/s/s queue.
13. Figure 10.13 Markov transition diagram for M/G/m/∞ queue.
14. Figure 10.14 Repairable inventory system under fleet expansion.
15. Figure 10.15 A three‐interval (Q, r) inventory policy.
16. Figure 10.16 The repairable inventory system in the METRIC model.
17. Figure 10.17 Probability mass functions of a logarithmic distribution.
18. Figure 10.18 Distributed design, manufacturing, and support of ATE machines.
19. Figure 10.19 (a) Installed base of the system; (b) installed base of a PCB item.
20. Figure 10.20 (a) MTBF run chart of the system; (b) MTBF run chart of PCB1.
21. Figure 10.21 (a) Pareto chart of corrective actions; (b) Pareto chart of custome...
22. Figure 10.22 (a) Weekly demand for PCB1; (b) cumulative demand for PCB1.
23. Figure 10.23 (a) Repair time versus failure modes; (b) reliability growth rate.
Chapter 11
1. Figure 11.1 Cost distribution across a product lifecycle.
2. Figure 11.2 Technological factors for product‐service integration.
3. Figure 11.3 Product reliability versus design cost.
4. Figure 11.4 Product manufacturing cost vs. reliability.
5. Figure 11.5 Trade‐off between design, manufacturing, and support costs.
6. Figure 11.6 (a) Linear reward function. (b) Exponential reward function.
7. Figure 11.7 A multi‐resolution (q, r) control policy.
8. Figure 11.8 A repairable inventory supporting preventive maintenance.
9. Figure 11.9 Planned and failure replacements in age‐based maintenance.
10. Figure 11.10 Superimposed renewal processes.
Chapter 12
1. Figure 12.1 Behavior of systems with different resilience capacities.
2. Figure 12.2 Variations of resilience curves.
3. Figure 12.3 Performance loss under a disruptive event.
4. Figure 12.4 Recoverability dimension of resilience performance.
5. Figure 12.5 Six dimensions of the system resilience.
6. Figure 12.6 SAIDI between 2005 and 2012 for North American utilities.
7. Figure 12.7 SAIFI between 2005 and 2012 for North American utilities.
8. Figure 12.8 CAIDI between 2005 and 2012 for North American utilities.
9. Figure 12.9 Poisson and negative binomial distributions fit for hurricanes.
10. Figure 12.10 Hurricane approach angle.
11. Figure 12.11 Wind speed decays over time.
12. Figure 12.12 Pressure filling rate under different parameters.
13. Figure 12.13 Onsite DG for industrial users.
14. Figure 12.14 (a) Passive distribution network; (b) active distribution network.
15. Figure 12.15 Transition diagram of repairing distribution lines.
16. Figure 12.16 A typical wind turbine power curve.
17. Figure 12.17 Annual solar irradiance distributions.
18. Figure 12.18 Recovery time and disconnected branches with λ = 1, R = 2.
19. Figure 12.19 Recovery time and disconnected branches with λ = 0.5, R = 2.

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Library of Congress Cataloging‐in‐Publication Data

Names: Jin, Tongdan, author.

Title: Reliability engineering and services / Dr. Tongdan Jin, Professor,

Texas State University, USA.

Description: Hoboken, NJ, USA : John Wiley & Sons, Inc., [2019] | Includes

bibliographical references and index. |

Identifiers: LCCN 2018031844 (print) | LCCN 2018032734 (ebook) | ISBN

9781119167037 (Adobe PDF) | ISBN 9781119167044 (ePub) | ISBN 9781119167013

(hardcover)

Subjects: LCSH: Reliability (Engineering)

Classification: LCC TS173 (ebook) | LCC TS173 .J56 2018 (print) | DDC

620/.00452–dc23

LC record available at https://lccn.loc.gov/2018031844

Cover Design: Wiley

Preface

Reliability engineering is a multidisciplinary study that deals with the lifecycle management of a product or system, ranging from design, manufacturing, and installation to maintenance and repair services. Reliability plays a key role in ensuring human safety, cost‐effectiveness, and resilient operation of infrastructures and systems. It has been widely accepted as a critical performance measure in both private and public sectors, including manufacturing, healthcare, transportation, energy, chemical and oil refinery, aviation, aerospace, and defense industries. For instance, commercial airplane engines can fly over 5000 hours before the need for overhaul and maintenance. This means that the plane can cross the Pacific Ocean nearly 500 times without failure. In road transportation, China has constructed a total of 16 000 km of high‐speed rail since 2008 and the annual ridership is three billion. The service reliability reaches 0.999 999 998 given the annual fatality of five passengers on average. The F‐35 is the next generation of jet fighters for the US Air Force. It is anticipated that 2000 aircraft will be deployed in the next 50 years. The design and manufacturing of these aircraft will cost $350 billion, yet the maintenance and support of the fleet is expected to be $600 billion. These examples indicate the success in deploying and operating a new product is highly dependent upon the reliability, maintenance, and repair services during its use.

This book aims to offer a holistic reliability approach to product design, testing, maintenance, spares provisioning, and resilience operations. Particularly, we present an integrated product‐service system with which the design for reliability, performance‐based maintenance, and spare parts logistics are synthesized to maximize the reliability while lowering the cost. Such a lifecycle approach is imperative as the industry is transitioning from a product‐oriented model to a service‐centric paradigm. We report the fundamental knowledge and best industry practices in reliability modeling, maintenance planning, spare parts logistics, and resilience planning across a variety of engineering domains. To that end, the book is classified into four topics: (1) design for reliability; (2) maintenance and warranty planning; (3) product and service integration; and (4) engineering resilience modeling. Each topic is further illustrated below.

Chapters 1 to 5 are dedicated to the design for reliability. They cover a wide array of reliability modeling and design methods, including non‐parametric models, parametric models, reliability block diagrams, min‐cut, and min‐path network theory, importance measures, multistate systems, reliability and redundancy allocation, multicriteria optimization, fault‐tree analysis, failure mode effects and criticality analysis, latent failures, corrective action and effectiveness, multiphase reliability growth planning, power law model, and accelerated life testing.

Chapters 6 to 8 focus on maintenance and warranty planning that deals with the decision making on replacement and repair of field units. Technical subjects include renewal theory, superimposed renewal, corrective maintenance, preventive maintenance, condition‐based maintenance, performance‐based maintenance, health diagnostics and prognostics management, repairable system theory, no‐fault‐found issue, free‐replacement warranty, pro‐rata warranty, extended warranty services, and two‐dimensional warranty policy.

Chapters 9 to 11 model and design integrated product‐service offerring systems. First, basic inventory models are reviewed, including economic order quantity, continuous and period review policy with deterministic and stochastic lead time, respectively. Then the analyses are directed to repairable inventory systems that face stationary (or Poisson) demand or non‐stationary demand processes. Multiresolution and adaptive inventory replenishment policy are applied to cope with the time‐varying demand rate. Both single‐echelon and multi‐echelon inventory models are analyzed. Finally, an integrated production‐service system that jointly optimizes reliability, maintenance, spares inventory, and repair capacity are elaborated in the context of multiobjective, performance‐based contracting.

Chapter 12 introduces the basic concepts and modeling methods in resilience engineering. Unlike reliability issues, events considered in resilience management possess two unique features: high impact with low occurrence probability and catastrophic events with cascading failure. We present several resilience performance measures derived from the resilience curve and further discuss the difference between reliability and resilience. The chapter concludes by emphasizing that prevention, survivability, and recoverability are the three main aspects in resilience management.

This book represents a collection of the recent advancements in reliability theory and applications, and is a suitable reference for senior and graduate students, researcher scientists, reliability practitioners, and corporate managers. The case studies at the end of the chapters assist readers in finding reliability solutions that bridge the theory and applications. In addition, the book also benefits the readers in the following aspects: (1) guide engineers to design reliable products at a low cost; (2) assist the manufacturing industry in transitioning from a product‐oriented culture to a service‐centric organization; (3) support the implementation of a data‐driven reliability management system through real‐time or Internet‐based failure reporting, analysis, and corrective actions system; (4) achieve zero downtime equipment operation through condition‐based maintenance and adaptive spare parts inventory policy; and (5) realize low‐carbon and sustainable equipment operations by repairing and reusing failed parts.

In summary, reliability engineering is evolving rapidly as automation and artificial intelligence are becoming the backbone of Industry 4.0. New products and services will constantly be developed and adopted in the next 10 to 20 years, including autonomous driving, home robotics, delivery drones, unmanned aerial vehicles, electric cars, augmented virtual reality, smart grids, Internet of Things, cloud and mobile computing, and supersonic transportation, just to name a few. The introduction and deployment of these new technologies require the innovation in reliability design, modeling tools, maintenance strategy, and repair services in order to meet the changing requirements. Therefore, emerging technologies, such as big data analytics, machine learning, neural networks, renewable energy, additive and smart manufacturing, intelligent supply chain, and sustainable operations will lead the initiatives in new product introduction, manufacturing, and after-sales support.

Tongdan Jin

San Marcos, TX 78666, USA