Book description
This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working on the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, which have seen extended research activates in the recent years.
Table of contents
- Cover Page
- Half Title Page
- Title Page
- Copyright Page
- Contents
- Preface
- Editors
- Contributors
- Chapter 1 Reliability Analysis of a Pseudo Working Markov Repairable System
- Chapter 2 System Reliability Assessment with Multivariate Dependence Models
- Chapter 3 Reliability Modelling of Multi-Phased Linear Consecutively Connected Systems
- Chapter 4 A Method for Complex Multi-State Systems Reliability Analysis Based on Compression Inference Algorithm and Bayesian Network
- Chapter 5 Reliability Analysis of Demand-Based Warm Standby System with Multi-State Common Bus
- Chapter 6 An Upside-Down Bathtub-Shaped Failure Rate Model Using a DUS Transformation of Lomax Distribution
- Chapter 7 Reliability Analysis of Multi-State Systems with Dependent Failures Based on Copula
- Chapter 8 Modelling and Inference for Special Types of Semi-Markov Processes
- Chapter 9 Weighted Multi-Attribute Acceptance Sampling Plans
- Chapter 10 Reliability Assessment for Systems Suffering Common Cause Failure Based on Bayesian Networks and Proportional Hazards Model
- Chapter 11 Early Warning Strategy of Sparse Failures for Highly Reliable Products Based on the Bayesian Method
- Chapter 12 Fault Detection and Prognostics of Aero Engine by Sensor Data Analytics
- Chapter 13 Stochastic Modelling of Opportunistic Maintenance for Series Systems with Degrading Components
- Chapter 14 On Censored and Truncated Data in Survival Analysis and Reliability Models
- Chapter 15 Analysis of Node Resilience Measures for Network Systems
- Chapter 16 Reliability Analysis of General Purpose Parts for Special Vehicles Based on Durability Testing Technology
- Chapter 17 State of Health Prognostics of Lithium-Ion Batteries
- Chapter 18 Life Prediction of Device Based on Material’s Micro-Structure Evolution by Means of Computational Materials Science
- Chapter 19 Low-Cycle Fatigue Damage Assessment of Turbine Blades Using a Substructure-Based Reliability Approach
- Chapter 20 Phased-Mission Modelling of Physical Layer Reliability for Smart Homes
- Chapter 21 Comparative Reliability Analysis of Different Traction Drive Topologies for a Search-and-Rescue Helicopter
- Chapter 22 Reliability and Fault Tolerance Assessment of Different Operation Modes of Air Conditioning Systems for Chemical Laboratories
- Chapter 23 Dependability Analysis of Ship Propulsion Systems
- Chapter 24 Application of Markov Reward Processes to Reliability, Safety, Performance Analysis of Multi-State Systems with Internal and External Testing
- Chapter 25 Multi-Objective Maintenance Optimization of Complex Systems Based on Redundancy-Cost Importance
- Chapter 26 Which Replacement Maintenance Policy Is Better for Multi-State Systems?: Policy T or Policy N?
- Chapter 27 Design of Multi-Stress Accelerated Life Testing Plans Based on D-Optimal Experimental Design
- Chapter 28 An Extended Optimal Replacement Policy for a Simple Repairable Modelling
- Index
Product information
- Title: Stochastic Models in Reliability Engineering
- Author(s):
- Release date: July 2020
- Publisher(s): CRC Press
- ISBN: 9781000094619
You might also like
book
Reliability Engineering and Services
Offers a holistic approach to guiding product design, manufacturing, and after-sales support as the manufacturing industry …
book
Fuzzy Neural Networks for Real Time Control Applications
AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS …
article
Run Llama-2 Models Locally with llama.cpp
Llama is Meta’s answer to the growing demand for LLMs. Unlike its well-known technological relative, ChatGPT, …
book
Machine Learning and Data Science in the Oil and Gas Industry
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can …