Stochastic models in reliability engineering

Author(s)

    • Cui, Lirong
    • Frenkel, Ilia
    • Lisnianski, Anatoly

Bibliographic Information

Stochastic models in reliability engineering

edited by Lirong Cui, Ilia Frenkel, and Anatoly Lisnianski

(Advanced research in reliability and system assurance engineering / series editor Mangey Ram)

CRC Press, 2021

  • : hbk

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Note

Includes bibliographical references and index

Description and Table of Contents

Description

This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working at the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, all of which have seen extended research activities in recent years. The methods presented in this book are real-world examples that demonstrate improvements in essential reliability and availability for industrial equipment such as medical magnetic resonance imaging, power systems, traction drives for a search and rescue helicopter, and air conditioning systems. The book presents real case studies of redundant multi-state air conditioning systems for chemical laboratories and covers assessments of reliability and fault tolerance and availability calculations. Conventional and contemporary topics in reliability engineering are discussed, including degradation, networks, dynamic reliability, resilience, and multi-state systems, all of which are relatively new topics to the field. The book is aimed at engineers and scientists, as well as postgraduate students involved in reliability design, analysis, experiments, and applied probability and statistics.

Table of Contents

Reliability Analysis of A Pseudo Working Markov Repairable System. System Reliability Assessment with Multivariate Dependence Models. Reliability Modeling of Multi-Phased Linear Consecutively Connected Systems. A Method for Complex Multistate Systems Reliability Analysis Based on Compression Inference Algorithm and Bayesian Network. Reliability Analysis of Demand-Based Warm Standby System with Multi-State Common Bus. An Upside-Down Bathtub Shaped Failure Rate Model Using DUS Transformation of Lomax Distribution. Reliability Analysis of Multi-State Systems with Dependent Failures Based on Copula. Modelling and Inference for Special Types of Semi-Markov Processes. Weighted Multi-Attribute Acceptance Sampling Plans. Reliability Assessment for Systems Suffering Common Cause Failure Based on Bayesian Networks and Proportional Hazards Model. Early Warning Strategy of Sparse Failures for Highly Reliable Products Based on Bayesian Method. Fault Detection and Prognostics of Aero Engine by Sensor Data Analytics. Stochastic Modeling of Opportunistic Maintenance for Series Systems with Degrading Components. On Censored and Truncated Data in Survival Analysis and Reliability Models. Analysis of Node Resilience Measures for Network Systems. Reliability Analysis of General Purpose Parts for Special Vehicles Based on Durability Testing Technology. State of Health Prognostics of Lithium-Ion Batteries. Life Prediction of Device Based on Material's Microstructure Evolution by Means of Computational Materials Science. Low-Cycle Fatigue Damage Assessment of Turbine Blades Using A Substructure-Based Reliability Approach. Phased-Mission Modeling of Physical Layer Reliability for Smart Homes. Comparative Reliability Analysis of Different Traction Drive Topologies for A Search-and-Rescue Helicopter. Reliability and Fault Tolerance Assessment of Different Operation Modes of Air Conditioning Systems for Chemical Laboratories. Dependability Analysis of Ship Propulsion Systems. Application of Markov Reward Processes to Reliability, Safety, Performance Analysis of Multi-State Systems with Internal and External Testing. Multi-Objective Maintenance Optimization of Complex System Based on Redundant-Cost Importance. Which Replacement Maintenance Policy is Better for Multi-State Systems: Policy T or Policy N? Design of Multi-Stress Accelerated Life Testing Plans Based on D-Optimal Experimental Design. An Extended Optimal Replacement Policy for a Simple Repairable Modelling.

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