Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis.This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases.Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system.

Over the last several decades, computer simulations have been widely utilized to model and analyze complex systems at low costs and risks. Although simulation can represent physical systems realistically, it is a descriptive tool without the capability of suggesting better solutions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This book, therefore, provides a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. We have used this novel simulation-optimization technique to study several disaster response problems. The methodologies provided herein should be useful to professionals and researchers in the fields of industrial engineering and operations research. The applications in disaster response management should help emergency managers and personnel to gain insights into several significant response problems.