Bayesian Networks In Fault Diagnosis Practice And Application
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Bayesian Networks In Fault Diagnosis: Practice And Application
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.
Bayesian Networks
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
Advances in Artificial Intelligence
Author: Maria Carolina Monard
language: en
Publisher: Springer Science & Business Media
Release Date: 2000-10-25
This book constitutes the refereed joint proceedings of the 7th Ibero-American Conference on AI and the 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, held in Atibaia, Brazil in November 2000. The 48 revised full papers presented together with two invited contributions were carefully reviewed and selected from a total of 156 submissions. The papers are organized in topical sections on knowledge engineering and case-based reasoning, planning and scheduling, distributed AI and multi-agent systems, AI in education and intelligent tutoring systems, knowledge representation and reasoning, machine learning and knowledge acquisition, knowledge discovery and data mining, natural language processing, robotics, computer vision, uncertainty and fuzzy systems, and genetic algorithms and neural networks.