AI Techniques for Reliability Prediction for Electronic Components

AI Techniques for Reliability Prediction for Electronic Components

Cherry Bhargava (Lovely Professional University, India)
Release Date: December, 2019|Copyright: © 2020 |Pages: 330
ISBN13: 9781799814641|ISBN10: 1799814645|EISBN13: 9781799814665|DOI: 10.4018/978-1-7998-1464-1


In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry.

AI Techniques for Reliability Prediction for Electronic Components provides emerging research exploring the theoretical and practical aspects of prediction methods using artificial intelligence and machine learning in the manufacturing field. Featuring coverage on a broad range of topics such as data collection, fault tolerance, and health prognostics, this book is ideally designed for reliability engineers, electronic engineers, researchers, scientists, students, and faculty members seeking current research on the advancement of reliability analysis using AI.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Bayesian Networks
  • Data Collection
  • Deep Learning
  • Evolutionary Computing
  • Fault Tolerance
  • Fuzzy Systems
  • Health Prognostics
  • Neural Networks
  • Quality Control
  • Reliability Analysis

Table of Contents and List of Contributors

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