Название: AI Techniques for Reliability Prediction for Electronic ComponentsАвтор: Cherry Bhargava
Издательство: IGI Global
Год: 2020
Страниц: 345
Язык: английский
Формат: pdf (true), epub
Размер: 26.7 MB
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.
The electronics industry is complex, consisting of several diverse components, technologies, process, materials and devices, ranging from higher order to Nano-order with multiple faces. The use of electronics improves industrial performance, due to their size, price, speed and ability to store information. Use of electronics is increasing on faster pace in every segment of manufacturing and design industry. In fact, we come into contact with them every day in such areas as transportation, communications, entertainment, instrumentation and control, aviation, IT, banking, medical appliances, home appliances, manufacturing etc. In world of technical competition, the effective relationship between electronics lifecycle and reliability is becoming complex and crucial.
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.
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