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Springer Predictive Maintenance in Dynamic Systems Book

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Description

Explore advanced decision support tools for predictive maintenance in this comprehensive guide from Springer. This book covers a wide range of essential techniques, including early anomaly and fault detection, diagnosis, and reasoning. You will learn about remaining useful life prediction through fault prognostics, as well as methods for quality prediction, self-reaction, optimization, control, and self-healing. The text details recent applications of these methods across various industrial settings, such as production plants, utilities, equipment, and smart devices. It specifically addresses modern challenges found in Industry 4.0, providing insights into managing Big Data Streams and the Internet of Things (IoT). This resource is designed for those looking to understand how to integrate complex decision-making processes into dynamic industrial systems.

Key Features

This Springer publication provides a complete overview of decision support tools used for predictive maintenance in dynamic systems.

Learn advanced methods for anomaly detection, fault diagnosis, and reasoning to improve system reliability.

The text covers fault prognostics for predicting remaining useful life and techniques for quality prediction and self-reaction.

Explore optimization, control, and self-healing techniques applied to modern industrial equipment and plants.

Study real-world applications addressing Industry 4.0 challenges, including Big Data Streams and the Internet of Things.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
12 March 2019
Listed Since
09 November 2018

Barcode

No barcode data available

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