Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Algorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data. This unique text/reference describes in...

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Bibliographic Details
Main Authors: Aldrich, Chris (Author), Auret, Lidia (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2013.
Series:Advances in Computer Vision and Pattern Recognition,
Subjects:
Computer science.
Artificial intelligence.
Computer Science.
Artificial Intelligence (incl. Robotics).
Online Access:Full Text via HEAL-Link

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Full Text via HEAL-Link

ΒΚΠ - Πατρα: ALFd

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Call Number: 330.01 BAU
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ΒΚΠ - Πατρα: BSC

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Call Number: 330.01 BAU
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