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...
| Main Authors: | , |
|---|---|
| Corporate Author: | |
| Format: | Electronic |
| Language: | English |
| Published: |
London :
Springer London : Imprint: Springer,
2013.
|
| Series: | Advances in Computer Vision and Pattern Recognition,
|
| Subjects: | |
| Online Access: | https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4471-5185-2 |