By Topic

Incipient fault detection and diagnosis using artificial neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

Fault is defined as degradation between 100% performance and complete failure. The authors demonstrate how an artificial neural network can detect and diagnose faults from online process data. A wide range of input patterns can be learned by artificial neural networks in the presence of noise by changing the interconnections of the nodes, their thresholds for activation, and their individual weights. Artificial neural networks are able to take inputs from the processes without knowing the process model, allowing representation of complex engineering systems which are difficult to model either with traditional model-based engineering or knowledge-based expert systems. A description is given of some of the characters of a neural network that are useful for fault discrimination in a chemical plant. It is shown that even when using noisy sensor data, the misclassification rate is nil

Published in:

Neural Networks, 1990., 1990 IJCNN International Joint Conference on

Date of Conference:

17-21 June 1990