Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
173298 | Computers & Chemical Engineering | 2010 | 5 Pages |
Abstract
A warning system for detection of excessive vibration in a centrifuge system of a product treatment plant is built using a database of past faults and an equivalent amount of normal operating null data. A logistic Partial Least Squares (PLS) model is derived using wavelet coefficients to approximately decorrelate the time series data. This model provides a baseline to evaluate any improvement through kernel methods. The kernel paradigm is introduced from a Bayesian perspective and used to develop a detector with significantly less false positives and missed detections.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
A.J. Willis,