Alarm Pattern Analysis Using Computational Intelligence Approach

In today’s high value manufacturing, machines are often equipped with sensors to detect disturbances in
the system, which may trigger the corresponding alarm(s) when limits are exceeded. Apart from displaying on a
terminal for human intervention, these alarm values are also logged to a database. Machine faults or breakdown
may be characterized by a set of alarm patterns. The ability to detect these patterns early can help to alert and
prevent impending machine failure, which is extremely useful for mission critical machines. This project involves
the development of pattern identification concepts and algorithms based on computational intelligence approaches
to predict machine failures. Some of these approaches include Ant Colony System, Genetic Algorithm, and
Statistical methods.
Students with keen interest to learn computational intelligence for pattern recognition and those with knowledge
in manufacture equipment management will have an added advantage for this project

Investigation of the Sequential Accelerator on the Perceptron for Pattern Recognition

In machine learning methods, when the input data becomes extremely large, the current direct methods
require too large learning times and memory. This project investigates one sequential method to overcome this
problem. It is quite a simple method to implement and is tested using the Perceptron as the base classifier. The
perceptron converges very slowly. So it will be interesting to find out if the proposed accelerator can improve
significantly the computational times of this simple classifier. The student will try various investigations on
different very large data sets and to measure their computational complexities. It has been shown that this
sequential method is very fast and only need a small subset of the large data set to complete the learning.

Investigation of the Sequential Accelerator on LDA for Pattern Recognition

In machine learning methods, when the input data becomes extremely large, the current direct methods
require too large learning times and memory. This project investigates one sequential method to overcome this
problem. It is quite a simple method to implement and is tested using the well-known LDA classifier. The student
will try various investigations on different very large data sets and to measure their computational complexities. It
has been shown that this sequential method is very fast and only need a small subset of the large data set to
complete the learning.