Vibration isolation is one of the most important issues in the development of smart structures to achieve
high performance of operation. It has historically been handled using passive techniques. However, the smart
structures continue to mature and require greater precision, the design of active control approaches will be
required to achieve desired performance levels. The Stewart Platform, consisting of a stiff active interface with a six degree of freedom, can be used to actively increase the structural damping of flexible systems attached to it.
Each leg of the active interface consists of a linear piezoelectric actuator, a collocated force sensor and flexible tips for the connections with the two end plates. By optionally providing the legs with strain or elongation sensors, the Stewart Platform can also be used as a vibration isolator. In this project, students are required to work together with postgraduate students on the following: (1) Understand and refine the models of the six loops, from every force sensor output to the corresponding actuator input using a frequency domain approach; 2) Based on the derived model, design controllers to achieve the required specifications and 3) Implement the controllers. Experiments are to be carried out to verify the system performance.
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
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
