Abstract
This paper examines the efficacy of symbolic time series analysis for online detection of fatigue failure in mechanical structures. The detection algorithm is formulated on the principles of Symbolic Dynamics and Automata Theory. The performance of this method is evaluated based on the information extracted from available sensor data for early detection of small anomalies in the observed data sequence. This concept is experimentally validated on a fatigue damage test apparatus. The time series data, generated from ultrasonic sensor and optical microscope, have been used for detection of small fatigue crack growth in ductile alloy 7075-T6 aluminium specimens.
| Original language | English (US) |
|---|---|
| Article number | ThC15.2 |
| Pages (from-to) | 3309-3314 |
| Number of pages | 6 |
| Journal | Proceedings of the American Control Conference |
| Volume | 5 |
| State | Published - 2005 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering