Markus Till and Stephan Rudolph Institute for Statics and Dynamics of Aerospace Structures

7 (+ $< " Markus Till and Stephan Rudolph Institute for Statics and Dynamics of Aerospace Structures University Stuttgart, Pfaffenwaldring 27, D-70569 Stuttgart, Germany Proceedings SPIE Conference On Applications and Science of Computation al Intelligence III, Orlando, Florida, April 24-28th, 2000 ABSTRACT Successful signal classification requires the selection of a problem-oriented signal representation and the extraction of a feature vector for final classification. The choice of the optimum representation and optimum features needs some a priori knowledge of the inherent structure and symmetries of the time series as well as the differences between the classes to be distinguished. One central question is: which time series are really similar? An important task for successful signal classification is therefore a clear concept of similarity. A very powerful tool for signal analysis are time-frequency distributions. They are two-dimensional functions that indicate the time-varying frequency content of one-dimensional signals. Each bilinear time-frequency distribution corresponds to a kernel function that controls the properties of the integral transform. However, there is no single time-frequency distribution which is optimal for all problems. It is still an unsolved problem to determine the optimum timefrequency distribution for a given signal and analysis task. In this work an data-driven adaptive time-frequency distribution is presented. The kernel of the time-frequency distribution is parametrized and adapts to the maximal classification rate. The subsequent feature extraction results from calculating the joint moments of the time-frequency distribution. Dimensional analysis is used for defining a similarity concept for time series analysis through generation of a set of dimensionless classification numbers.