This paper deals with the classification of signals in terms of their autocorrelation functions. For each of two classes the eigenfunctions of the autocorrelation function are found, and a proper subset of each one is chosen. An unknown signal is classified by comparing the norms of its projections on the two subsets of eigenfunctions. By working with the eigenfunctions corresponding to the smallest eigenvalues, the method approximates singular detection. An application of this technique is shown for the classification of engine-vibration records as a basis for automatic mechanical fault diagnosis.
['Geng Seng Fang', 'Theodosios Pavlidis']
Signal classification through quasi-singular detection with applications in mechanical fault diagnosis