A novel feature extraction method is presented by combining wavelet packet transform with ant colony clustering analysis in this paper. Vibration signals acquired from equipments are decomposed by wavelet packet transform, after which frequency bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed in frequency-domain for extracting intrinsic features reflecting operating condition of machinery. Furthermore, the robust ant colony clustering algorithm is proposed by adjusting comparing probability dynamically. Finally, effectiveness and feasibility of the proposed method are verified by vibration signals acquired from a rotor test bed.
