Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery
Fault diagnosis is crucial to improve reliability and performance of machinery. Effective feature extraction and clustering analysis can mine useful information from large amounts of raw data and facilitate fault diagnosis. This paper presents a novel intelligent fault diagnosis method based on ant colony clustering analysis. Vibration signals acquired from equipment are decomposed by wavelet packet transform, after which sub-bands of signals are clustered by ant colony algorithm, and each cluster as a set of data is analyzed from pattern of frequency band perspective for selecting intrinsic features reflecting operation condition of equipment, and thus fault diagnosis model is established to combine the extracted major features with given fault prototypes from historical data. The classification process for fault diagnosis is carried out using Euclidean nearness degree based on the established model. Furthermore, an improved ant colony clustering algorithm is proposed to adjust comparison probability dynamically and detect outliers. When compared with other clustering algorithms, the algorithm has higher convergence speed to meet requirements of real-time analysis as well as further improvement of accuracy. Finally, effectiveness and feasibility of the proposed method is verified by vibration signals acquired from a rotor test bed.