Health condition monitoring of aeroengine with unknown clustering number based on ant colony algorithm

An algorithm based on ant colony algorithm for health condition monitoring of aero-engine with unknown clustering number was put forward. The algorithm conversed the health status classification of aero-engine into solving the clustering-based optimization problem with constrain. Ant colony algorithm based on colony collaboration and learning could solve this clustering problem. The proposed algorithm was applied to a monitor case of health condition for an aero-engine. The emulation result shows that the algorithm has the merits of simple realization, fast convergence, strong parallelism and robustness, high identification accuracy and high reliability. The algorithm is fit for health condition monitoring of aero-engine with unknown clustering number and with low demands on fault samples.