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To accurately diagnose the mechanical failure of fan, the method based on the symmetrized dot pattern (SDP) analysis and improved Back Propagation (BP) neural network is proposed. Vibration signals acquisition of 13 kinds of running states were achieved on the 4-73 No.8D centrifugal fan test rig and the SDP technique was utilized to reconstruct the vibration signals. Then, the features of the SDP pattern of each running state were extracted and the fault eigenvectors based on the multiple feature fusion were constructed. Finally, the sample set of the eigenvectors was trained and tested by the improved BP neural network to diagnose the mechanical failure of the fan. The results show that the fan fault diagnosis method based on the SDP analysis and improved BP neural network can effectively diagnose the category, severity and site of the fan mechanical failures with high diagnosis rate, short testing time and good online diagnosis performance.

Fan fault diagnosis based on symmetrized dot pattern and improved BP neural network