Multiple intelligent debris classifying system (MIDCS) to detect sensitive wear particles whose size are 10μm or more in long axis on the aero-engine rolling bearing for wear fault monitoring was developed. The MIDCS overcomed the shortcomings

such as the insensitivity to large wear particles in the traditional spectral analysis

and cumbersome detection steps in the ferrographic analysis. The MIDCS could be used to directly detect wear particles whose size are 10μm or more in long axis in the flowing oil. Seven digital characteristic parameters and their identification were proposed for the moving wear particles in the flowing oil

implemented the auto-identification for the wear particles. The basic identification accuracy of the MIDCS is above 99%. Through the experiments on the real oil samples from aero-engines

result shows that the MIDCS is superior to the spectral analysis in terms of the ability and timeliness on detecting the wear abrasion fault of the aero-engine rolling bearing.