Improved FSVM and multi-class fuzzy membership method for aeroengine vibration fault identification

In order to diagnose and identify effectively faults for an aeroengine's whole-body vibration, the improved fuzzy support vector machine (FSVM) and multi-class fuzzy membership method combined with the information entropy technique was proposed here. It was compared with the traditional FSVM membership analysis method. The calculation model of multi-class fuzzy membership was established based on improving the traditional FSVM fuzzy membership function. Tests and examples for aircraft engines' overall vibration performance, and fault diagnosis and identification verified that the technology of multi-class fuzzy membership combined with information entropy is very effective. The weighted values between fault modes and fault causes were determined and the multi-parameter vibration performance analysis model was developed, the effects of various vibration causes on the overall state of an aeroengine were analyzed quantitatively, and a quantitative reference index was provided for aeroengine vibration suppressing.