Damage level recognition for planetary gearbox in rotorcraft based on GRA and ANN

Planetary gearbox is a common mechanical component and is widely used to transmit power and change speed and/or direction in rotary aircrafts. The part failure of planetary gearbox is one of the main causes for the helicopter accidents. The need to identify the level of developing damage in part is central to reduce mechanically induced failures. An approach based on grey relational analysis(GRA) and artificial neural net (ANN) is presented to recognize the damage level quantitatively for planetary gearbox of rotorcraft. A particular emphasis is put on the feature selection based on GRA and the damage level recognition based on BP ANN. After that, the experiments with different-level-damage seeded are designed to validate the method above, and then the proposed method is used to identify the damage level based on test data. With the results of several experiments for damage level recognition, the feasibility and the effect of this approach are verified.