Neural network system for helicopter rotor smoothing

Helicopter rotor smoothing (track and balance) is a periodic maintenance task required to minimize rotor induced aircraft vibrations at the fundamental (once per revolution) rotor frequency. We have designed and implemented a general, neural network based software system for rotor smoothing. Neural networks provide non-parametric mappings between the spaces of adjustments and vibration measurements. In the network training process, these mappings are extracted from experimental data without any assumptions about their functional form. On the other hand, when the experimental data available for training is not complete, simulated data based on model dependencies (linear or other) may be also incorporated into the neural network model. The neural networks are easily updated (retrained) if new data becomes available thus allowing the system to evolve and mature in the course of its use. The customization of the system for helicopters of different types is facilitated by general-purpose software for application development, which includes preparation of flight data and neural network training. It is worth noting that the prototype applications developed up to date required relatively modest amount of flight data (20-30 flights). The neural network system has been applied to Apache (AH-64), Blackhawk (UH-60), and Kiowa Warrior (OH-58D) helicopters as part of the Vibration Management Enhancement Program (VMEP). Preliminary results are very encouraging. In the verification tests, we were able to shorten the smoothing time for AH-64, with all of the rotor smoothing procedures completed in 2 to 4 flights. In all cases, the neural network approach produced solutions with experimentally verified low vibration levels and small track split. The system has also demonstrated the ability to detect errors in implementation of the smoothing adjustments. Application to other types of helicopters is considered