Helicopter fault detection and classification with neural networks

The application of neural networks to helicopter drive train fault detection and classification is discussed. A practical approach to the problem is outlined including preprocessing and network design issues. Two different neural networks are designed, constructed and demonstrated. The results indicate that a low-resolution fast Fourier transform (FFT) may provide a sufficiently rich feature set for fault detection and classification if combined with a properly structured and controlled neural network. Future directions for this work are discussed, including more data, longer time window, channel synchronization to pulse, and additional layers of cross-checking class neurons