: Utilize a Deep Auto-Encoder (DAE) or Convolutional Neural Network (CNN) . These models are designed to learn complex, non-linear patterns that traditional manual feature engineering might miss.
: For complex machinery data, techniques like Local Preserving Projection (LPP) are often applied to fuse multiple deep features, making the final representation more effective for tasks like fault classification.
: Combine the .rar parts to access the raw signal data (often vibration or acoustic signals). Normalize the data to prepare it for neural network input.