Advanced Fault Diagnosis in Milling Machines Using Acoustic Emission and Transfer Learning

The accurate diagnosis of faults in milling machines is important to ensure manufacturing efficiency and minimize downtime. Acoustic emission (AE) signals, known for their transient and high-frequency nature, provide valuable insights into tool and machine faults. However, their non-stationary characteristics present challenges for traditional analysis methods. This study proposes an innovative framework that combines time-frequency representation, transfer learning, and dimensionality reduction for effective fault diagnosis. AE signals are transformed into scalograms and spectrograms using continuous wavelet transform (CWT) and short-time Fourier transform (STFT), respectively, extracting both localized and global signal characteristics. These visual representations are processed through pre-trained deep learning architectures, EfficientNet-B0 and InceptionV3, to extract high-level features. Dimensionality reduction through uniform manifold approximation and projection (UMAP) further refines these features while preserving useful patterns. Finally, a lightweight k-nearest neighbors (k-NN) classifier is used to distinguish across all classes with high accuracy, achieving an average of 99.60% cross-validation performance. This framework highlights the strength of combining transfer learning with dimensionality reduction for fault diagnosis, providing a computationally efficient and highly accurate solution with significant potential for real-time monitoring and predictive maintenance in advanced manufacturing systems.