  This paper investigates the use of deep transfer learning based on
convolutional neural networks (CNNs) to monitor the condition of bolted joints
using acoustic emissions. Bolted structures are critical components in many
mechanical systems, and the ability to monitor their condition status is
crucial for effective structural health monitoring. We evaluated the
performance of our methodology using the ORION-AE benchmark, a structure
composed of two thin beams connected by three bolts, where highly noisy
acoustic emission measurements were taken to detect changes in the applied
tightening torque of the bolts. The data used from this structure is derived
from the transformation of acoustic emission data streams into images using
continuous wavelet transform, and leveraging pretrained CNNs for feature
extraction and denoising. Our experiments compared single-sensor versus
multiple-sensor fusion for estimating the tightening level (loosening) of bolts
and evaluated the use of raw versus prefiltered data on the performance. We
particularly focused on the generalization capabilities of CNN-based transfer
learning across different measurement campaigns and we studied ordinal loss
functions to penalize incorrect predictions less severely when close to the
ground truth, thereby encouraging misclassification errors to be in adjacent
classes. Network configurations as well as learning rate schedulers are also
investigated, and super-convergence is obtained, i.e., high classification
accuracy is achieved in a few number of iterations with different networks.
Furthermore, results demonstrate the generalization capabilities of CNN-based
transfer learning for monitoring bolted structures by acoustic emission with
varying amounts of prior information required during training.
