Loose bolt localization and torque prediction in a bolted joint using lamb waves and explainable artificial intelligence

Loose bolt localization and torque prediction in a bolted joint using lamb waves and explainable artificial intelligence

Hu, M. (TU Delft Structural Integrity & Composites; Harbin Engineering University)
Yue, N. (TU Delft Group Yue)
Groves, R.M. (TU Delft Group Groves)

2024

With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path.

explainable AI
Lamb waves
loose bolt localization
one-dimensional convolutional neural network
structural health monitoring
torque prediction

http://resolver.tudelft.nl/uuid:4a3522df-5002-4d85-8fcd-fd09ab671f3f

https://doi.org/10.1177/14759217241241976

1475-9217

Structural Health Monitoring: an international journal

Institutional Repository

journal article

© 2024 M. Hu, N. Yue, R.M. Groves