Intelligent Health Inspection for Road Multipart Covers Based on Vibration Feature Encoding and Denoising Diffusion Model

Road multipart covers (MPCs) are installed to seal
the entrance ports of large drains. Due to the long-term impacts
of traffic vehicles, MPC damages may occur. While manual
inspection is effective, it can lead to traffic disruptions and is
inefficient. In this article, we present a noninvasive approach that
analyzes acoustic emissions generated by vehicle-MPC impacts.
Specifically, we propose an effective deep learning-based method
named vibration feature encoding and denoising diffusion model
(VFEDDM), which includes three successive stages. First, the
process of “peak window truncation-> scale normalization->
direction aligned RGB encoding” is proposed to appropriately
form the key characteristics of vibrations in different propagation
directions. This process ensures robustness against variations
in vehicle running conditions and changes in measurement
distances. Second, the recent generative AI, denoising diffusion
model, is introduced to synthesize high-quality RGB feature
images, achieving data augmentation. This can address the model
training issue caused by data imbalances between normal and
defective data. Third, a deep CNN is constructed and trained
by utilizing the augmented RGB image set to learn MPC statusdiscriminative
patterns, which are used to assess the health status
of the test MPCs. The effectiveness of VFEDDM is verified in the
dataset collected from real MPC sites in Hong Kong. It achieves
an accuracy of 0.93 for the test MPCs, and the diagnostic results
are well visualized by t-SNE. This would provide support for
MPC maintenance decision-making and significantly improve
inspection efficiency while reducing traffic interference rendered
by road closures.