Multimodal AI-based water pipeline data accumulation and leakage prediction research using image and ultrasound data

Yuntae Jeon; Byoungjoon Yu; Dongyoung Ko; Dai Quoc Tran; Seunghee Park

doi:10.1117/12.3007717

9 May 2024 Multimodal AI-based water pipeline data accumulation and leakage prediction research using image and ultrasound data

Yuntae Jeon, Byoungjoon Yu, Dongyoung Ko, Dai Quoc Tran, Seunghee Park

Author Affiliations +

Proceedings Volume 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024; 129490F (2024) https://doi.org/10.1117/12.3007717
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

This study presents a multi-modal artificial intelligence (AI)-based water pipeline maintenance method based on RGB images and ultrasound data. Our methodology leverages the concurrent collection of visual and auditory data from pipelines to improve the detection and prediction of anomalies such as abnormal welds, corrosion, scale, and cracks. By converting ultrasound data into spectrogram images using short-time Fourier transform (STFT) and combining them with RGB images, we create a composite data input for a convolutional neural network (CNN) model. This model is trained to classify the condition of water pipelines into distinct categories based on multi-modal inputs. The fusion of these two data modalities aims to significantly enhance the accuracy of pipeline anomaly detection, offering a novel approach for predictive maintenance in water pipeline facilities. We tested on 6 different classes with each 100 pair of datasets, therefore a total of 600 pairs of RGB and spectrogram images, and achieved an average accuracy of 92.7%. Our research contributes the potential application of multi-modal AI in pipeline maintenance.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuntae Jeon, Byoungjoon Yu, Dongyoung Ko, Dai Quoc Tran, and Seunghee Park "Multimodal AI-based water pipeline data accumulation and leakage prediction research using image and ultrasound data", Proc. SPIE 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, 129490F (9 May 2024); https://doi.org/10.1117/12.3007717

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