GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage
This provides a dataset for fault diagnosis in engineering, but it is incremental as it focuses on a specific domain without broad methodological advances.
The authors introduced GPLA-12, a new acoustic dataset with 12 categories and 684 signals for gas pipeline leakage detection, and trained models to observe performance, though no specific results or numbers were provided.
In this paper, we introduce a new acoustic leakage dataset of gas pipelines, called as GPLA-12, which has 12 categories over 684 training/testing acoustic signals. Unlike massive image and voice datasets, there have relatively few acoustic signal datasets, especially for engineering fault detection. In order to enhance the development of fault diagnosis, we collect acoustic leakage signals on the basis of an intact gas pipe system with external artificial leakages, and then preprocess the collected data with structured tailoring which are turned into GPLA-12. GPLA-12 dedicates to serve as a feature learning dataset for time-series tasks and classifications. To further understand the dataset, we train both shadow and deep learning algorithms to observe the performance. The dataset as well as the pretrained models have been released at both www.daip.club and github.com/Deep-AI-Application-DAIP