  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
