# High Precision Detection Pipe Bursts Based on Small Sample Diagnostic Method

## TL;DR

A new method combining head loss ratio and deep transfer learning improves pipe burst detection accuracy in water networks using limited data.

## Contribution

Proposes a novel small sample diagnosis method for high-precision pipe burst detection using head loss ratio and deep transfer learning.

## Key findings

- The HLR method improves leaked features by 350%.
- The SSDM achieves 99.56% accuracy in pipe burst detection.
- The method successfully applies to real-world water distribution networks.

## Abstract

In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the burst state was quickly detected through the limited data analysis of pressure monitoring points. The HLR method was introduced to enhance data features. DTL was introduced to improve the accuracy of small sample burst detection. The simulated data and real data were enhanced by HLR. Then, the model was trained and obtained through the DTL. The performance of the model was evaluated in both simulated and real scenarios. The results indicate that the leaked features can be improved by 350% by the HLR. The accuracy of SSDM reaches 99.56%. The SSDM has been successfully applied to the detection of real WDNs. The proposed method provides potential application value for detecting pipe bursts.