Real-time burst detection based on multiple features of pressure data

Pipe bursts are an essential issue for water loss in water distribution systems. This study proposes a real-time burst detection method that combines multiple data features of multiple time steps. The method sets burst thresholds in three dimensions according to different moments at a specific monitoring point, and achieves burst identification based on a classification model. First, three data features, namely, absolute pressure value, predicted deviation value obtained by prediction model, and pressure variation value, of historical pressure at each time step are scored based on the Western Electric Company rules. The scores represent different abnormalities. Then, the scores corresponding to the three features are used as input of the decision tree classification model. The trained model is used for detecting burst events. Results show that this method achieves 99.56% detection accuracy, indicating that it is effective for burst detection. The proposed method outperformed the single feature-based method and provides good results in water distribution systems. HIGHLIGHTS A data-driven real-time burst detection method using three different pressure data features, namely, absolute pressure value, predicted deviation value, and pressure variation value. Three stages, namely setting the abnormal recognition thresholds in each feature at each moment separately, scoring real-time pressure feature data, and finally training the decision tree model to form an effective burst recognition model.