2022, 49(10):43-50.
Abstract:Accurate detection of leakage is the key to reduce the leakage rate of water distribution networks. This paper proposes a leakage detection method based on a semi-fixed-length sliding window. The method uses a sliding window to detect leakages by analyzing the time series flow data and improves the information quality of the acquired data with a variable-length window, in which the length of the window is limited. Based on Clustering by Fast Search and Find of Density Peaks(CFSFDP),the entropy function is introduced to adaptively select cutoff distance parameters according to the data distribution characteristics. In this way, the detection rate of leakage events is improved. The experimental results show that the proposed algorithm can effectively detect the leakage data in the four simulated scenarios, and obtain a higher leakage detection rate and a lower false alarm rate.
2018, 45(2):110-118.
Abstract:As the data imbalance of pipeline working conditions decreases the accuracy of the pipeline leakage diagnosis, a method of pipeline leak detection and location based on imbalance data was proposed. First, the imbalance data of different working conditions were processed by K-means clustering algorithm and under-sampling to achieve the balance data. Then, the Fischer-Burmeister function was introduced into the learning process of the twin support vector machine (TWSVM), in order to avoid the matrix inversion calculation, and the balance data were input into the improved TWSVM to distinguish the pipeline leakage. Leak location was obtained by the cross-correlation function method. Moreover, a flow model of pipeline was put forward based on the Flowmaster software, and the proposed method was used to identify pipeline leakage. The experimental results show that the proposed method is more effective than the classical TWSVM and the Lagrange TWSVM to identify the pipeline leakage aperture and location.