Gas flow meter anomaly data detection based on fused LOF-DBSCAN algorithm

Abstract

Anomaly detection for gas flowmeter data is one of the important means to improve the reliability of fair trade of natural gas transmission and distribution. However, the field environment of natural gas in the industrial scene has the characteristics of complex anomaly categories and difficult to distinguish some anomalies. At the same time, the traditional anomaly detection methods are difficult to accurately analyze the abnormal state for a period of time, and are easy to be disturbed by many factors. For example, although DBSCAN (density based spatial clustering of applications with noise) can cluster dense data sets of arbitrary shape, it will greatly affect the classification effect of data sets with uneven density, and the noise points will also interfere to a certain extent, resulting in the weakening of the ability of the algorithm to distinguish anomalies. LOF(local outliers factor) algorithm realizes outlier detection by calculating the local density deviation of a given data point relative to its neighborhood. In view of the above problems. A more accurate anomaly detection strategy is proposed. Firstly, the local anomaly factor algorithm is used to eliminate outliers with too large LOF value, so as to reduce the clustering effect of DBSCAN due to uneven density as much as possible. Experiments show that the clustering effect of this strategy is significantly improved compared with the traditional detection methods.