Anomaly detection of gas flow meter based on improved GAN

Abstract

As one of the core equipment in the gas industry, gas flow meter guides the operation and maintenance of gas companies and reflects the gas consumption habits of downstream users. Therefore, the anomaly detection of gas flow meter has important theoretical value and practical significance. However, the sensors of the gas flow meter are coupled with each other, and the collected data has the characteristics of difficult to collect negative samples. For the solution of the above challenges, an approach to unsupervised anomaly detection based on generative adversarial networks (GAN) is introduced in this paper. Firstly, after the flow meter signal is processed with the data screening, the Savitzky Golay (S-G) filter can filter out the noise from the raw data. The information between channels is captured by the feature attention module for improving the accuracy of the potential representation of samples, in addition, residual blocks are used to prevent the network degradation phenomenon in the deep network. Finally, the proposed model was demonstrated to be valid by using the actual user gas flow meter data. Compared with the current popular depth learning methods, the experimental results reveal that the proposed method can be very accurate in terms of ACC and F1 scores.