Key points are not available for this paper at this time.
Due to the widely deployed sensors in the pipeline network, the data-driven detection method is a natural choice with multiple sensor measurements. However, the incomplete data problem caused by device failure or network interruption seriously hinders the implementation of pipeline status monitoring. Aiming at this difficulty, this article proposes a generative adversarial network based on trinetworks form (tnGAN) to handle leak detection problems with incomplete sensor data. First, the generative model is proposed to recover incomplete data through fully exploiting the same-level nature similarity of data features. Therein, the same type of sensor data, obtained from the pipeline network, is used as the input. Next, to further boost the temporal evolvement characteristics and the spatial similarity, a multiview awareness strategy is incorporated in the established model to facilitate the integration of inherent information. Then, a dual-discriminative network architecture is proposed to detect pipeline status through computing the similarity of the latent features of samples. With the abovementioned structure, the proposed method can achieve different incomplete data recovery situations, such as individual lost and random missing. In addition, it can also aggregate the output and features of the discriminative networks to obtain the pipeline leak detection result. Finally, the experiment results on a pipeline network demonstrate the capability and effectiveness of the proposed method in both data recovery and leak detection.
Hu et al. (Fri,) studied this question.