Health evaluation methodology of remote maintenance control system of natural gas pipeline based on ACWGAN-GP algorithm

Abstract

The remote maintenance system of natural gas pipeline is of great significance to ensure the safe and stable operation of the pipeline network. The multi-classification method based on machine learning is more effective for the health evaluation of remote maintenance control system than the traditional evaluation method based on expert experience. In view of the sev ere imb alance i n the nu mber of s amples of f ive health levels, a health evaluation methodology of remote maintenance control system based on Wasserstein distance sand auxiliary classification generative adversarial network (ACWGAN-GP) is proposed. Firstly, the model stability is improved by introducing Wasserstein distance and gradient penalty. The generator generates balanced data, while the discriminator trains with generated and actual data. In this way, several ACWGAN-GP sub-models are trained. Then, the health levels of the sub-model are directly obtained by using the discriminator to classify the samples. Finally, according to the hierarchical relationship of the system, a parallel-serial combined evaluation method is adopted. By this means, the health evaluation model of remote maintenance control system including ACWGAN-GP sub-models is constructed. The experimental results based on 13 sets of KEEL and UCI multi-class imbalanced datasets and actual sampling data show that the effectiveness and advancement of the proposed method improved significantly compared with the existing similar typical algorithms.