… This work proposes a semi-supervised machine learning … Although pipelines have the
lowest accident rates compared to … to the oil industry. CONCAWE associated the failures in the …

… Data-driven approaches, based on statistical learning algorithmsare far more suitable in …
a semi-supervised and a supervised learning approach, for damage detection in pipes. In …

… detect leakages in oil and gas pipelines. In this framework, our novelty lies in the emphasis
on providing Situational Awareness of the oil and gas pipelines … ever-growing oil industry has …

… semi-supervised learning by using a real-world case. The accuracy of the hybrid model is
verified in comparison with other traditional machine learning … the fault detection of pipeline …

… Considering the recognition algorithm, it is common to use machine learning and deep
learning methods (DL) [11]. Sheng et al. [12] improved the stochastic configuration network …

… of the gas and oil industry. Modern data-driven fault … a semi-supervised machine learning
method based on multivariate time series data for leak detection of natural gas pipelines. Thus, …

… Deep learning can enhance the accuracy and real-time performance of pipeline leak … for
urban gas pipeline leaks based on a semi-supervised learning Generative Adversarial Network (…

… gas and oil industry. Due to the lack of leak data and the changes in leak features, semi-supervised
leak detection … More importantly, for most real pipelines, leak accidents are infrequent…

… Machine learning has powerful data processing capabilities and real-time computing …
supervised learning. Therefore, the present paper focuses on developing semi-supervised learning …

… of such pipeline accidents that … detecting defects in pipelines can be classified into different
categories, including supervised, semi-supervised, unsupervised, or reinforcement learning, …