{'fraction_non_alphanumeric': 0.014042126379137413, 'fraction_numerical': 0.0, 'mean_word_length': 4.870588235294117, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Modeling of gas pipeline in order to implement a leakage detection system using artificial neural networks based on instrumentation

In this paper, by using of gas flow pattern, a novel neural network‐based fault detection method is presented to detect the leakage in the gas pipeline. The pipe is divided into four segments, and each segment is modeled by using input/output pressure of the gas flow. For this purpose, the acquired practical data from the real life gas pipeline are gathered and utilized for training a neural network to model the process. Some of the data are used for training set to adjust the neural network weights, and others are used to evaluate the performance of the neural network‐based fault detection system. Gathered practical data from a real life pipeline made sure that the proposed method is prominent and applicable for practical implementations. The model was verified with the data obtained from the test in the actual pipeline and compared with leakage mode.