Method for identifying construction disturbance and leakage along long oil and gas pipeline based on AlexNet
A recognition method and a technology for oil and gas transportation, applied in pattern recognition in signals, neural learning methods, character and pattern recognition, etc., can solve problems such as the decline in recognition rate and hidden dangers of long-term oil and gas pipeline leakage, and reduce the loss of communication performance , Enhanced model generalization ability and high engineering reliability
Pending Publication Date: 2021-05-14
XIAN UNIV OF SCI & TECH
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[0005] The purpose of the present invention is to provide a construction disturbance and leakage identification method along the long-distance oil and gas pipeline based on the AlexNet network, to solve the traditional identification method that requires the selection of feature values, and to avoid the decline in recognition rate caused by improper selection of feature values problem, and subdivides the disturbance signal into manual excavation and machine excavation, combined with the leakage signal, to realize comprehensive long-distance oil and gas pipeline leakage hidden dangers and hazard identification
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[0035] Embodiment A construction disturbance and leakage identification method along the long-distance oil and gas pipeline based on AlexNet network
[0036] A construction disturbance and leakage identification method along the long-distance oil and gas pipeline based on AlexNet network, its flow chart is as follows figure 1 shown, including the following steps:
[0037] S1. Use the sensing method based on Φ-OTDR technology to collect construction disturbance signals during manual excavation within 5 meters along the long-distance oil and gas pipeline, construction disturbance signals during machine excavation within 50 meters, pipeline leakage signals, and environmental background noise Signal and soil vibration signals, establish a field signal database, and build a laboratory simulation field by analyzing relevant characteristics, simulate the state of oil and gas pipelines under different working conditions, collect experimental data of the laboratory simulation field, an...
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The invention belongs to the field of pipeline state monitoring, and discloses method for identifying construction disturbance and leakage along a long oil and gas pipeline based on the AlexNet. The method comprises the following steps of: acquiring a signal and establishing a database; signal processing; establishing the AlexNet for training and testing; and optimizing the AlexNet , and carrying out identification and the like. The method for identifying construction disturbance and leakage along the long oil and gas pipeline based on the AlexNet provided by the invention is high in reliability, realizes comprehensive hidden danger and danger identification of leakage of the long oil and gas pipeline, is high in identification rate, further realizes combination of software and hardware, and can detect all points on an optical fiber link without being influenced by current. The method is suitable for identification of construction disturbance and leakage along the long oil and gas pipeline.
Description
technical field [0001] The invention belongs to the field of pipeline state monitoring, and relates to identification of construction disturbance and leakage along oil and gas pipelines, in particular to an AlexNet network-based construction disturbance and leakage identification method along long oil and gas pipelines. Background technique [0002] In recent years, my country's pipeline transportation construction has advanced by leaps and bounds, which exposed the safety early warning technology that is far behind the development of pipeline transportation. The asynchronous development of the two has led to frequent pipeline transportation accidents in recent years, which has brought great harm to the safety of people's lives and property. loss. [0003] The methods currently used in the field of leak detection mainly include artificial neural networks, support vector machines, etc. The following introduces several traditional algorithms that are often used in signal recogn...
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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06F17/14
CPCG06F17/142G06N3/04G06N3/08G06F2218/02G06F2218/12G06F18/2414G06F18/214
Inventor 严瑞锦李俊阮诗怡刘莹莹田彪张紫琦骆宏杰曹豫其刘楚琪秦小川裴文博张訢炜
Owner XIAN UNIV OF SCI & TECH