CN-122015952-A - Method for diagnosing and predicting state of extremely low-temperature medium transportation pipeline

CN122015952ACN 122015952 ACN122015952 ACN 122015952ACN-122015952-A

Abstract

A diagnosis and prediction method for the state of extremely low-temperature medium transportation pipeline includes such steps as collecting Wen Zhen optical fibre data to obtain the temp field and structural dynamic information of pipeline, collecting the sound-print signals of vibration of pipeline by sound-print sensor array arranged on the external wall of pipeline, calculating to obtain the deformation data of pipeline, synchronously collecting the external environmental parameters of pipeline, obtaining multi-source fusion feature set according to Wen Zhen optical fibre data, sound-print signals and external environmental parameters, training the multi-mode deep learning diagnosis model, and obtaining the diagnosis result of pipeline failure.

Inventors

WANG CHU
LIANG YUHANG
XIANG JUNWU
YU YING
JIN ZHAOYU

Assignees

浙江探灵科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260127

Claims (8)

1. The method for diagnosing and predicting the state of the extremely low-temperature medium transportation pipeline is characterized by comprising the following steps of: Wen Zhen optical fiber data of a distributed Wen Zhen optical fiber sensor axially distributed along a pipeline are collected, and temperature field and structure dynamic information of the pipeline are obtained according to Wen Zhen optical fiber data; Collecting voiceprint signals of pipeline vibration by using a voiceprint sensor array arranged on the outer wall of a pipeline, and calculating to obtain pipeline deformation data, wherein the pipeline deformation data comprises pipeline displacement and displaced positions; synchronously collecting external environment parameters of the pipeline; Obtaining a multisource fusion characteristic set according to the temperature field, the structure dynamic information, the voiceprint signal and the external environment parameters; Training a pre-established multi-mode deep learning diagnosis model by using a multi-source fusion feature set; obtaining a diagnosis result of the pipeline fault according to the response of the trained multi-mode deep learning diagnosis model to the current multi-source fusion characteristics; Constructing a time sequence sample based on the current diagnosis result and the historical fault monitoring data of the last N months; inputting the time sequence samples into a pre-established prediction model; Obtaining a prediction result of the pipeline fault according to the output of the prediction model; and generating risk early warning information according to the diagnosis result and the abnormal position.
2. The method for diagnosing and predicting the state of a cryogenic medium transportation pipeline according to claim 1, wherein, The method for acquiring the temperature field and the structure dynamic information of the pipeline according to Wen Zhen optical fiber data comprises the following steps: preprocessing the collected Wen Zhen optical fiber data to remove noise and abnormal values; analyzing the vibration frequency component by adopting Fourier transform or wavelet transform to determine the structure dynamic information; Carrying out sliding window average and first order difference processing on Wen Zhen optical fiber data, extracting the temperature change rate and the temperature gradient distribution characteristics, reading the thermal expansion coefficient of the pipeline material, and calculating the temperature change according to the structure dynamic information; And obtaining the temperature field of the pipeline according to the continuous temperature change tracking and the set initial temperature value.
3. The method for diagnosing and predicting the state of a cryogenic medium transportation pipeline according to claim 2, wherein, The method for acquiring the temperature field and the structure dynamic information of the pipeline according to Wen Zhen optical fiber data further comprises the following steps: segmenting the pipeline, and calculating according to the temperature field of the pipeline to obtain segmented temperature space gradient and temperature difference time domain change rate; According to the temperature space gradient and the temperature difference time domain change rate, a temperature change uneven region is obtained, and according to the temperature change uneven region, a stress abnormal region is obtained.
4. The method for diagnosing and predicting the state of a cryogenic medium transportation pipeline according to claim 1, wherein, The method for acquiring the pipeline deformation data by calculating the voiceprint signals of the pipeline vibration by utilizing the voiceprint sensor array distributed on the outer wall of the pipeline comprises the following steps: calculating the displacement of the pipeline according to the data of the plurality of voiceprint sensors by using a triangulation method; The position of the displaced pipeline is positioned through time delay analysis of the voiceprint signal.
5. The method for diagnosing and predicting the state of a cryogenic medium transportation pipeline according to claim 1, wherein, The method for obtaining the multisource fusion characteristic set according to Wen Zhen optical fiber data, voiceprint signals and external environment parameters comprises the following steps: Extracting temperature trend characteristics and vibration deep frequency characteristics from Wen Zhen optical fiber data; extracting time-frequency domain features and deep time-frequency features extracted from the voiceprint signals and convolutional neural networks; extracting multi-parameter associated features from the environmental data; The temperature trend feature, the vibration deep frequency feature, the time-frequency domain feature, the deep time-frequency feature and the multi-parameter association feature are fused through an attention mechanism, and then fault types are associated to form a multi-source fusion feature set.
6. An electronic device comprising a processor and a memory; the processor is connected with the memory; The memory is used for storing executable program codes; The processor runs a program corresponding to executable program code stored in the memory by reading the executable program code for performing the method according to any one of claims 1-5.
7. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-5.
8. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.

Description

Method for diagnosing and predicting state of extremely low-temperature medium transportation pipeline Technical Field The invention relates to the technical field of pipeline monitoring, in particular to a method for diagnosing and predicting the state of an extremely low-temperature medium transportation pipeline. Background The long-distance pipeline transportation of extremely low-temperature mediums such as liquefied natural gas, liquid nitrogen and low-temperature chemical raw materials is a key infrastructure in the field of energy and chemical industry. The pipeline operates under the ultralow temperature working condition, and the structural integrity and the sealing reliability of the pipeline are directly related to the transportation safety and the efficiency. The real-time sensing, accurate diagnosis and fault prediction of the pipeline state have important significance for preventing major safety accidents, environmental pollution and economic loss caused by leakage and breakage. At present, monitoring of pipeline states in industry mainly depends on a traditional sensor technology and a data analysis method, but when dealing with a special scene of extremely low-temperature medium transportation pipelines, the prior art has the obvious defects that traditional pipeline temperature detection equipment such as thermocouples and infrared thermometers cannot be directly contacted with extremely low-temperature mediums below-40 ℃, only can detect the temperature of the outer wall of the pipeline, cannot reflect the temperature distribution of the medium inside the pipeline, causes lagged temperature data and large deviation, cannot support internal state judgment, traditional displacement detection equipment such as strain gauges and laser rangefinders are easy to fail in extremely low-temperature, high-humidity and high-saline-alkali environments, and have positioning accuracy more than centimeter level, cannot capture millimeter-level micro displacement of the pipeline caused by medium temperature change and environmental load, is difficult to evaluate the structural stability of the pipeline, the traditional system depends on single parameter judgment of pipeline states, does not combine transportation medium characteristics and environmental factors, causes diagnosis result face to be high in false judgment rate, particularly, the traditional prediction method cannot effectively extract the correlation characteristics of pipeline historical data and current data in advance, and has low prediction accuracy and potential risk early warning in the scene of large fluctuation of the environmental data. The prior art is limited by the defects of sensor hardware performance, a data processing method and an analysis model, and has obvious short plates in the aspects of direct sensing of the internal state of an extremely low-temperature medium transportation pipeline, accurate millimeter-level displacement monitoring, multi-dimensional state fusion diagnosis, high-precision prospective prediction and the like. Disclosure of Invention Aiming at the problems in the related art, the invention provides an extremely low temperature medium transportation pipeline state diagnosis and prediction method, so as to overcome the technical problems in the prior related art. For this purpose, the invention adopts the following specific technical scheme: a method for diagnosing and predicting the state of an extremely low-temperature medium transportation pipeline comprises the following steps: wen Zhen optical fiber data of distributed Wen Zhen optical fiber sensors axially distributed along the pipeline are collected, and temperature field and structure dynamic information of the pipeline are obtained according to Wen Zhen optical fiber data; Collecting voiceprint signals of pipeline vibration by using a voiceprint sensor array arranged on the outer wall of a pipeline, and calculating to obtain pipeline deformation data, wherein the pipeline deformation data comprises pipeline displacement and displaced positions; synchronously collecting external environment parameters of the pipeline, including environment temperature, humidity, wind speed, soil salinity and alkalinity and precipitation; Obtaining a multisource fusion characteristic set according to the temperature field, the structure dynamic information, the voiceprint signal and the external environment parameter; Training a pre-established multi-mode deep learning diagnosis model by using a multi-source fusion feature set; obtaining a diagnosis result of the pipeline fault according to the response of the trained multi-mode deep learning diagnosis model to the current multi-source fusion characteristics; Constructing a time sequence sample based on the current diagnosis result and the historical fault monitoring data of the last N months; inputting the time sequence samples into a pre-established predictive model combining a bidirectional LSTM and an attenti