CN-122017851-A - Soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption

CN122017851ACN 122017851 ACN122017851 ACN 122017851ACN-122017851-A

Abstract

The invention discloses a soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption, and relates to the technical field of industrial pipeline nondestructive testing. The method can at least partially solve the problems of unstable detection signals and insufficient recognition accuracy of micro corrosion thinning under the high-temperature environment in the prior art. The method comprises the steps of collecting the surface temperature of a pipeline and calculating the force contribution ratio of Lorentz force and magnetostriction force, dynamically optimizing electromagnetic ultrasonic body wave excitation parameters according to the force contribution ratio, exciting body waves point by point along the axial direction of the pipeline and collecting echo signals, adopting empirical mode decomposition and wavelet threshold combined noise reduction to extract bottom wave characteristics, correcting the wall thickness of each measuring point based on temperature, generating a B-scan image and carrying out grading evaluation according to the thinning amount percentage. The invention realizes accurate wall thickness measurement under high temperature working condition, ensures stable signal through temperature self-adaptive parameter optimization, obviously improves signal to noise ratio by combining noise reduction, intuitively presents wall thickness distribution by B-scan imaging, and provides reliable data support for safe operation and maintenance of the pipeline.

Inventors

LI WEI
CHEN TIEJUN
CAI ZHENGCHUN
CHEN MIN
LIN YAOJIAN
XIE HUANBIAO
Zhu Binsha
TIAN XIAOXUAN
XU BOWEI
Sun Xiongbo
YANG QIANGBIN
CHEN SHENGGUANG
YOU LIANG
SHI YIMING
JI JIEHONG
LOU ZHENGJI
SUN QI
LI JUTAO
CHEN SHAOHUA

Assignees

西安热工研究院有限公司
华能(广东)能源开发有限公司海门电厂

Dates

Publication Date: 20260512
Application Date: 20260115
Priority Date: 20260112

Claims (10)

1. The soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption is characterized by comprising the following steps of: Configuring basic detection parameters according to the material characteristics and the design wall thickness of a pipeline to be detected, wherein the basic detection parameters comprise a body wave excitation frequency range, sampling frequency, mode decomposition iteration times and a wavelet noise reduction threshold; A temperature sensor is axially arranged along the pipeline, surface temperature data of a region to be detected of the pipeline are collected, and a representative temperature value is calculated according to the surface temperature data; Calculating the force contribution ratio of the Lorentz force and the magnetostriction force according to the representative temperature value and the electromagnetic characteristic parameter of the pipeline material, and dynamically optimizing the electromagnetic ultrasonic body wave excitation parameter based on the force contribution ratio; The method comprises the steps of moving an electromagnetic ultrasonic sensor point by point along the axial direction of a pipeline in a preset step length, emitting ultrasonic body waves at each measuring point perpendicular to the surface of the pipeline, collecting echo signals, and adding a time stamp to each signal point of the echo signals; sequentially performing empirical mode decomposition noise reduction and wavelet threshold noise reduction on the echo signals, and extracting bottom wave features from the noise-reduced signals; Calculating the flight time according to the time stamp of the bottom wave characteristic, and calculating the wall thickness value of each measuring point by combining the bulk wave sound velocity of the pipeline material at the representative temperature value; And mapping the wall thickness value of each measuring point into a gray value to generate a B-scan image, identifying a wall thickness abnormal region in the B-scan image, carrying out grading evaluation according to the wall thickness value and the thinning amount percentage of the designed wall thickness, and outputting an evaluation result.
2. The temperature-adaptive soot blowing pipeline wall thickness on-line monitoring method according to claim 1, wherein the bulk wave excitation frequency range is 1MHz to 5MHz; the sampling frequency is more than 3 times of the upper limit value of the bulk wave excitation frequency range; the range of the mode decomposition iteration times is 30 to 80 times; the wavelet noise reduction threshold ranges from 0.01V to 0.1V.
3. The temperature-adaptive soot blowing pipeline wall thickness on-line monitoring method according to claim 1, wherein the calculation mode of the representative temperature value is as follows: the surface temperature data acquired by a plurality of temperature sensors arranged along the axial direction of the pipe is arithmetically averaged.
4. The temperature-adaptive soot blowing pipeline wall thickness on-line monitoring method according to claim 1, wherein the calculation formula of the force contribution ratio is: ; Wherein, the For the representative temperature value The lower force contribution ratio is set, For the lower temperature limit to be a calibrated value, For the upper temperature limit to be a calibrated value, A force contribution ratio reference value corresponding to the lower temperature limit calibration value, And comparing the force contribution corresponding to the upper temperature limit calibration value with a reference value.
5. The temperature-adaptive soot blowing pipeline wall thickness on-line monitoring method according to claim 1, wherein the dynamically optimizing electromagnetic ultrasonic body wave excitation parameters comprises: Configuring an excitation current amplitude according to the ratio of lorentz forces in the force contribution ratio; configuring the strength of the externally applied magnetic field according to the ratio of the magnetostriction force in the force contribution ratio; And selecting the frequency which minimizes the ultrasonic attenuation coefficient of the pipeline material at the representative temperature value from the bulk wave excitation frequency range as a target excitation frequency.
6. The temperature-adaptive soot blowing pipeline wall thickness online monitoring method according to claim 1, wherein the ultrasonic wave is longitudinal wave, and the propagation direction is perpendicular to the surface of the outer wall of the pipeline; the ultrasonic body wave passes through the wall thickness of the pipeline and then reaches the inner wall of the pipeline to be reflected to form a bottom wave; The preset step size ranges from 0.5mm to 2mm.
7. The temperature-adaptive soot blowing pipeline wall thickness on-line monitoring method according to claim 1, wherein the specific process of empirical mode decomposition noise reduction is as follows: Decomposing the echo signal into a plurality of eigenmode functions and residual components; Calculating the energy ratio of each eigenmode function, and reserving eigenmode functions of which the energy ratio is accumulated to reach a preset energy threshold; and removing a high-frequency noise component and a low-frequency trend component with low energy ratio, and superposing the reserved eigen mode functions to obtain a preliminary denoising signal.
8. The online monitoring method for wall thickness of soot blowing pipeline based on temperature self-adaption according to claim 7, wherein the specific process of wavelet threshold noise reduction is as follows: carrying out multi-layer decomposition on the preliminary denoising signal by adopting a Symlet series wavelet base to obtain wavelet coefficients of each layer; Performing hard threshold processing on the wavelet coefficients of each layer, setting the coefficients with absolute values smaller than the wavelet noise reduction threshold to zero, and reserving the coefficients with absolute values larger than or equal to the wavelet noise reduction threshold; and reconstructing the processed wavelet coefficient through wavelet inverse transformation to obtain a pure echo signal.
9. The temperature-adaptive soot blowing pipeline wall thickness on-line monitoring method according to claim 1, wherein the wall thickness value is calculated according to the following formula: ; Wherein, the For the wall thickness value in question, Is made of pipeline material at the representative temperature value The velocity of sound of the longitudinal wave at the bottom, For the time of flight; the flight time is the difference between the time stamp of the bottom wave characteristic and the transmission moment of the bulk wave.
10. The temperature-adaptive soot blowing pipe wall thickness on-line monitoring method according to any one of claims 1 to 9, wherein the decision criteria of the hierarchical evaluation are: when the thinning amount percentage is smaller than a first preset threshold value, judging that the area is a normal area; When the thinning amount percentage is larger than or equal to the first preset threshold value and smaller than a second preset threshold value, judging as a light thinning area; And when the thinning amount percentage is larger than or equal to the second preset threshold value, judging as a serious thinning area and generating early warning information.

Description

Soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption Technical Field The invention relates to the technical field of nondestructive testing of industrial pipelines, in particular to a soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption. Background The soot blowing system is an auxiliary system of thermal equipment in the industries of thermal power plants, petrochemical industry and the like, and a pipeline of the soot blowing system runs under the severe working conditions of high temperature, high pressure and medium flushing for a long time. Due to the continuous corrosion of the working environment, the inner wall of the pipeline is extremely easy to be corroded and thinned, so that the wall thickness of the pipeline is gradually thinned. When the wall thickness is reduced to a critical value, the bearing capacity of the pipeline is greatly reduced, accidents such as pipeline rupture, medium leakage and the like can be caused, and the safe operation of equipment is threatened. The existing pipeline wall thickness detection method mainly relies on the conventional ultrasonic thickness measurement technology, and a piezoelectric ultrasonic probe is adopted to contact the outer wall of a pipeline for point-to-point measurement. However, such methods suffer from the following drawbacks: firstly, the piezoelectric probe needs a couplant to effectively transmit ultrasonic waves, and the couplant is easy to evaporate and lose efficacy in a high-temperature environment, so that detection is required to be carried out after shutdown and cooling, and the production continuity is seriously affected; Secondly, the acoustic characteristics of the material in a high-temperature environment are obviously changed, the ultrasonic propagation speed is changed along with the temperature change, and the traditional method cannot establish the association relation between the temperature and the excitation parameter, so that the detection signal is unstable under the high-temperature working condition and the measurement accuracy is reduced; Third, the traditional single noise reduction method is difficult to simultaneously eliminate complex electromagnetic interference and structural noise in a high-temperature environment, the identification precision of the micro corrosion thinning defect is insufficient, and the condition of omission is easy to occur. Therefore, an online monitoring method capable of realizing accurate wall thickness measurement in a high-temperature running state, having temperature self-adaptive parameter optimization capability and effectively identifying micro corrosion thinning defects is needed. Disclosure of Invention The invention aims to at least solve one of the technical problems in the prior art, and provides a soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption. The invention provides a soot blowing pipeline wall thickness on-line monitoring method based on temperature self-adaption, which comprises the following steps: Configuring basic detection parameters according to the material characteristics and the design wall thickness of a pipeline to be detected, wherein the basic detection parameters comprise a body wave excitation frequency range, sampling frequency, mode decomposition iteration times and a wavelet noise reduction threshold; A temperature sensor is axially arranged along the pipeline, surface temperature data of a region to be detected of the pipeline are collected, and a representative temperature value is calculated according to the surface temperature data; Calculating the force contribution ratio of the Lorentz force and the magnetostriction force according to the representative temperature value and the electromagnetic characteristic parameter of the pipeline material, and dynamically optimizing the electromagnetic ultrasonic body wave excitation parameter based on the force contribution ratio; The method comprises the steps of moving an electromagnetic ultrasonic sensor point by point along the axial direction of a pipeline in a preset step length, emitting ultrasonic body waves at each measuring point perpendicular to the surface of the pipeline, collecting echo signals, and adding a time stamp to each signal point of the echo signals; sequentially performing empirical mode decomposition noise reduction and wavelet threshold noise reduction on the echo signals, and extracting bottom wave features from the noise-reduced signals; Calculating the flight time according to the time stamp of the bottom wave characteristic, and calculating the wall thickness value of each measuring point by combining the bulk wave sound velocity of the pipeline material at the representative temperature value; And mapping the wall thickness value of each measuring point into a gray value to generate a B-scan image, identifying a wall thickness abnormal region