Acoustic Detection of Pipeline Blockages in Gas Extraction Systems: A Novel Approach

Abstract

Gas extraction is crucial for coal mine safety, yet pipeline blockages by solid slag and water severely hinder efficiency and pose risks. Traditional detection methods are limited by rapid signal attenuation and noise interference. In this study, an acoustic detection technology is introduced for pipeline blockages, utilizing sensors at potential blockage points to collect sound wave data. Experiments with a scaled pipeline model reveal that slag blockages produce characteristic peaks in the 1200 Hz–2000 Hz range, while water blockages show peaks in the 1 kHz–2 kHz and 3.5 kHz–4.5 kHz bands. The longitudinal blockage intensity and extraction pressure significantly affect the sound pressure levels. A reliable fitting model predicts the blockage intensity based on acoustic signals, achieving high accuracy. This novel method enhances blockage identification, offering a non-invasive, cost-effective solution that improves coal mine safety and efficiency.

1. Introduction

Gas extraction is a critical process in coal mining to prevent gas disasters and ensure production safety [1,2,3,4,5,6]. However, blockages often occur in the extraction pipelines, particularly due to solid slag and water accumulation, which significantly affects the efficiency and stability of gas extraction [7,8,9]. Slag blockages are primarily caused by the accumulation of solid particles such as coal slag and rock fragments. These particles can originate from the fragmentation of the coal seam itself or detach due to vibration and friction within the pipelines during extraction. As these particles accumulate, they form blockages that decrease or even completely interrupt gas flow. Water blockages, on the other hand, are mainly caused by accumulated water in the pipelines. The damp environment and the high water content of the coal seam lead to water being pumped into the pipeline during extraction. The condensation of water molecules due to variations in temperature and pressure results in the accumulation of liquid water or water vapor. When this accumulation reaches a certain level, it blocks the gas flow. Both slag and water blockages reduce the efficiency of gas extraction and pose serious safety risks. Blockages can cause an abnormal increase in pipeline pressure, raising the risk of rupture. Additionally, accumulated blockages may lead to fires or explosions. Therefore, in-depth research on the causes, characteristics, and prevention of slag and water blockages in underground gas extraction pipelines is crucial for improving extraction efficiency and ensuring safety in coal mines.

Various detection methods have been proposed by scholars to accurately identify and resolve pipeline blockages, including transmission detection [10,11,12], strain measurement [13,14], vibration analysis [15,16], ultrasonic guided waves [17,18,19], and acoustic reflection [20,21,22,23,24]. Among these, acoustic detection is highly regarded for its non-invasive, real-time, high-sensitivity, and cost-effective advantages [25,26]. It adapts to various pipeline configurations and media, such as oil, gas, and water. Papadopoulou [21] detected the pipeline blockages and leaks through the fluid transmission speed based on acoustic signals, providing an effective method for remote pipeline blockages and leaks. Zhu et al. [27] proposed a multiple-blockage identification scheme for buried pipelines using an acoustic signature model and the lightweight SqueezeNet deep learning network. They utilized indirect sound intensity and dynamic threshold techniques to segment acoustic signature images, which were then used to train SqueezeNet for feature extraction. Their model achieved high accuracy in recognizing both known and unknown blockage types, demonstrating robust adaptability and generalization in complex operating conditions. This method significantly improves blockage detection by reducing dependency on extensive labeled data and manual feature extraction. An et al. [28] introduced a novel method for the online monitoring of natural gas pipeline safety, focusing on the detection and location of hydrate blockages and leaks using acoustic pulse compression and envelope extraction techniques. They utilized linear frequency modulation (LFM) signals and matched the filtering to process the reflected acoustic signals, achieving high sensitivity and precision in identifying and locating abnormalities. Their approach demonstrated significant improvements in spatial resolution and noise suppression, enhancing the performance of existing pipeline safety monitoring systems. Wang et al. [29] utilized acoustic reflectometry to detect and locate hydrate and other blockages in long gas-filled pipelines. The study demonstrated that this technique accurately identifies blockages in industrial pipelines, even under varying pressure and flow conditions. The method shows promise for the early detection and remediation of blockages, crucial for maintaining pipeline safety and efficiency. Jing et al. [23] proposed an approximate inverse scattering technique to reconstruct blockage profiles in water pipelines using acoustic transients. They utilized the Born approximation to achieve good accuracy with low computational complexity. Through numerical simulations and experimental results, the technique demonstrated reliable performance for detecting both mild and severe blockages, offering an explicit reconstruction formula based on measured acoustic reflectivity. Time-frequency analysis and the boundary element method were employed by Kim et al. [30] to obtain the time-frequency characteristics of sound waves inside pipelines. Gao et al. [31] designed and built a simulation test bench for valve leakage faults to study the relationship between the characteristic parameters of acoustic emission signals and the leakage state. The optimal parameters for characterizing the valve leakage fault state have been found. Quy et al. [32] proposed a filtering technique to enhance the accuracy of leak location based on acoustic emission burst monitoring. A sound wave processing method was proposed by Abdullahi et al. [24], which adopts the propagation time of sound waves to effectively identify pipeline blockages and location.

However, the attenuation of sound wave reflections is relatively fast, and the high noise environment in mines can cause significant disturbance to the reflection signals, seriously affecting the detection accuracy. Therefore, a detection technology for slag blockage and water blockage in gas extraction pipelines is proposed based on sound signals. The sound wave detection sensor is pasted at the possible blockage locations to collect sound wave signals inside the pipeline. Based on the characteristics of the sound wave signals, it can be determined whether the blockage exists in the extraction pipeline, effectively strengthening the efficiency of blockage identification. As a result, it guarantees the safety of coal mine production and pipeline operation, minimizing safety hazards and losses caused by blockages, and has important practical significance.

2. Materials and Methods

2.1. Principle of Acoustic Detection

Aeroacoustics indicate that noise can be generated through turbulent fluid motion or aerodynamic forces interacting with surfaces. Currently, most practical aeroacoustic analyses rely on the aeroacoustic analogy proposed by James Lighthill in the 1950s. According to this analogy, the sound wave equation for a moving fluid can be represented by Equation (1) [33], as follows:

$${\displaystyle \frac{{\partial }^2\rho }{\partial t^2}}-c_0^2{\nabla }^2\rho ={\displaystyle \frac{{\partial }^2{T}_{ij}}{\partial x_i{x}_j}}$$

(1)

where $\rho$ is the density of fluid; t is time; $T_{ij}=\rho u_i{u}_j+p{\delta }_{ij}+{\tau }_{ij}-c_0^2\rho {\delta }_{ij}$ is called Lighthill’s turbulence stress tensor; u is the velocity; p is the pressure; ${\delta }_{ij}$ is the Kronecker symbol; and ${\tau }_{ij}$ is the fluid viscous stress tensor.

According to Equation (1), the sound waves originate from the changes in fluid velocity and pressure gradient. When the fluid encounters the blockage, the flow rate will decrease while the pressure will increase, generating the pressure waves, namely sound waves. When the pipeline becomes blocked, the increased pressure near the blockage point generates sound waves. Additionally, the blockage reduces the effective diameter of the pipeline, which brings about a higher gas flow rate around the blockage point. Due to the velocity fluctuation, the violent friction between the gas and the pipe wall or the blockage can generate sound waves. Therefore, the frequency spectrum variation in airflow sound waves inside the pipeline is monitored to determine whether the pipeline is blocked.

2.2. Experimental Apparatus

Based on the similarity principle, the mode should be designed reasonably to make the observed data and variations obtained in laboratory blockage detection closer to the prototype. According to the similarity theory, mechanical similarity is an important link between the prototype and the model. Only when based on mechanical similarity can the relevant hydraulic calculations be carried out, including geometric similarity, kinematic similarity, and dynamic similarity.

2.2.1. Geometric Similarity

Geometric similarity refers to maintaining a certain proportion between the length l and the area A of the corresponding segment in the respective flow fields of the prototype and model. The relationship is expressed as follows:

$${\lambda }_l={\displaystyle \frac{l_p}{l_m}}$$

(2)

where l_p is the length of the prototype pipeline and l_m is the length of the model pipeline, m;

$${\lambda }_A={\displaystyle \frac{A_p}{A_m}}={\lambda }_l^2$$

(3)

where A_p is the area of the prototype pipeline and A_m is the area of the model pipeline, m².

In the flow field, the pipe length and pipe diameter satisfy the length ratio of geometric similarity, which can be presented as follows:

$${\lambda }_1={\displaystyle \frac{l_p}{l_m}}$$

(4)

$${\lambda }_{\mathrm{d}}={\displaystyle \frac{d_{\mathrm{p}}}{d_{\mathrm{m}}}}$$

(5)

2.2.2. Kinematic Similarity

Kinematic similarity refers to the relationship where the ratio of physical quantities of kinematics at corresponding points in the prototype and model flow fields is constant, with the same direction. In the flow field, the time, velocity, and flow ratio are discussed, which can be expressed as follows:

$${\lambda }_{\mathrm{t}}={\displaystyle \frac{t_{\mathrm{p}}}{t_{\mathrm{m}}}}$$

(6)

where t_p and t_m represent the time when gas flows through the corresponding flow fields of prototype and model, s.

$${\lambda }_v={\displaystyle \frac{v_{\mathrm{p}}}{v_{\mathrm{m}}}}={\displaystyle \frac{{\lambda }_l}{{\lambda }_t}}$$

(7)

where v_p and v_m denote the velocity when gas flows through the corresponding flow fields of the prototype and model, m/s.

The similarity criteria for similar experiments are shown in

Table 1. Correspondence between experimental model and prototype parameters.

Physical Quantity	Prototype	Equation	Type
Diameter of the branch pipe (mm)	100	${\lambda }_d={\displaystyle \frac{d_p}{d_m}}=2$	50
Length of the transverse section of the branch pipe (m)	2	${\lambda }_l={\displaystyle \frac{l_p}{l_m}}=2$	1
Flow rate	2	$v_m={\displaystyle \frac{{\lambda }_d^2}{{\lambda }_1{\lambda }_{\gamma }}}{v}_p$	2

.

The experimental system mainly consists of a simulation system for blocking slag and water in extraction pipelines, and a sound wave detection system. The extraction main pipeline, extraction branch pipeline, and gas manifold are composed of the simulation system, and the sound wave sensor, acquisition card, and PC are mainly included in the detection system. An ACE1001 Acoustic Vibration Analyzer (Hangzhou Aihua instrument Co., Ltd., Hangzhou, China) was utilized in this study, which offers high sensitivity and precise frequency detection capabilities suitable for detecting subtle variations in acoustic signals within pipelines. The device has a sensitivity rating of 30 mV/Pa and a broad frequency response from 10 Hz to 20 kHz, enabling it to capture a wide spectrum of sound wave frequencies generated by both blockages and unobstructed flow. Calibration of the analyzer was conducted before each experimental set to ensure consistent and reliable data capture. The system is shown in Figure 1.

In the experimental system, gas flows from gas extraction boreholes through the manifold, branch pipes, main pipelines, and gas storage tanks to the water tank. Moreover, the gas storage tank is utilized to stabilize the pressure of the test pipeline system. The pressure adjustment of the experimental system can be achieved by changing the pressure of the gas storage tank, which is adjusted by the PLC control system inside the vacuum pump control box. The experimental system designs four gas extraction boreholes, and each is equipped with a flow control valve, which can adjust the gas extraction flow rate of each borehole. Meanwhile, the flow rate of the extraction main pipeline and branch pipeline can be adjusted through the flow control valves.

The entire experimental system is 5 m long, and the room temperature is regarded as a working temperature. The main pipeline size is DN200 with a length of 3.6 m, the branch pipeline size is DN50, and the length of the transverse section is 1 m. The system can control the flow rate, negative pressure, and blockage of the extraction pipeline.

In order to eliminate the influence of environmental acoustic noise, an advanced dual-channel acoustic signal collector was utilized in the experiment, capable of precisely capturing both the internal acoustic signals of the pipeline and the surrounding environmental noise. This method allows for a more comprehensive and detailed understanding of the actual conditions of the pipeline’s acoustic signals and environmental noise.

In the specific operation, the internal acoustic signals of the pipeline were first collected. These signals often contain crucial information about the pipeline’s operating status and potential blockages. Simultaneously, environmental noise was also collected. The next step involved calculating the difference between the pipeline noise and the environmental noise. The key to this step is accurately identifying and separating the pipeline acoustic signals from the environmental noise signals, thereby obtaining more accurate pipeline acoustic data. These processed acoustic data were then subjected to spectral analysis. By comparing parameters such as acoustic intensity and waveform characteristics at different frequencies, it was possible to determine the presence of blockages or other issues.

2.3. Experimental Scheme

To study the influence of longitudinal and transverse blockage intensity on the acoustic source characteristics of a gas extraction pipeline blockage, different blockage degrees are set to conduct the acoustic detection experiment. When the system is operating, the pressure inside the extraction pipeline is monitored, and the acoustic signal is collected as soon as the extraction negative pressure reaches the predetermined value. Blockage length, blockage height, and extraction negative pressure are selected as the influencing factors. There are six levels of blockage height, namely, 0.625 cm, 1.25 cm, 1.875 cm, 2.5 cm, 3.125 cm, and 3.8 cm; four length levels for blockage, i.e., 5 cm, 10 cm, 15 cm, and 20 cm; and four levels of extraction negative pressure, namely, 12 kPa, 24 kPa, 36 kPa, and 48 kPa.

To quantify the impact of blockage height on wave sound in blocked pipelines, the ratio of pipeline blockage area to pipeline cross-section area is defined as the longitudinal blockage intensity, y. The pipeline longitudinal blockage section is presented in Figure 2, and the calculation formula is given in Equation (10).

$$y={\displaystyle \frac{S_b}{S_p}}$$

(8)

$$S_b={\displaystyle {\int }_{{\mathrm{x}}_1}^{{\mathrm{x}}_2}(\mathrm{h}-\mathrm{r}+\sqrt{r^2-{(x-r)}^2}})dx$$

(9)

$$y={\displaystyle \frac{{\displaystyle {\int }_{{\mathrm{x}}_1}^{{\mathrm{x}}_2}(\mathrm{h}-\mathrm{r}+\sqrt{r^2-{(x-r)}^2}})dx}{\pi r^2}}$$

(10)

where $S_b$ and $S_p$ are the blockage area and cross-section area of the pipeline, respectively, in cm²; r denotes the radius of the pipeline, cm; h is the height of pipeline blockage, cm.

Table 2. Longitudinal blockage intensity for different blockage heights.

Number	1	2	3	4	5	6
Blockage height h (cm)	0.625	1.25	1.875	2.5	3.125	3.75
Longitudinal blockage intensity y	0.07	0.2	0.34	0.5	0.66	0.8

shows the longitudinal blocking intensity at different blocking heights.

A 1/2 scaled model is employed to simulate the actual pipeline system, where the length of the transverse section of the branch pipe is approximately 1 m. In order to investigate the influence of blockage length on acoustic signals in pipelines, four different levels of blockage lengths are set within the range of 0.05 m to 0.2 m. To quantify the blockage effect, the parameter x of transverse blockage intensity is introduced, which is the ratio of blockage length to the length of the transverse section of the branch pipe. The calculation method can refer to Equation (11). The correspondences between the set values for transverse blockage intensity and different blockage lengths are listed in

Table 3. Correspondence between the transverse blockage intensity and the blockage length.

Number	1	2	3	4
Blockage length L (cm)	5	10	15	20
Transverse blockage intensity x	0.05	0.1	0.15	0.2

.

$$x={\displaystyle \frac{L_d}{L}}$$

(11)

where L_d is the length of the blocked pipeline, cm and L is the length of the transverse pipeline, cm.

In summary, transverse blockage strength, longitudinal blockage strength, and extraction negative pressure are considered. Through the orthogonal experimental design, the experiment can be divided into 33 groups, as shown in

Table 4. Extraction pipeline blockage experimental conditions.

Number	Extraction Pressure (kPa)	Longitudinal Blocking Intensity	Transverse Blocking Intensity
1	12	0.07	0.05
2	12	0.2	0.05
3	12	0.34	0.05
4	12	0.5	0.05
5	12	0.66	0.05
6	12	0.8	0.05
7	24	0.07	0.05
8	24	0.2	0.05
9	24	0.34	0.05
10	24	0.5	0.05
11	24	0.66	0.05
12	24	0.8	0.05
13	36	0.07	0.05
14	36	0.2	0.05
15	36	0.34	0.05
16	36	0.5	0.05
17	36	0.66	0.05
18	36	0.8	0.05
19	48	0.07	0.05
20	48	0.5	0.05
21	48	0.34	0.05
22	48	0.5	0.05
23	48	0.66	0.05
24	48	0.8	0.05
25	24	0.07	0.1
26	24	0.07	0.15
27	24	0.07	0.2
28	36	0.07	0.1
29	36	0.07	0.15
30	36	0.07	0.2
31	48	0.07	0.1
32	48	0.07	0.15
33	48	0.07	0.2

. To ensure the accuracy of the experiment, each working condition is collected twice and 66 signal acquisition tests are conducted for the pipeline blockage. The parameter settings under different experimental conditions are listed in detail, which ensures the comprehensiveness and accuracy of the experiment. The impact of these factors on pipeline acoustic signals are further explored to provide more reliable basis for practical engineering applications.

3. Results

3.1. Effect of Pipeline Slag Blockage on Acoustic Signals

3.1.1. Influence of Different Extraction Negative Pressures on the Frequency Domain of Acoustic Signals for Pipeline Slag Blockage

Figure 3 shows the acoustic spectrum under a transverse blockage intensity of 0.05, a longitudinal blockage intensity of 0.07, and extraction pressures of 12 kPa, 24 kPa, 36 kPa, and 48 kPa. According to the spectrum, it can be seen that when the extraction negative pressure is 12 kPa, the peak point of the sound wave signal is 3.8 dB, and its frequency is 1574 Hz; when the extraction negative pressure is 24 kPa, the peak point of the sound wave signal is 4.6 dB, and its frequency is 1437 Hz. Meanwhile, the sound pressure levels of sound waves increase in the frequency range of 1.5~2.5 kHz; when the extraction negative pressure is 36 kPa, the peak point of the acoustic signal is 6.4 dB, and its frequency is 1293 Hz; when the extraction negative pressure is 48 kPa, the peak point is 11.2 dB, and its frequency is 1473 Hz. As the extraction negative pressure increases from 12 kPa to 48 kPa, the peak sound pressure level presents an increasing trend. Compared to the peak at 12 kPa, the peak level increases by 21.1%, 68.4%, and 194.7%. The frequency value of the acoustic peak point fluctuates within the range of 1293 Hz to 1574 Hz, and the sound pressure level at the peak point exhibits an increasing trend.

3.1.2. Influence of Different Transverse Blockage Intensities on the Frequency Domain of Acoustic Signals for Pipeline Slag Blockage

Figure 4 shows the acoustic frequency spectrum of different transverse blockage intensities under a longitudinal blockage intensity of 0.07 and an extraction pressure of 12 kPa. A blockage intensity of 0 indicates a sound wave curve without blockage. Note that the sound wave curves in this figure and the following figures have subtracted the influence of environmental noise. Based on the spectrum diagram, when the transverse blockage intensity is 0.05, the peak point of the sound wave signal is 3.8 dB at a frequency of 1574 Hz; when the transverse blockage intensity is 0.1, the peak point is 4.1 dB at a frequency of 1792 Hz; when the transverse blockage intensity is 0.15, the peak point of the sound wave signal is 4 dB at a frequency of 1684 Hz, when the transverse blockage intensity is 0.2, the peak point of the sound wave signal is 4 dB at a frequency of 1483 Hz. As the transverse blockage intensity rises, the variation in peak sound pressure level is not significant, and even the peak sound pressure level shows a reduction trend. The frequency value of the acoustic peak point fluctuates within the range of 1483 Hz–1792 Hz, indicating that the change in transverse blockage intensity has little effect on the peak point and its frequency.

3.1.3. Influence of Different Longitudinal Blockage Intensities on the Frequency Domain of Acoustic Signals for Pipeline Slag Blockage

Figure 5 shows the acoustic frequency spectrum of different longitudinal blockage intensities under a transverse blockage intensity of 0.05 and an extraction pressure of 12 kPa. According to the spectrum, when the longitudinal blockage intensity is 0.07, the peak point of the acoustic signal is 3.8 dB, and its frequency is 1574 Hz; when the longitudinal blockage intensity is 0.2, the peak point is 4.9 dB, and its frequency is 1373 Hz; when the longitudinal blockage intensity is 0.34, the peak point is 8 dB, and its frequency is 1742 Hz; when the longitudinal blockage intensity is 0.5, the peak point is 9.6 dB, and its frequency is 1379 Hz; when the longitudinal blockage intensity is 0.66, the peak point of the acoustic signal is 14 dB, and its frequency is 1265 Hz; when the longitudinal blockage intensity is 0.8, the peak point is 18 dB, and its frequency is 1573 Hz. As the longitudinal blockage intensity increases, the peak sound pressure level gradually increases, and the growth rate also gradually increases. The frequency value of acoustic peak fluctuates within the range of 1265 Hz–1742 Hz.

Table 5. Frequency range of the sound source signal from slag blockage.

Extraction Pressure/kPa	Transverse Blockage Intensity	Longitudinal Blockage Intensity
		0.07	0.2	0.34	0.5	0.66	0.8
12	0.05	1574	1373	1742	1379	1265	1573
	0.1	1792	1532	1742	1544	1371	1742
	0.15	1684	1399	1592	1395	1264	1635
	0.2	1483	1535	1733	1853	1784	1593
24	0.05	1437	1537	1841	1732	1854	1538
	0.1	1554	1415	1392	1843	1485	1854
	0.15	1743	1534	1754	1646	1854	1639
	0.2	1853	1734	1954	1854	1643	1843
36	0.05	1293	1539	1643	1965	1854	1754
	0.1	1483	1343	1531	1643	1645	1543
	0.15	1385	1481	1854	1853	1855	1593
	0.2	1631	1836	1483	1649	1956	1692
48	0.05	1473	1743	1855	1476	1753	1775
	0.1	1645	1942	1632	1854	1864	1945
	0.15	1591	1727	1843	1643	1459	1723
	0.2	1839	1541	1642	1858	1688	1654

summarizes the sound pressure level and frequency at the peak point from the frequency domain of sound waves under different operating conditions. It can be observed that compared to the longitudinal blockage intensity, the transverse blockage intensity has a minor impact on the sound pressure level and the frequency at the peak point. In the process of pipeline slag blockage detection, the influence of the transverse blockage intensity on the frequency domain of sound waves can be ignored.

Based on the experiments, when the slag blockage occurs in the gas extraction pipeline, there exists a peak point in the frequency range of the sound signal, with the range of 1200 Hz–2000 Hz, which is the characteristic frequency band of the pipeline blockage. As the slag blockage increases, the frequency of the characteristic peak from the slag blockage sound wave signal fluctuates within this range. Overall, the total amplitude in the characteristic frequency band is positively correlated with the transverse and longitudinal slag blockage intensity. As the slag blockage increases, the effective pipe diameter at the blockage point decreases, and the gas flow rate accelerates, which brings about severe friction between the gas and the coal slag at the blockage point. Consequently, the amplitude of the sound wave also increases. However, the sound pressure level and frequency of the maximum peak point are not clearly related to the transverse and longitudinal blockage intensity.

3.2. Effect of Pipeline Water Blockage on Acoustic Information

3.2.1. Effect of Different Extraction Negative Pressures on the Frequency Domain of Pipeline Acoustic Signals

Figure 6 presents the acoustic frequency spectrum under a longitudinal blockage intensity of 0.07, a transverse blockage intensity of 0.05, and extraction pressures of 12 kPa, 24 kPa, 36 kPa, and 48 kPa. According to the spectrum, there are two characteristic frequency bands when a water blockage occurs in the pipeline, and the sound pressure level at the peak point is higher in the two frequency bands. When the extraction negative pressure is 12 kPa, the peak points of the acoustic signal are 5.6 dB and 3.5 dB, with frequencies of 1635 Hz and 3844 Hz, respectively; when the extraction negative pressure is 24 kPa, the peak points of the acoustic signal are 10 dB and 6 dB, and their frequencies are 1583 Hz and 3972 Hz; when the extraction negative pressure is 36 kPa, the peak points of the acoustic signal are 15 dB and 14 dB, with frequencies of 1872 Hz and 4183 Hz; when the extraction negative pressure is 48 kPa, the peak points of the acoustic signal are 21 dB and 17 dB, and their frequencies are 1734 Hz and 4062 Hz. As the extraction negative pressure increases from 12 kPa to 48 kPa, the sound pressure level at the peak point exhibits an increasing trend. Compared with 12 kPa, the sound pressure levels at the two peak points increase by 69.7%, 167.9%, 385.7%, and 71.4%, 167.9%, and 275%, respectively. The frequency values of peak point 1 and peak point 2 fluctuate in the range of 1635 Hz~1872 Hz and 3844 Hz~4183 Hz, and the sound pressure level at the peak point tends to increase.

3.2.2. Effect of Different Transverse Blockage Intensities on the Frequency Domain of Pipeline Acoustic Signals

Figure 7 shows the acoustic frequency spectrum under a negative pressure of 12 kPa, a longitudinal blockage intensity of 0.07, and transverse blockage intensities of 0.05, 0.1, 0.15, and 0.2. When the transverse blockage intensity is 0.05, the peak points of the two characteristic frequency bands of the acoustic signals are 5.6 dB and 3.5 dB, with frequencies of 1635 Hz and 3844 Hz; when the transverse blockage intensity is 0.1, the peak points are 5.9 dB and 4.6 dB, with frequencies of 1588 Hz and 3973 Hz; when the transverse blockage intensity is 0.15, the peak points of the acoustic signal are 7.1 dB and 5.7 dB, with frequencies of 1748 Hz and 4127 Hz; when the transverse blockage intensity is 0.2, the peak points are 9.3 dB and 7.8 dB, with frequencies of 1683 Hz and 4352 Hz. As the transverse blockage intensity rises, the sound pressure level at the peak point gradually rises, and the rate also gradually accelerates. The frequency values of peak point 1 and peak point 2 fluctuate within the range of 1588 Hz~1748 Hz and 3844 Hz~4352 Hz. An increase in transverse blockage intensity has relatively little influence on the sound pressure level at the peak point of the acoustic signal.

3.2.3. Effect of Different Longitudinal Blockage Intensities on the Frequency Domain of Pipeline Acoustic Signals

Figure 8 shows the acoustic frequency spectrum under an extraction negative pressure of 12 kPa, a transverse blockage intensity of 0.05, and different longitudinal blockage intensities. When the longitudinal blockage intensity is 0.07, the peak points of the two characteristic frequency bands of the sound wave signals are 5.6 dB and 3.5 dB, respectively, with frequencies of 1635 Hz and 3844 Hz; when the longitudinal blockage intensity is 0.2, the peak points are 7.8 dB and 4.4 dB, with frequencies of 1773 Hz and 3912 Hz; when the longitudinal blockage intensity is 0.34, the peak points of the acoustic signals are 10.1 dB and 5.1 dB, and their frequencies are 1842 Hz and 4437 Hz; when the longitudinal blockage intensity is 0.5, the peak points are 12.2 dB and 6.3 dB, and their frequencies are 1732 Hz and 4285 Hz; when the longitudinal blockage intensity is 0.66, the peak points are 31 dB and 7.6 dB, with frequencies of 1573 Hz and 4187 Hz; when the longitudinal blockage intensity is 0.8, the peak points of the acoustic signals are 14.2 dB and 8.4 dB, with frequencies of 1686 Hz and 3984 Hz. As the longitudinal blockage intensity rises, the sound pressure level at the peak point gradually increases, and the rate also gradually grows. The frequency values of peak point 1 and peak point 2 of the sound wave fluctuate within the range of 1635 Hz~1842 Hz and 3844 Hz~4437 Hz.

It can be observed that the influence of transverse blockage intensity on the amplitude of sound pressure level is negligible, while on the contrary, longitudinal blockage intensity and extraction pressure are important factors determining the amplitude of sound pressure level.

The frequency response range of sound source signals under different pressures and longitudinal blockage intensities is illustrated in

Table 6. Frequency values of sound source signals at the peak point under the same water blockage.

Extraction Pressure/kPa	Transverse Blockage Intensity	Longitudinal Blockage Intensity
		0.07	0.2	0.34	0.5	0.66	0.8
12	0.05	1635/3844	1893/3422	1641/4128	1834/3847	1537/3837	1684/3742
	0.1	1588/3973	1532/3572	1742/3392	1544/3274	1371/3932	1742/3284
	0.15	1748/4127	1399/3933	1592/4492	1395/3348	1264/3741	1635/3371
	0.2	1683/4352	1535/3972	1733/4238	1853/3853	1784/3429	1593/3736
24	0.05	1583/3559	1537/3843	1841/3231	1732/3583	1854/3854	1538/4832
	0.1	1554/3384	1415/4283	1392/3652	1843/3823	1485/4954	1854/3865
	0.15	1743/3583	1534/3549	1754/3833	1646/3422	1854/3532	1639/3483
	0.2	1853/3329	1734/3384	1954/3248	1854/3329	1643/3969	1843/3549
36	0.05	1872/4248	1539/3432	1643/3867	1965/3482	1854/3439	1754/4392
	0.1	1483/3438	1343/3592	1531/3482	1643/3283	1645/3852	1543/3853
	0.15	1385/3339	1481/3483	1854/3833	1853/3853	1855/3328	1593/3854
	0.2	1631/3832	1836/3843	1483/4382	1649/3932	1956/3958	1692/3332
48	0.05	1734/3849	1743/3932	1855/3953	1476/3773	1753/4272	1775/3543
	0.1	1645/4829	1942/4289	1632/3826	1854/3823	1864/3472	1945/3864
	0.15	1591/3943	1727/3285	1843/3747	1643/3953	1459/3572	1723/3993
	0.2	1839/3459	1541/4392	1642/3526	1858/4273	1688/3972	1654/3643

. When a water blockage occurs in the gas extraction pipeline, the frequency range of the sound source signals is distributed between 1 kHz~2 kHz and 3.5 kHz–4.5 kHz. As a result, the frequency response range varies with the blockage. When the extraction pressure remains unchanged and the blockage increases, there is a remarkable increasing trend in the frequency of the sound signal at the peak point. Additionally, as the blockage remains unchanged and the extraction pressure rises, the frequency of the sound signal at the peak point also increases.

Based on the analysis of experimental results, it was concluded that when a water blockage occurs in the pipeline, there are two obvious peak points in the sound signal spectrum, mostly located in the characteristic frequency band 1 (1 kHz–2 kHz) and the characteristic frequency band 2 (3.5 kHz–4.5 kHz). The influence of the longitudinal and transverse blockage intensity on the peak points of the characteristic frequency band is relatively slight, but it has a great impact on the average amplitude of sound waves within the characteristic frequency band. With the increase in longitudinal and transverse blockage intensity, the average amplitude within the characteristic frequency range also exhibits a significant upward trend.

4. Discussion

The parameter analysis method will be adopted to provide an in-depth analysis of the generation characteristics of the sound source signals from water and slag blockage. A dual-channel sound signal collector is employed to simultaneously collect the environmental noise and the blockage sound signals inside the pipeline. The difference between the sound signals and the environmental sound signals is calculated, integrated, and then averaged, as follows:

$$F_{average}={\displaystyle \frac{{\displaystyle {\int }_{x_1}^{x_2}Fdx}}{\left(x_2-x_1\right)}}$$

(12)

where $F_{average}$ represents the amplitude of the sound wave in the frequency domain and $x_1$ and $x_2$ denote the frequency range of the characteristic frequency band.

4.1. Parameter Characteristics of Acoustic Signal for Slag Blockage in Gas Extraction Pipelines

4.1.1. The Influence of Extraction Pressure on Mean Value of Sound Pressure Level in Characteristic Frequency Bands for Slag Blockage

Under the transverse blockage intensity of 0.05 and longitudinal blockage intensity of 0.07, the acoustic signal data of the slag blockage at 12 kPa, 24 kPa, 36 kPa, and 48 kPa are analyzed. To ensure the accuracy of the experimental data, each working condition is measured twice to obtain the variation in the average value of the characteristic frequency domain from the pipeline slag blockage acoustic signals, as shown in Figure 9. It can be seen that as the extraction pressure increases, the average value of the characteristic frequency band also increases, indicating that the energy value of the sound signals is positively related with the extraction pressure. When the extraction pressure is 12 kPa, the average amplitude of the characteristic frequency band is 3.8 dB; when the extraction pressure increases to 24 kPa, the average amplitude of the characteristic frequency band is 4.5 dB, which is 18.4% higher than the average amplitude of the characteristic frequency band of 12 kPa; when the pressure is 36 kPa, the average amplitude is 5.9 dB, which is 55.3% higher than that of the 12 kPa characteristic frequency band; when the pressure is 48 kPa, the average amplitude is 7.7 dB, which has increased by 102.6% compared to the average amplitude of the 12 kPa.

4.1.2. The Influence of Transverse Blockage Intensity on Mean Value of Sound Pressure Level in Characteristic Frequency Bands for Slag Blockage

Under the pressure of 12 kPa and longitudinal blockage intensity of 0.07, the acoustic signal data of slag blockage at transverse blockage intensities of 0.05, 0.1, 0.15, and 0.2 are analyzed, as presented in Figure 10. It can be observed that as the transverse blockage intensity rises, the average amplitude of the characteristic frequency band also rises, but the growth rate is relatively small. When the transverse blockage intensity is 0.05, the average amplitude in the characteristic frequency band is 4.2 dB. As the transverse blockage intensity increases to 0.1, the average amplitude is 4.2 dB, a 5.8% increase compared to the 0.05 transverse blockage intensity. When the transverse blockage intensity is 0.15, the average amplitude is 4.3 dB, a 7.8% increase compared to the 0.05 transverse blockage intensity. As the transverse blockage intensity is 0.2, the average amplitude in the characteristic frequency band is 4.5 dB, which has increased by 13.7% compared to the 0.05 transverse blockage intensity.

The blockage of gas extraction pipelines is attributed to the pressure pulsation and the hindering effect of the blockage point. As the degree of blockage changes, the size of the gas flow channel at the blockage location will also vary, which causes the differences in the reaction degree of pressure pulsation. As the gas flows from the upstream of the blockage point towards the blockage point, the gas collides with the blockage point and is reflected. Meanwhile, the process is accompanied by an unstable flow state of the gas, including vibration, collision, or sudden expansion. Reynolds stress or shear force occurs in the unstable flow states, which forms turbulence and leads to aerodynamic noise. Furthermore, the more reflections there are, the greater the energy of aerodynamic noise.

4.1.3. The Influence of Longitudinal Blockage Intensity on Mean Value of Sound Pressure Level in Characteristic Frequency Bands for Slag Blockage

Under the pressure of 12 kPa and a transverse blockage intensity of 0.05, the slag blockage sound wave signal data at six longitudinal blockage intensities (0.07, 0.2, 0.34, 0.5, 0.66, 0.8) are analyzed, as presented in Figure 11. It can be seen that as the longitudinal blockage intensity increases, the average amplitude of the characteristic frequency band also rises, and the amplitude grows relatively significant. When the longitudinal blockage intensity is 0.07, the average amplitude in the characteristic frequency band is 4.2 dB. As the longitudinal blockage intensity increases to 0.2, the average amplitude is 5.2 dB, which has increased by 23.8% compared to the longitudinal blockage intensity of 0.07. As the longitudinal blockage intensity is 0.34, the average amplitude is 6.9 dB, a 64.3% increase compared to the 0.07 longitudinal blockage intensity. When the longitudinal blockage intensity is 0.5, the average amplitude is 9.3 dB, a 121.4% increase compared to the 0.07 longitudinal blockage intensity. As the longitudinal blockage intensity is 0.66, the average amplitude is 11.7 dB, which is 178.6% high than the 0.07 longitudinal blockage intensity. When the longitudinal blockage intensity is 0.8, the average amplitude is 13.6 dB, which has increased by 223.8% compared to the 0.07 longitudinal blockage intensity. It is mainly related to the pressure pulsation and the hindering effect of the blockage point. When a gas extraction pipeline is blocked, the size of the gas flow channel is different owing to the different degree of blockage at the blockage point, which results in different reaction degree of pressure pulsation.

An in-depth analysis is conducted on the characteristics of the acoustic signals inside pipelines under different longitudinal and transverse blockage intensities, as well as extraction negative pressure. It was concluded that the extraction pressure, the longitudinal blockage intensity in the pipeline, and the average amplitude of the characteristic frequency band have a significant functional relationship. Accordingly, the average amplitude of the characteristic frequency band will change with the extraction pressure or longitudinal blockage intensity. The transverse blockage intensity has a relatively minor impact on the average amplitude of the characteristic frequency band of the sound source signals. Therefore, in the subsequent analysis, this factor will be temporarily ignored, and the main focus will be on the influence of longitudinal blockage intensity and negative extraction pressure on the average sound pressure level in the characteristic frequency band. However, when analyzing the longitudinal blockage intensity or extraction pressure separately, it can be found that their respective influences are not completely independent. Therefore, in order to more accurately describe this relationship, a fitting analysis should be performed on the longitudinal blockage intensity and extraction negative pressure. The fitting results are shown in Figure 12.

The fitting function relationship is expressed as follows:

$$z=z_0-a_{1}x-a_2{x}^2-a_{3}xy+a_4{y}^2+a_5{x}^{2}y-a_{6}x{y}^2-a_7{y}^3$$

(13)

The parameters are given by: z₀ = 2.23, a₁ = 0.226, a₂ = 0.007, a₃ = 0.068, a₄ = 26.73, a₅ = 0.003, a₆ = 0.087, a₇ = 17.841, and R² = 0.987.

4.2. Parameter Characteristics of Water Blockage Acoustic Signal in Gas Extraction Pipelines

4.2.1. The Influence of Extraction Pressure on Mean Value of Sound Pressure Level in Characteristic Frequency Bands for Water Blockage

Under a transverse blockage intensity of 0.05 and a longitudinal blockage intensity of 0.07, in-depth analysis is conducted on the water blockage sound wave signal data under four different pressures of 12 kPa, 24 kPa, 36 kPa, and 48 kPa. To ensure the accuracy of the experimental data, each operating condition is measured twice to obtain the changes in the average value of characteristic frequency domain from the pipeline water blockage sound signals under different pressures.

As presented in Figure 13, it can be clearly observed that as the extraction negative pressure gradually increases, the average value of the characteristic frequency band also shows a significant growth trend. As a result, the energy value of sound signals is positively correlated with the degree of blockage. When the extraction pressure is 12 kPa, the average amplitude of characteristic frequency band 1 is 5.1 dB, and the average amplitude of characteristic frequency band 2 is 3.2 dB. When the extraction pressure rises to 24 kPa, the average amplitude of characteristic frequency band 1 rises to 6.2 dB, and the average amplitude of characteristic frequency band 2 rises to 4.3 dB. Compared to the 12 kPa, the average amplitude of characteristic frequency band 1 increased by 21.6%, and the average amplitude of characteristic frequency band 2 increased by 12.5%. As the pressure further strengthens, the average characteristic frequency band of the sound signals also continues rising. When the pressure reaches 36 kPa, the average amplitude of characteristic frequency band 1 reaches 7.8 dB, and the average amplitude of characteristic frequency band 2 rises to 5.6 dB. Compared with 12 kPa, the average amplitude of characteristic frequency band 1 increased by 52.9%, and the average amplitude of characteristic frequency band 2 increased by 37.5%. When the pressure reaches the maximum of 48 kPa, the average amplitude of characteristic frequency band 1 is 9.7 dB, and the average amplitude of characteristic frequency band 2 is 7.1 dB. Compared with 12 kPa, the average amplitude of feature frequency band 1 increased by a remarkable 90.2%, and the average amplitude of feature frequency band 2 increased by 65.6%. It can be concluded that there is a close relationship between the extraction negative pressure and average value of the characteristic frequency domain from pipeline water blockage sound signals.

4.2.2. The Influence of Transverse Blockage Intensity on Mean Value of Sound Pressure Level in Characteristic Frequency Bands for Water Blockage

Under the pressure of 12 kPa and longitudinal blockage intensity of 0.07, a detailed analysis is conducted on the acoustic signal data of water blockage at four different transverse blockage intensities of 0.05, 0.1, 0.15, and 0.2. To ensure the accuracy of the data, each operating condition is measured twice to obtain the variation law of the average value of characteristic frequency domain from the pipeline water blockage sound signals under different transverse blockage intensities.

As demonstrated in Figure 14, as the transverse blockage intensity of water blockage increases, the average amplitude of the characteristic frequency bands exhibits a growing trend, albeit with a relatively minor increase in amplitude. When the transverse blockage intensity is 0.05, the average amplitude of characteristic frequency band 1 is 5.1 dB, and the average amplitude of characteristic frequency band 2 is 3.2 dB. As the transverse blockage intensity gradually increases to 0.1, the average amplitude of characteristic frequency band 1 slightly rises to 5.2 dB, while the average amplitude of characteristic frequency band 2 remains at 3.2 dB. Compared to the transverse blockage intensity of 0.05, the average amplitude of characteristic frequency band 1 increased by 1.96%, while characteristic frequency band 2 remains unchanged. As the transverse blockage intensity further rises, the growth trend remains mild. For example, when the transverse blockage intensity reaches 0.15, the average amplitude of characteristic frequency band 1 increases to 5.3 dB, and the average amplitude of characteristic frequency band 2 rises to 3.3 dB. Compared to the initial transverse blockage intensity of 0.05, the average amplitude of characteristic frequency band 1 increased by 3.92%, and that of the characteristic frequency band 2 increased by 3.13%. It is worth noting that when the extraction negative pressure changes to −48 kPa, the average amplitude of characteristic frequency band 1 reaches 5.4 dB, and the average amplitude of characteristic frequency band 2 rises to 3.4 dB. Although the growth rate is still relatively slight, compared to the data at a transverse blockage intensity of 0.05, the average amplitude of characteristic frequency band 1 increased by 8.82% and characteristic frequency band 2 increased by 6.25%.

4.2.3. The Influence of Longitudinal Blockage Intensity on Mean Value of Sound Pressure Level in Characteristic Frequency Bands for Water Blockage

Under an extraction pressure of 12 kPa and a transverse blockage intensity of 0.05, in-depth analysis is conducted on the water blockage sound wave signal data at six different longitudinal blockage intensities of 0.07, 0.2, 0.34, 0.5, 0.66, and 0.8. As shown in Figure 15, it can be observed that with the increase in water blockage height, the average amplitude of the characteristic frequency band also presents a clear increasing trend. When the longitudinal blockage intensity is 0.07, the average amplitude of characteristic frequency band 1 is 5.1 dB, and the average amplitude of characteristic frequency band 2 is 3.2 dB. As the longitudinal blockage intensity gradually rises to 0.2, the average amplitude of characteristic frequency band 1 increases to 8.1 dB, and the average amplitude of characteristic frequency band 2 rises to 3.7 dB. Compared to the blockage intensity of 0.07, the average amplitude of characteristic frequency band 1 increased by 5.8%, and the average amplitude of characteristic frequency band 2 increased by 3.1%. With further increases in blockage intensity, the growth rate become more significant. For example, when the longitudinal blockage intensity reaches 0.34, the average amplitude of characteristic frequency band 1 jumps to 9.8 dB, and the average amplitude of characteristic frequency band 2 also reaches 4.3 dB. Compared to the blockage intensity of 0.07, it has increased by 7.8% and 9.4%, respectively. When the blockage intensity reaches 0.5, the average amplitudes of characteristic frequency band 1 and 2 rise to 12.6 dB and 5.1 dB, respectively, with further significant increases in amplitude. At higher blockage intensities, such as 0.66 and 0.8, the average amplitude of the characteristic frequency band maintains a significant growth. Especially at a longitudinal blockage intensity of 0.8, the average amplitude of characteristic frequency band 1 reaches 18.2 dB, and the average amplitude of characteristic frequency band 2 reaches 7.4 dB. Compared to the blockage intensity of 0.07, the average amplitudes of characteristic frequency band 1 and 2 have increased by approximately 13.7% and 12.5%, respectively.

It can be observed that as the longitudinal blockage intensity rises, the average amplitude in the characteristic frequency band also increases, and the increasing rate exhibits a noticeable enlargement. From the mechanism of sound waves, the accumulation of blockages inside the pipeline gives rise to the obstruction of fluid flow, which triggers irregular movement and pressure disturbance. When these disturbances propagate within the pipeline, the sound waves will occur. As the longitudinal blockage intensity rises, the fluid is more obstructed and the irregular movement and pressure disturbance become more intense, which stimulates higher amplitude sound waves. As the longitudinal blockage intensity increases, the propagation path of sound waves in the pipeline becomes more complex, and the reflection and refraction also increase. These effects will enhance the energy of sound waves, and accelerate the average amplitude of the characteristic frequency band of sound waves. It can be concluded that as the longitudinal and transverse blockage intensity varies, the average amplitude of the characteristic frequency band inside the pipeline gradually rises. Due to the occurrence of water blockage in the pipeline, which hinders fluid flow, reduces flow velocity, and increases pressure, thereby exacerbating the occurrence of water hammer. The intense sound wave is generated by the water hammer, which causes an increase in the amplitude of the acoustic signals. Additionally, during the process of pipeline water blockage, tiny bubbles may be generated by the turbulence and vortex. The expansion and collapse of these bubbles in the liquid can produce oscillating sound waves, namely bubble noise. As the longitudinal blockage intensity increases, the turbulence and vortex in the fluid intensify, which enhances the bubble noise. Furthermore, the enhancement of bubble noise also contributes to the overall rise in the amplitude of the acoustic signals.

An in-depth analysis of the characteristics of the sound signals within the pipeline is conducted under different longitudinal and transverse blockage intensities and extraction pressures; it can be observed that there is a significant functional relationship between the extraction pressure, longitudinal blockage intensity, and the average amplitude of the characteristic frequency band. When the extraction negative pressure or longitudinal blockage intensity varies, the average amplitude of the characteristic frequency band will also change accordingly. The transverse blockage intensity has a relatively minor impact on the average amplitude of the sound source signal in the characteristic frequency band. Therefore, in the subsequent analysis, the transverse blockage intensity will be temporarily ignored and mainly focus on the influence of the longitudinal blockage intensity and extraction pressure on the average sound pressure level of the characteristic frequency band. Nevertheless, when analyzing the longitudinal blockage intensity or extraction pressure separately, it can be found that their respective influences are not entirely independent. Thereby, in order to more accurately describe this relationship, a fitting analysis should to be conducted on the longitudinal blockage intensity and extraction negative pressure. The fitting results are presented in Figure 16.

The fitting function relationship is expressed as follows:

$$z=z_0-a_{1}x-a_{2}y+a_3{x}^2-a_{4}xy+a_5{y}^2+a_{6}x{y}^2-a_7{y}^3$$

(14)

The parameters are given by: z₀ = 1.791, a₁ = 0.033, a₂ = 39.4, a₃ = 0.0051, a₄ = −0.42, a₅ = −71.52, a₆ = 0.649, a₇ = 61.91, R² = 0.984, indicating a better fit.

This model is based on six levels of longitudinal blockage intensity and four levels of extraction negative pressure. In practical applications, the levels of blockage and extraction pressure could be expanded to refine the fitting model.

Compared to traditional methods such as the pressure wave technique and the time-frequency analysis of acoustic waves for pipeline blockage detection, the acoustic wave detection method proposed in this study offers significant advantages in terms of detection speed and non-destructive testing. By analyzing the acoustic wave spectrum, this method can quickly identify pipeline blockages and is particularly suitable for complex, multi-branch pipelines used in coal mine gas extraction, unaffected by the shape of the pipeline. Moreover, the required sensors and analytical equipment are simple, with low maintenance requirements and relatively low cost.

More importantly, this acoustic detection method employs dual-channel acoustic signal acquisition technology, allowing for the simultaneous and precise collection of both the internal pipeline acoustic signals and external environmental noise. This approach makes the detection process more comprehensive and detailed, effectively filtering out environmental noise in various conditions, thereby significantly improving the accuracy and reliability of the detection.

5. Conclusions

To address the gas extraction pipeline blockages, in this paper, a detection technology is proposed for slag blockage and water blockage based on the acoustic signal, and in-depth experiments are conducted to analyze the acoustic signal characteristics under different blockage intensities and extraction negative pressures in curved pipelines. By analyzing the characteristics of the acoustic signal under different conditions, the following conclusions can be drawn:

(1)

Characteristic frequency ranges exist for both slag and water blockages. Separately, for the slag blockage, there is a prominent peak in the sound pressure level within the frequency range of 2 kHz to 3 kHz. For the water blockage, there are two peak points of sound pressure level in the frequency domain, located in the frequency range of 1 kHz–2 kHz and 3.5 kHz–4.5 kHz, respectively.

(2)

The transverse blockage intensity has a minor impact on the sound pressure level when the slag blockage and water blockage occur in the extraction pipeline. However, the extraction negative pressure and longitudinal blockage intensity have a significant influence on the average sound pressure amplitude within the characteristic frequency range. Under a transverse blockage intensity of 0.05 and the longitudinal blockage intensity of 0.07, as the extraction pressure increases from 12 kPa to 48 kPa, the average amplitude of the characteristic frequency band rises from 3.8 dB to 7.7 dB, with an increase of 102.6%. With an extraction pressure of −12 kPa and a transverse blockage intensity of 0.05, as the longitudinal blockage intensity rises from 0.07 to 0.2, the average amplitude of the characteristic frequency band has increased by 223.8%. However, when the transverse blockage intensity rises from 0.05 to 0.2, the average amplitude of the characteristic frequency band has only increased by 13.7%. Consequently, the impact of the longitudinal blockage intensity on the characteristic frequency band is much greater than that of transverse blockage intensity. In the process of acoustic detection, the influence of longitudinal blockage intensity on the amplitude of characteristic frequency band should be mainly analyzed. The origin is employed to fit the relationship between the slag or water blockage and the average amplitude of the characteristic frequency band under the longitudinal blockage intensity and the extraction negative pressure. As the fitting degrees reach 0.987 and 0.984, a high fitting degree is exhibited and can be considered as a reliable basis for assessing the longitudinal blockage intensity of pipelines.

Although in this paper, the acoustic characteristics of pipeline blockage are analyzed in detail, the blockage was simplified during the experiment and the effect of the shape of the blockage on the acoustic characteristics was not considered. Future research should focus on the effect of different blockage shapes on the acoustic characteristics and improve the acoustic detection method.