A pipe wall sound wave collaborative hole collapse dynamic monitoring system based on inverse piezoelectric effect

Abstract

The present application belongs to the technical field of intelligent monitoring in coal mine, and particularly relates to a pipe wall acoustic wave collaborative hole collapse dynamic monitoring system based on inverse piezoelectric effect. The system comprises a piezoelectric excitation module, an acoustic wave sensor array, a data processing unit and a wireless transmission module. The piezoelectric excitation module generates mechanical vibration under the action of an applied electric field by using the inverse piezoelectric effect, and generates acoustic wave signals to propagate along the pipe wall of the drill hole; the acoustic wave sensor array is distributed on the pipe wall, and is used for receiving reflected signals and collaboratively identifying hole collapse characteristics; the data processing unit extracts and distinguishes features of the acoustic wave signals based on frequency domain analysis and machine learning algorithms; and the wireless transmission module is used for transmitting monitoring data to the ground control center in real time, so as to realize early warning and remote monitoring. The present application realizes dynamic monitoring of hole collapse, has the advantages of self-powered, long service life, high precision and high adaptability, and is suitable for real-time monitoring of deep wells in coal mines.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety monitoring and intelligent sensing technology, and specifically relates to a dynamic monitoring system for pipe wall acoustic wave-coordinated hole collapse based on the inverse piezoelectric effect. Background Technology

[0002] In coal mine gas extraction and geological drilling, borehole collapse is a significant hidden danger affecting borehole stability and construction safety. Existing borehole collapse monitoring methods mainly include: (1) Pressure / displacement sensing detection method requires a large number of wired sensors, which is complicated to install and consumes a lot of energy; (2) The passive sound wave monitoring method relies on environmental sound sources, resulting in large signal attenuation and severe noise interference, making it impossible to achieve active and high-sensitivity detection.

[0003] The above methods all suffer from problems such as poor real-time monitoring, low recognition accuracy, and limited applicability.

[0004] Therefore, there is an urgent need for a dynamic monitoring system for borehole collapse that can achieve active acoustic excitation, multi-node collaborative sensing, and real-time early warning, in order to improve the accuracy and reliability of borehole monitoring in coal mines. Summary of the Invention

[0005] I. Purpose of the Invention The purpose of this invention is to overcome the problems of high energy consumption, large signal attenuation, low monitoring accuracy, and difficult maintenance in existing coal mine collapse monitoring technologies, and to provide a pipe wall acoustic wave-based dynamic monitoring system for collapse based on the inverse piezoelectric effect.

[0006] This system utilizes the inverse piezoelectric effect to actively excite acoustic signals, and achieves dynamic monitoring of borehole collapse through coordinated transmission with the pipe wall. It also has data analysis, early warning, and remote monitoring functions.

[0007] II. Technical Solution (1) System composition A dynamic monitoring system for pipe wall acoustic wave-assisted pore collapse based on the inverse piezoelectric effect, comprising: Piezoelectric excitation module: Made of piezoelectric material (PZT ceramic or PVDF film), it generates mechanical vibration under the action of an external electric field (100~500V / mm) based on the inverse piezoelectric effect, and generates an acoustic wave signal in the range of 1~10kHz that is injected into the borehole wall. Acoustic sensor array: Distributed on the inner wall of the borehole casing, with one node arranged every 10 to 50 m, to receive reflected acoustic signals and realize collaborative detection of the casing wall; the sensor adopts piezoelectric composite material and supports multi-node data fusion. Data processing unit: includes signal amplifier, A / D converter and embedded processor, used to analyze sound wave reflection characteristics; algorithms include FFT spectrum analysis and machine learning threshold discrimination to identify the location and degree of hole collapse; Wireless transmission module: It adopts Bluetooth Low Energy or ZigBee protocol to transmit monitoring data to the ground control center to realize real-time early warning and remote monitoring.

[0008] (2) Monitoring methods A method for dynamic monitoring of hole collapse based on the inverse piezoelectric effect includes the following steps: Acoustic excitation: The collected electrical energy is used to excite the piezoelectric module to generate periodic acoustic pulses (pulse width 0.1 to 1 ms) based on the inverse piezoelectric effect. Signal propagation: Sound waves propagate along the pipe wall and are reflected or attenuated at the collapsed hole; Signal reception and analysis: The sensor array captures the reflected signal, and the data processing unit extracts characteristic parameters (such as a 10% to 20% increase in the reflection wave delay) to achieve hole collapse detection; Intelligent identification and early warning: If an anomaly is detected, the system triggers an early warning and simultaneously adjusts the excitation frequency and energy output adaptively through algorithms to achieve the best detection effect.

[0009] III. Beneficial Effects Compared with the prior art, the present invention has the following significant advantages: Active acoustic excitation: Active sound source generation is achieved based on the inverse piezoelectric effect, resulting in stable signal and strong penetration; Pipe wall collaborative monitoring: Multi-node sensor array enables collaborative acoustic wave transmission, with a long signal coverage distance; Intelligent recognition and adaptive optimization: Based on a joint discrimination model of frequency domain analysis and machine learning algorithms, the threshold can be automatically optimized to reduce the false alarm rate; Strong real-time early warning capability: The system achieves real-time data uploading and remote early warning through a wireless communication module, with a fast response speed; High compatibility and reliability: The system has a modular design and can be directly embedded into the borehole wall protection pipe structure in coal mines, which facilitates maintenance and application expansion. Attached Figure Description

[0010] Figure 1 A schematic diagram illustrating an application scenario in underground coal mines; Figure 2 This is a schematic diagram of the piezoelectric excitation module installation. Figure 3 A schematic diagram of the installation of an acoustic wave sensor array; Figure 4 This is a flowchart of the signal processing for acoustic hole collapse monitoring of pipe walls based on the inverse piezoelectric effect; Explanation of icon numbers: 1—Drill hole; 2—Extraction pipe; 3—Side support anchor bolt; 4—Piezoelectric excitation module; 5—Acoustic sensor array; 6—Data processing unit. Detailed Implementation

[0011] Example 1: System Assembly and Installation PZT-5H piezoelectric ceramic was selected as the excitation material, and it was processed into a ring structure (50 mm in diameter and 2 mm in thickness) and embedded in the inner wall of a drilled protective tube (100 mm in diameter and 500 m in length).

[0012] The acoustic sensor array is made of PVDF film, with a node arranged every 50m.

[0013] The data processing unit uses an STM32 microcontroller and integrates an FFT algorithm for spectrum analysis; the wireless module uses the ZigBee protocol to achieve encrypted data transmission.

[0014] After installation, the system automatically generates 1kHz sound wave pulses and performs a scan every 5 minutes to achieve periodic dynamic monitoring.

[0015] Example 2: Hole Collapse Monitoring Experiment During underground drilling and extraction or geological exploration in coal mines, the system generates sound waves through a piezoelectric excitation module, which then propagate along the pipe wall.

[0016] When local collapse or hole wall detachment occurs around the borehole, the acoustic wave reflection characteristics change significantly. The system can capture this change in real time and identify it through the data processing unit.

[0017] The monitoring results can be transmitted to the ground monitoring center via a wireless transmission module, enabling early warning of borehole collapse and visual display of its status.

[0018] The system can operate stably for a long time in complex downhole environments and features low power consumption, self-powered operation, anti-interference capabilities, and high reliability.

Claims

1. A dynamic monitoring system for pipe wall acoustic wave-coordinated collapse based on the inverse piezoelectric effect, characterized in that, It includes a piezoelectric excitation module (4), an acoustic sensor array (5), a data processing unit (6), and a wireless transmission module; the piezoelectric excitation module (4) generates an acoustic signal based on the inverse piezoelectric effect and propagates along the borehole wall to realize active excitation and response monitoring of the borehole collapse state.

2. The system according to claim 1, characterized in that, The piezoelectric excitation module (4) is made of PZT ceramic or PVDF thin film material, with an applied electric field strength of 100-500 V / mm and a vibration frequency of 1-10 kHz.

3. The system according to claim 1, characterized in that, The acoustic sensor array (5) is arranged axially along the borehole wall with a node spacing of 10 to 100 m, supporting multi-node collaborative signal processing and data fusion.

4. The system according to claim 1, characterized in that, The data processing unit (6) adopts an embedded processor structure, has the function of joint analysis in time domain and frequency domain, and dynamically optimizes the threshold for identifying collapsed hole features based on machine learning algorithms.

5. The system according to any one of claims 1 to 4, characterized in that, It is suitable for monitoring underground boreholes in coal mines with a depth of not less than 500 m, and has real-time early warning and remote data transmission functions.

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