Ore pulp pipeline elbow leakage risk monitoring device and monitoring method

Abstract

The invention discloses a leakage risk monitoring device and method for an elbow of an ore pulp pipeline, and the device is used for obtaining a sound wave signal generated at a target elbow through the arrangement of a resonant cavity and ultrasonic probe monitoring combination, and inputting the obtained sound wave signal into a leakage risk model. The leakage risk at the elbow of the ore pulp pipeline can be accurately monitored in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline leakage monitoring, and in particular to a leakage risk monitoring device and a monitoring method for a slurry pipeline bend. Background Art

[0002] In mining production, slurry pipelines are key equipment for material transportation, and their operating status is directly related to production efficiency and safety. Due to the characteristics of slurry such as strong abrasiveness, fast flow speed, and complex composition, pipelines are prone to wear, corrosion, and loose connection parts after long-term operation, which in turn increases the risk of leakage. Among them, the bends of slurry pipelines are the weakest link in wear resistance. Traditional leakage detection methods mainly include manual inspections and pressure tests, but they have disadvantages such as low efficiency, slow response, and difficulty in real-time monitoring. In particular, the monitoring of erosion and wear of slurry on the bends of slurry pipelines is weak. Therefore, it is particularly important to develop a device that can monitor the leakage risk of slurry pipeline bends in real time and accurately. Summary of the invention

[0003] In view of the above problems, the present invention proposes a leakage risk monitoring device and a monitoring method for a slurry pipeline bend, which mainly solve the problems of the background technology.

[0004] In order to solve the above technical problems, the first aspect of the present invention provides a leakage risk monitoring device for a slurry pipeline bend, comprising:

[0005] The resonant cavity is used to be installed on the outside of the target elbow and fits tightly with the target elbow;

[0006] An ultrasonic probe, installed in the resonance cavity, for acquiring the sound wave information generated at the target bend;

[0007] The data transmission module is used to transmit the sound wave information to the data analysis system.

[0008] A second aspect of the present invention provides a method for monitoring leakage risk at a bend in a slurry pipeline, which is used in the above-mentioned device and comprises the following steps:

[0009] S1, establish leakage risk model based on sample data;

[0010] S2, preprocessing the sound wave information to obtain a preprocessing signal;

[0011] S3, analyzing the characteristic waveform to obtain an analysis result. When the analysis result shows that there is a leakage risk, an early warning mechanism is automatically triggered and the leakage warning is uploaded to the remote information platform.

[0012] In some embodiments, the process of establishing the leakage risk model includes:

[0013] S101, determining the hierarchy and number of nodes of the network structure according to the characteristics of the sample data;

[0014] S102, setting an activation function;

[0015] S103, initializing weights and biases;

[0016] S104, build network model

[0017] S105, dividing part of the sample data into a training set, and defining a loss function for training the network.

[0018] In some embodiments, the learning process of the leakage risk model includes: the characteristic waveform is marked and used to optimize the loss function.

[0019] In some embodiments, the preprocessing includes: using a preset filter to reduce noise on the sound wave information, and performing data transformation on the noise-reduced sound wave information to form discrete data.

[0020] In some implementations, the setting of the characteristic waveform includes: presetting a leakage condition, and acquiring a waveform under the leakage condition as the characteristic waveform.

[0021] In some embodiments, the method for setting the size of the resonance cavity includes: obtaining the frequency of the sound wave generated under the characteristic waveform, and determining the specific size of the resonance cavity according to the sound wave frequency and a preset amplification factor.

[0022] The beneficial effects of the present invention are as follows: by setting up a group of resonance cavities and ultrasonic probe monitoring combinations to obtain the acoustic wave signals generated at the target bend, and inputting the acquired acoustic wave signals into the leakage risk model, the leakage risk at the bend of the slurry pipeline can be monitored in real time and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic structural diagram of a leakage risk monitoring device at a bend of a slurry pipeline disclosed in Embodiment 1 of the present invention;

[0024] Figure 2 It is a schematic diagram of the structure of the resonant cavity disclosed in the first embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the content of the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It is understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all the contents.

[0026] Embodiment 1

[0027] This embodiment proposes a leakage risk monitoring device for a slurry pipeline bend. Figure 1 , 2 As shown, including:

[0028] The resonance cavity 1 is used to be installed on the outside of the target curved pipe 2. A connector 6 is set at the bottom of the target curved pipe 2 so that the bottom of the resonance cavity 1 fits tightly with the surface of the target curved pipe 2. Both ends of the target curved pipe 2 are connected to the straight pipe via flanges 3. The resonance cavity 1 is a closed container for receiving sound waves. It can resonate with sound waves of a specific frequency according to the dimensional parameters such as the length, width and height of the cavity, thereby enhancing or changing the characteristics of the sound. Specifically, a mounting position 5 is set inside the resonance cavity 1 to accommodate an ultrasonic probe 4. The resonant frequency of the resonance cavity 1 depends on its size and shape. When the wavelength of the sound wave matches the size of the resonance cavity, the sound wave will form a standing wave in the resonance cavity, thereby enhancing the sound. Therefore, when designing a resonance cavity, it is necessary to determine the specific size of the cavity according to the frequency of the sound wave to be enhanced.

[0029] The ultrasonic probe 4 is installed in the resonance cavity 1, and the sound is received through the resonance center of the resonance cavity to obtain the sound wave information generated at the target elbow 2. Preferably, the ultrasonic probe 4 uses an acoustic wave sensor array, which is composed of a plurality of high-precision acoustic wave sensors, evenly distributed on the outer wall of the slurry pipeline, to capture the leakage acoustic wave signals that may be generated inside the pipeline.

[0030] The data transmission module is used to transmit the acoustic wave information to the data analysis system. Specifically, the data transmission module is arranged near the resonance cavity 1, and is responsible for receiving the original acoustic wave signal from the ultrasonic probe 4 and transmitting it to the remote data analysis system. After the data analysis system performs pre-processing such as amplification and filtering, the high-performance computing unit performs complex signal processing and analysis to extract the characteristic waveform of the leakage.

[0031] Embodiment 2

[0032] This embodiment proposes a leakage risk monitoring method for a slurry pipeline bend, which is used in the device described in the first embodiment and includes the following steps:

[0033] S1, establish a leakage risk model based on sample data.

[0034] The process of establishing the leakage risk model includes:

[0035] S101, determining the hierarchy and number of nodes of the network structure according to the characteristics of the sample data;

[0036] S102, setting an activation function;

[0037] S103, initializing weights and biases;

[0038] S104, build network model

[0039] S105, dividing part of the sample data into training sets and defining a loss function for training the network.

[0040] The learning process of the leakage risk model includes: the characteristic waveform is marked and used to optimize the loss function.

[0041] S2, preprocessing the sound wave information to obtain a preprocessed signal.

[0042] The preprocessing includes: using a preset filter to reduce the noise of the sound wave information, and performing data transformation on the noise-reduced sound wave information to form discrete data.

[0043] S3, analyzes the characteristic waveform to obtain the analysis result. When the analysis result shows that there is a leakage risk, the early warning mechanism is automatically triggered and the leakage warning is uploaded to the remote information platform. The relevant personnel are notified in time to take corresponding measures through sound and light alarms, SMS notifications, etc.

[0044] In this embodiment, the setting of the characteristic waveform includes: presetting a leakage condition, and obtaining a waveform under the leakage condition as the characteristic waveform.

[0045] Specifically, the method for setting the size of the resonance cavity includes: obtaining the frequency of the sound wave generated under the characteristic waveform, and determining the specific size of the resonance cavity according to the sound wave frequency and a preset amplification factor.

[0046] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A leakage risk monitoring device for a slurry pipeline bend, characterized in that: include: The resonant cavity is used to be installed on the outside of the target elbow and fits tightly with the target elbow; An ultrasonic probe, installed in the resonance cavity, for acquiring the sound wave information generated at the target bend; The data transmission module is used to transmit the sound wave information to the data analysis system.

2. A method for monitoring leakage risk at a bend in a slurry pipeline, used in the device of claim 1, characterized in that: The following steps are involved: S1, establish leakage risk model based on sample data; S2, preprocessing the sound wave information to obtain a preprocessing signal; S3, analyzing the characteristic waveform to obtain an analysis result. When the analysis result shows that there is a leakage risk, an early warning mechanism is automatically triggered and the leakage warning is uploaded to the remote information platform.

3. The method for monitoring leakage risk at a bend in a slurry pipeline according to claim 2, wherein the process of establishing the leakage risk model comprises: S101, determining the hierarchy and number of nodes of the network structure according to the characteristics of the sample data; S102, setting an activation function; S103, initializing weights and biases; S104, building a network model; S105, dividing part of the sample data into a training set, and defining a loss function for training the network.

4. The method for monitoring leakage risk at a bend in a slurry pipeline according to claim 3, wherein the learning process of the leakage risk model comprises: The characteristic waveform is marked and used to optimize the loss function.

5. The method for monitoring leakage risk at a bend in a slurry pipeline according to claim 2, wherein the pretreatment comprises: A preset filter is used to reduce noise on the sound wave information, and the sound wave information after noise reduction is transformed into discrete data.

6. The method for monitoring leakage risk at a bend in a slurry pipeline according to claim 2, wherein the setting of the characteristic waveform comprises: A leakage condition is preset, and a waveform under the leakage condition is obtained as the characteristic waveform.

7. The method for monitoring leakage risk at a bend in a slurry pipeline according to claim 6, wherein the method for setting the size of the resonance cavity comprises: The frequency of the sound wave generated under the characteristic waveform is obtained, and the specific size of the resonance cavity is determined according to the sound wave frequency and a preset amplification factor.

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