Pipeline leakage diagnosis method based on sound signal and vibration signal fusion network

Abstract

The present application relates to the technical field of pipeline leakage, and more particularly to a pipeline leakage diagnosis method based on a sound signal and vibration signal fusion network, comprising a fusion classifier comprising a sound emission classifier, a vibration classifier and a combined classifier; the feature extraction network of the sound emission classifier and the vibration classifier is a densely connected layer with a stacking structure, a first activation layer and a regularization layer; the feature extraction network of the combined classifier is a compression attention mechanism module and a densely connected layer with a stacking structure, a first activation layer; the classification network of the combined classifier is a densely connected layer and a second activation layer; the sound signal feature vector, the vibration feature vector and the knock angle value are used as the input of the combined classifier; the feature extraction network of the combined classifier is connected in residual with the densely connected layer of its classification network. The present application solves the problem that the accuracy of pipeline leakage judgment of a single signal in the prior art needs to be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline leakage, and in particular to a pipeline leakage diagnosis method based on an acoustic signal and vibration signal fusion network. Background Art

[0002] Given the potential dangers of pipeline gas leakage, timely and accurate diagnosis is particularly important; through effective diagnostic methods, gas leakage points can be discovered in time and appropriate remedial measures can be taken to avoid the occurrence of safety accidents.

[0003] Patent application number 202510021350.4 is a gas pipeline leakage diagnosis method based on imbalance data. It only uses acoustic emission signals. Although acoustic emission signals can capture high-frequency transient events, they do not pay enough attention to low-frequency mechanical vibrations that can reflect structural abnormalities, looseness and other factors. Vibration signals can reflect the overall operating status of the mechanical structure at low frequencies, but due to their weak characteristics, they are difficult to identify and have low accuracy at different leakage severities during gas pipeline leakage. Summary of the Invention

[0004] In response to the shortcomings of existing methods, the present invention constructs a fusion classifier composed of an acoustic emission classifier, a vibration classifier and a combined classifier, and fuses the acoustic signal feature vector, the vibration feature vector and the knocking angle to improve the accuracy of pipeline leakage classification.

[0005] The technical solution adopted by the present invention is: a pipeline leakage diagnosis method based on an acoustic signal and vibration signal fusion network includes the following steps:

[0006] Step 1: Collect acoustic emission signals and vibration signals at different knocking angles to construct a pipeline leakage status dataset;

[0007] As a preferred embodiment of the present invention, different leakage states are simulated by adjusting the horizontal angle between the hammer and the pipeline.

[0008] As a preferred embodiment of the present invention, the leakage status includes: healthy, light leakage, moderate leakage and heavy leakage.

[0009] Step 2: Preprocess the pipeline leakage status dataset;

[0010] As a preferred embodiment of the present invention, the preprocessing includes: calculating the mean value, the standard deviation value, normalization and fast Fourier transform.

[0011] Step 3. Construct a fusion classifier including an acoustic emission classifier, a vibration classifier and a combined classifier; the feature extraction networks of the acoustic emission classifier and the vibration classifier are a densely connected layer, a first activation layer and a regularization layer of a stacked structure; the feature extraction network of the combined classifier is a cascade of a compressed attention mechanism module and a densely connected layer and a first activation layer of a stacked structure; the classification network of the combined classifier is a cascade of a densely connected layer and a second activation layer; the acoustic signal feature vector, the vibration feature vector and the knocking angle value are used as inputs of the combined classifier; the feature extraction network of the combined classifier is residually connected to the densely connected layer of its classification network.

[0012] As a preferred embodiment of the present invention, the stacking structure of the feature extraction network of the acoustic emission classifier is three layers.

[0013] As a preferred embodiment of the present invention, the stacking structure of the feature extraction network of the vibration classifier is 2 layers.

[0014] As a preferred embodiment of the present invention, the stacking structure of the feature extraction network of the combined classifier is 2 layers.

[0015] As a preferred embodiment of the present invention, the first activation layer is a ReakyRelu activation function, and the second activation layer is a SoftMax activation function.

[0016] As a preferred embodiment of the present invention, a pipeline leakage diagnosis system based on a fusion network of acoustic signals and vibration signals includes: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement a pipeline leakage diagnosis method based on a fusion network of acoustic signals and vibration signals.

[0017] As a preferred embodiment of the present invention, a computer readable medium stores computer program code, and when the computer program code is executed by a processor, a pipeline leakage diagnosis method based on a fusion network of acoustic signals and vibration signals is implemented.

[0018] Beneficial effects of the present invention:

[0019] 1. Build a fusion classifier to fuse the acoustic signal feature vector, vibration feature vector, and tapping angle. Use the compressed attention mechanism module to capture the correlation between the three feature vectors. Perform residual design on the densely connected layer of the combined classifier to enable efficient training and rapid convergence of the fusion classifier, thereby improving generalization capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural diagram of the fusion classifier of the present invention;

[0021] Figure 2 Schematic diagram of the striking angle of the striking hammer of the present invention;

[0022] Figure 3 It is a schematic diagram of the sensor and the hammer of the present invention;

[0023] Figure 4 is a confusion matrix diagram of the present invention;

[0024] Figure 5 This is a comparison diagram of the individual model, fusion model and existing support vector machine of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore only shows the components related to the present invention.

[0026] like Figure 1 As shown, the pipeline leakage diagnosis method based on the acoustic signal and vibration signal fusion network includes the following steps:

[0027] Step 1: Perform a tapping test on the gas pipeline surface to simulate operating conditions; collect acoustic emission signals and vibration signals at different tapping angles to construct a data set;

[0028] Use a percussion hammer to perform a percussion test on a designated location on the gas pipeline surface, controlling the horizontal angle between the percussion hammer and the gas pipeline surface to simulate the percussion intensity;

[0029] like Figure 2 、 3 , a percussion test is carried out on the gas pipeline to simulate different operating conditions; the leakage status is divided into four types: healthy, light leakage, moderate leakage and severe leakage; when the percussion hammer is placed on the surface of the gas pipeline with a horizontal angle of 0°, the pipeline is in a healthy state; when the angle is 30° and a percussion is applied, a light leakage is simulated; when the angle increases to 60°, it corresponds to a moderate leakage; if the angle reaches 90°, it represents a severe leakage state.

[0030] A combined system using the low-frequency vibration signal of an accelerometer and the high-frequency acoustic emission signal of an ultrasonic transducer, that is, using an acceleration sensor to measure the vibration signal of the equipment and using an acoustic emission sensor to measure the acoustic emission signal of the equipment. The type and number of sensors may not be limited to these two.

[0031] Step 2: Preprocess the data set;

[0032] Preprocess the data set by calculating the mean and standard deviation of each vibration or acoustic emission sample and then standardizing it.

[0033] Perform a fast Fourier transform on each standardized sample; that is, append the mean and standard deviation values ​​to the fast Fourier transformed sample to create a feature vector for each sample;

[0034] Based on the range parameters of the training dataset in stage 1, all datasets are additionally scaled to a feature range between 0 and 1; where x is the mean, x~ is the standard deviation, N is the number of sample points, and x i is the i-th sample value; each sample value x i Normalized to z i ; f is the frequency, the formula is as follows:

[0035]

[0036] After preprocessing, the feature vector of each acoustic emission segment finally obtained contains 1014 values, while the feature vector of each vibration segment contains 2045 values;

[0037] Step 3: Construct a fusion classifier including an acoustic emission classifier, a vibration classifier and a combined classifier;

[0038] The feature extraction network of the acoustic emission classifier is a stacked structure of densely connected layers, first activation layers, and regularization layers; the stacked structure is three layers;

[0039] The classification network of the acoustic emission classifier includes: a densely connected layer and a second activation layer;

[0040] The feature extraction network of the vibration classifier is a stacked structure of densely connected layers, first activation layers, and regularization layers; the stacked structure is two layers;

[0041] The classification network structure of the vibration classifier is the same as that of the acoustic emission classifier, which is a cascade of dense connection layers and the second activation layer.

[0042] The feature extraction network of the combined classifier is a cascade of a compressed attention mechanism module, a densely connected layer of a stacked structure, and the first activation layer, with a two-layer stacked structure;

[0043] The classification network of the combined classifier is a dense connection layer and a second activation layer, and the dense connection layer of the classification network of the combined classifier is residually connected to the first dense connection layer of the feature extraction network of the combined classifier;

[0044] By introducing skip paths, residual connections enable the model to be trained efficiently, converge quickly, and improve generalization capabilities; they also make up for the defects of densely connected layers such as large number of parameters and difficulty in optimization.

[0045] The Condensed Attention Neural Block (CAB) consists of two parts: channel attention and spatial attention. Channel attention first extracts information from different features, then renormalizes and calculates weights, multiplies them by different weights, and outputs them through 1x1 convolution. Spatial attention captures the correlation between input information from different modalities in the spatial dimension.

[0046] The first activation layer is the ReakyRelu activation function, and the second activation layer is the SoftMax activation function.

[0047] It also includes: a feature extraction network of the vibration classifier and a classification network of the vibration classifier to predict the leakage state.

[0048] It also includes: a feature extraction network of the acoustic emission classifier and a classification network of the acoustic emission classifier to predict the leakage state.

[0049] Experimental results:

[0050] The confusion matrix is ​​a table layout that supports the visualization of model classification performance and can intuitively express the target value and predicted value. This embodiment evaluates and analyzes the gas pipeline diagnosis results. The confusion matrix is ​​shown in the attached figure. Figure 4 , a single vibration classifier can accurately classify the healthy working condition and mild leakage of gas pipelines, with a diagnostic accuracy of 100%; for moderate leakage, the correct diagnosis rate is 16.33%, and the misdiagnosis rates in mild leakage and severe leakage are 60.33% and 23.14% respectively; for severe leakage, the correct diagnosis rate is 92.50%, and the misdiagnosis rates in healthy working condition, mild leakage and severe leakage are 0.83%, 4.17% and 2.50% respectively.

[0051] For healthy operating conditions, the correct diagnosis rate of a single acoustic emission classifier was 99.48%, and the misdiagnosis rates for mild leaks, moderate leaks, and severe leaks were 0.32%, 0.06%, and 0.13%, respectively. For mild leaks, the correct diagnosis rate was 89.34%, and the misdiagnosis rates for healthy operating conditions, moderate leaks, and severe leaks were 0.73%, 8.84%, and 1.08%, respectively. For moderate leaks, the correct diagnosis rate was 41.61%, and the misdiagnosis rates for healthy operating conditions, mild leaks, and severe leaks were 7.90%, 46.61%, and 3.89%, respectively. For severe leaks, the correct diagnosis rate was 96.09%, and the misdiagnosis rates for healthy operating conditions, mild leaks, and moderate leaks were 1.47%, 1.78%, and 0.66%, respectively.

[0052] The fusion classifier composed of vibration classifier, acoustic emission classifier and combined classifier can accurately classify the healthy working condition and severe leakage of gas pipelines. For mild leakage, the correct diagnosis rate is 90.62%, and the misdiagnosis rate for moderate leakage is 9.38%. For moderate leakage, the correct diagnosis rate is 56.25%, and the misdiagnosis rates for healthy working condition, mild leakage and severe leakage are 3.12%, 34.38% and 6.25% respectively.

[0053] like Figure 5The individual models and fusion models of the present invention are compared with the existing support vector machine (SVM), XGBoost (Extreme Gradient Boosting), and 1D CNN (1D Convolutional Neural Network). It can be seen that the fusion model of the present invention has the best effect when considering the overall accuracy, detection speed, and model size.

[0054] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A pipeline leakage diagnosis method based on a fusion network of acoustic and vibration signals, characterized in that: The following steps are involved: Step 1: Collect acoustic emission signals and vibration signals at different knocking angles to construct a pipeline leakage status dataset; By adjusting the angle between the hammer and the pipe, different leakage conditions can be simulated; Leakage status includes: healthy, minor leakage, moderate leakage and major leakage; Step 2: Preprocess the pipeline leakage status dataset; Preprocessing includes: calculating the mean, standard deviation, normalization and fast Fourier transform; Step 3, constructing a fusion classifier including an acoustic emission classifier, a vibration classifier and a combined classifier; the feature extraction networks of the acoustic emission classifier and the vibration classifier are a dense connection layer, a first activation layer and a regularization layer of a stacked structure; the feature extraction network of the combined classifier is a cascade of a compressed attention mechanism module and a dense connection layer and a first activation layer of a stacked structure, and the classification network of the combined classifier is a cascade of a dense connection layer and a second activation layer; the acoustic signal feature vector, the vibration feature vector and the knocking angle value are used as inputs of the combined classifier; the feature extraction network of the combined classifier is residually connected with the dense connection layer of its classification network; The stacking structure of the feature extraction network of the acoustic emission classifier is 3 layers; The stacked structure of the feature extraction network of the vibration classifier is 2 layers; The stacking structure of the feature extraction network of the combined classifier is 2 layers; The first activation layer is the ReakyRelu activation function, and the second activation layer is the SoftMax activation function.

2. A pipeline leakage diagnosis system based on a fusion network of acoustic and vibration signals, characterized in that: include: a memory for storing instructions executable by the processor; A processor is used to execute instructions to implement the pipeline leakage diagnosis method based on the acoustic signal and vibration signal fusion network as claimed in claim 1.

3. A computer-readable medium storing computer program code, characterized in that When the computer program code is executed by a processor, the pipeline leakage diagnosis method based on the acoustic signal and vibration signal fusion network according to claim 1 is implemented.

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